Taotlus

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Study Protocol P3-C3-005

DARWIN EU® – Incidence rates of venous

thromboembolic events in cancer

patients

30/10/2024

Version 1.0

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Table of Contents

LIST OF ABBREVIATIONS ......................................................................................................................... 4

1. TITLE .............................................................................................................................................. 5

2. RESPONSIBLE PARTIES – STUDY TEAM ............................................................................................. 5

3. ABSTRACT ...................................................................................................................................... 7

4. AMENDMENTS AND UPDATES ......................................................................................................... 8

5. MILESTONES ................................................................................................................................... 9

6. RATIONALE AND BACKGROUND ...................................................................................................... 9

7. RESEARCH QUESTION AND OBJECTIVES ......................................................................................... 10

8. RESEARCH METHODS .................................................................................................................... 13 8.1 Study type and study design .......................................................................................................... 13 8.2 Study setting and data sources ...................................................................................................... 14 8.3 Study period ................................................................................................................................... 21 8.4 Follow-up ....................................................................................................................................... 21 8.5 Study population with inclusion and exclusion criteria ................................................................. 22 8.6 Variables ......................................................................................................................................... 25 8.7 Study size........................................................................................................................................ 28 8.8 Analysis ........................................................................................................................................... 28 8.9 Evidence synthesis ......................................................................................................................... 30

9. DATA MANAGEMENT ................................................................................................................... 30 9.1 Data management ................................................................................................................................. 30 9.2 Data storage and protection ................................................................................................................. 30

10. QUALITY CONTROL ................................................................................................................... 31

11. LIMITATIONS OF THE RESEARCH METHODS............................................................................... 31

12. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE REACTIONS ........................... 32

13. GOVERNANCE BOARD ASPECTS ................................................................................................ 32

14. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS ....................................... 32

15. OTHER ASPECTS ....................................................................................................................... 32

16. REFERENCES ............................................................................................................................. 33

17. ANNEXES ................................................................................................................................. 34

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Study Title DARWIN EU® – Incidence rates of venous thromboembolic events in

cancer patients

Protocol version 1.0

Date 30/10/2024

EU PAS number Study not yet registered

Active substance None

Medicinal product None

Research question

and objectives

This study aims to estimate incidence rates of venous thromboembolic

events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous

thromboembolism (VTE, composite of DVT and PE), pelvic venous

thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and

extra-hepatic vein thrombosis, retinal vein thrombosis (RVT), including

retinal central vein thrombosis, and disseminated intravascular

coagulation (DIC) in adult patients (aged 18 and above) newly diagnosed

with selected cancers in 2016-2023 (lung, breast, ovary, corpus uteri,

prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone,

kidney, melanoma, lymphoma and leukaemia) and to describe their

characteristics at the time of cancer diagnosis.

The specific objectives of the study are:

1. To estimate the incidence rates of thromboembolic events in

patients newly diagnosed with each type of selected cancers

stratified by country/database, age group, sex, study sub-period,

and cancer stage (when available) one and two years after cancer

diagnosis.

2. To characterise cancer patients at the time of cancer diagnosis in

terms of demographics, comorbidities, concomitant medications,

as well as treatments received in the first 90 days after cancer

diagnosis.

Country(ies) of study Belgium, Denmark, Estonia, Finland, Germany, The Netherlands, Spain,

United Kingdom

Author Anton Barchuk ([email protected])

Talita Duarte-Salles ([email protected])

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LIST OF ABBREVIATIONS

Abbreviation Name

ATC Anatomical Therapeutic Chemical

CDM Common Data Model

CPRD Clinical Practice Research Datalink

DARWIN EU® Data Analysis and Real World Interrogation Network

DK-DHR Danish Data Health Registries

DOI Declaration Of Interests

DQD Data Quality Dashboard

DRE Digital Research Environment

DVT Deep Venous Thrombosis

DIC Disseminated Intravascular Coagulation

GP General Practitioner

EHR Electronic Health Record

EMA European Medicines Agency

EBB Estonian Biobank

ECOG Eastern Cooperative Oncology Group

EGCUT Estonian Genome Center at the University of Tartu

ENCePP European Network of Centres for Pharmacoepidemiology and

Pharmacovigilance

ICD-O-3 International Classification of Diseases for Oncology, 3rd Edition

ICD-10 International Classification of Diseases, 10th revision

ICPC-1 International Classification of Primary Care

IPCI Integrated Primary Care Information Project

LPD Longitudinal Patient Database

OHDSI Observational Health Data Sciences and Informatics

OMOP Observational Medical Outcomes Partnership

PE Pulmonary Embolism

PVT Pelvic Venous Thrombosis

RVT Retinal vein thrombosis

SNOMED Systematized Nomenclature of Medicine

SVT Splanchic Vein Thrombosis

UKBB UK Biobank

VTE Venous Thromboembolism

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1. TITLE

DARWIN EU® – Incidence rates of venous thromboembolic events in cancer patients

2. RESPONSIBLE PARTIES – STUDY TEAM

Study team role Names Organisation

Study Project Manager /

Principal Investigator

Talita Duarte-Salles

Anton Barchuk

Erasmus MC

Data Scientist Cesar Barboza

Ger Inberg

Maarten van Kessel

Adam Black

Ross Williams

Erasmus MC

Epidemiologist Berta Raventós

Julieta Politi

Erasmus MC

Clinical Domain Expert Anton Barchuk Erasmus MC

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Data Partner* Names Organisation

CPRD GOLD and UKBB Antonella Delmestri University of Oxford

DK-DHR Claus Møldrup

Elvira Bräuner

Susanne Bruun

Tine Iskov Kopp

Cæcilie Brinth Christiansen

Danish Medicines Agency

EBB Marek Oja

Raivo Kolde Estonian Biobank, Estonia

FinOMOP-HILMO Anna Hammais

Gustav Klingstedt

Finnish Care Register for Health

Care, Finland

FinOMOP-HUS Eric Fey

Kimmo Porkka

Tiina Wahlfors

Hospital District of Helsinki and

Uusimaa, Finland

IQVIA DA Germany and IQVIA LPD

Belgium

Gargi Jadhav

Isabella Kacmarczyl

Akram Mendez

Hanne van Ballegooijen

Dina Vojinovic

IQVIA

IPCI Katia Verhamme Integrated Primary Care

Information, Netherlands

SIDIAP Anna Palomar-Cros

Irene López-Sánchez

Agustina Giuliodori

IDIAPJGol

*Data partners’ role is only to execute code at their data source, review and approve their results. These people do not have an

investigator role.

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3. ABSTRACT

Title

DARWIN EU® – Incidence rates of venous thromboembolic events in cancer patients

Rationale

Cancer-associated venous thrombosis is relatively common: from 20% to 30% of all primary venous thromboembolic events are cancer-associated. While cancer patients have an increased risk of developing venous thromboembolism (VTE) compared to individuals without underlying malignancies, it is also recognised as one of the major causes of death in cancer patients. Still, the reported incidence varies across different populations and cancer types and can also be attributed to variations in patient characteristics, management options, and the cancer stage at diagnosis. The incidence of VTE was found to be higher in cases of renal cell, ovarian, pancreatic, stomach, and lung cancers, as well as acute myelogenous leukaemia and non-Hodgkin lymphoma during the four months immediately preceding the cancer diagnosis. When investigating a safety signal, reliable information on background risk is crucial to assess potential associations with oncological treatments.

Research Objectives

This study aims to estimate incidence rates of venous thromboembolic events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis, retinal vein thrombosis (RVT), including retinal central vein thrombosis, and disseminated intravascular coagulation (DIC) in adult patients (aged 18 and above) newly diagnosed with selected cancers in 2016- 2023 (lung, breast, ovary, corpus uteri, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia) and to describe their characteristics at the time of cancer diagnosis.

The specific objectives of the study are:

1. To estimate the incidence rates of thromboembolic events in patients newly diagnosed with each type of selected cancers stratified by country/database, age group, sex, study sub-period, and cancer stage (when available) one and two years after cancer diagnosis.

2. To characterise cancer patients at the time of cancer diagnosis in terms of demographics, comorbidities, concomitant medications, as well as treatments received in the first 90 days after cancer diagnosis.

Research Methods

Study design

Population-based cohort study.

Population

The study population will include all individuals aged 18 years and above with a primary diagnosis of one of the selected cancers (lung, breast, ovary, corpus uteri, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia) in the study period from 01/01/2016 to 31/12/2023. Only patients with the first and one cancer diagnosis (except non-melanoma skin cancer) will be included. Cancer cases and thromboembolic events will be identified based on appropriate computable phenotyping algorithms. Conditions in the OMOP CDM use the Systematised

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Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. The International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) will also be considered for cancer diagnoses. Additional eligibility of a minimum of 1 year of potential follow-up time will be imposed to ensure sufficient time to capture potential outcomes of interest.

Variables

Outcomes will include thromboembolic events, in particular, DVT, PE, VTE (composite of DVT and PE), PVT, SVT, RVT, DIC.

Data sources

1. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom 2. Danish Data Health Registries (DK-DHR), Denmark 3. Estonian Biobank (EBB), Estonia 4. Finnish Care Register for Health Care (FinOMOP-HILMO) Finland 5. Hospital District of Helsinki and Uusimaa (FinOMOP-HUS), Finland 6. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 7. IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium), Belgium 8. Integrated Primary Care Information (IPCI), Netherlands 9. The Information System for Research in Primary Care (SIDIAP), Spain. 10. UK Biobank (UKBB), United Kingdom

Sample size

No sample size will be calculated as this is a descriptive study. However, analysis by strata will be limited to databases with enough cases (5 or more) to provide meaningful results. Summary measures of occurrence will not be calculated for strata with counts less than 5.

Data analyses

Analyses will be conducted separately for each database and carried out in a federated manner, allowing analyses to be run locally without sharing patient-level data.

The incidence of thromboembolic events (Objective 1) will be estimated over one and two years after the selected cancer diagnosis. Each cancer type and outcome will be assessed separately. Large-scale patient- level characterisation (Objective 2) will be conducted at the index date. Age and sex will be described at the time of diagnosis. The medical history and medication will be assessed at the index date.

For all analyses, absolute and relative frequencies will be reported. A minimum cell count of 5 will be used when reporting results, with any smaller counts reported as “<5” and zero counts as “0”. Overall analyses will be done separately for each database. Further stratification by age category (18-29; 30-39; 40-49; 50- 59; 60-69; 70-79; 80-89; 90 and over), sex, study sub-period, cancer stage will be conducted when possible (minimum cell count reached and data available). The following sub-periods will be also used: 2016-2019, 2020-2023.

4. AMENDMENTS AND UPDATES

None.

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5. MILESTONES

Study deliverables Timeline

Draft Study Protocol October 2024

Final Study Protocol October/November 2024 (or upon EMA acceptance)

Creation of Analytical code and Phenotyping November/December 2024

Execution of Analytical Code on the data January 2024 (depending on IRB approval dates)

Draft Study Report February 2024

Final Study Report February 2024

6. RATIONALE AND BACKGROUND

Cancer-associated venous thrombosis is relatively common: from 20% to 30% of all primary venous thromboembolic events are cancer-associated (Timp, 2013). While cancer patients have an increased risk of developing venous thromboembolism (VTE) compared to individuals without underlying malignancies (Blom, 2005), it is also recognised as one of the major causes of death in cancer patients (Wang, 2017). Still, the reported incidence varies across different populations and cancer types and can also be attributed to variations in patient characteristics, management options, and the cancer stage at diagnosis (Timp, 2013).

The association between cancer and thromboembolic events was assessed in various studies. In a large cohort of cancer patients, the incidence of thromboembolic events was found to be higher in cases of renal cell, ovarian, pancreatic, stomach, and lung cancers, as well as acute myelogenous leukaemia and non- Hodgkin lymphoma during the four months immediately preceding the cancer diagnosis (White, 2005). The prevalence of thromboembolic events at the time of diagnosis was highest for pancreatic cancer and lowest for breast cancer in another registry-based study (Ohashi, 2020). All cancer sites showed an increased prevalence in the thromboembolic events incidence cohort (Petterson, 2015). The incidence of thromboembolic events was notably high during the first few months of chemotherapy in a cohort of cancer patients followed for up to 12 months, with higher odds observed in pancreatic, gastric, and lung cancers (Khorana, 2013). Certain cancer medications have also been associated with higher risks of thromboembolic events (Nalluri, 2008; Khorana, 2013). Additionally, major surgery is known to be associated with thromboembolic events, with an increased risk that persists for 90 to 120 days post-surgery (Björklund, 2024). A high rate of recurrent thromboembolic events is observed over time following the discontinuation of anticoagulant therapy in patients with cancer-associated thrombosis (van Hylckama Vlieg, 2023).

When a safety signal of this nature appears in cancer populations, it can be challenging to assess a potential association with new treatment options without reliable information on background risk. This study

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addresses this knowledge gap by generating background incidence rates of thromboembolic events events among patients with selected cancer types.

7. RESEARCH QUESTION AND OBJECTIVES

This study aims to estimate incidence rates of venous thromboembolic events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis, retinal vein thrombosis (RVT), including retinal central vein thrombosis, and disseminated intravascular coagulation (DIC) in adult patients (aged 18 and above) newly diagnosed with selected cancers in 2016- 2023 (lung, breast, ovary, corpus uteri, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia) and to describe their characteristics at the time of cancer diagnosis.

The specific objectives of the study are:

1. To estimate the incidence rates of thromboembolic events in patients newly diagnosed with each type of selected cancers stratified by country/database, age group, sex, study sub-period, and cancer stage (when available) one and two years after cancer diagnosis.

2. To characterise cancer patients at the time of cancer diagnosis in terms of demographics, comorbidities, concomitant medications, as well as treatments received in the first 90 days after cancer diagnosis.

A description of the proposed objectives to be achieved in the study is found in Table 1.

Table 1. Primary and secondary research questions and objectives.

A. Primary research question and objective.

Objective: To estimate the incidence rates of thromboembolic events in patients

newly diagnosed with each type of selected cancers stratified by

country/database, age group, sex, study sub-period, and cancer stage

(when available) one and two years after cancer diagnosis.

Hypothesis: N/A

Population (mention key inclusion-

exclusion criteria): The study population will include all individuals aged 18 years and above

with a primary diagnosis of selected cancers in the study period from

01/01/2016 to 31/12/2023. Selected cancer types include:

• Malignant neoplasm of oesophagus (specified in the protocol as

oesophageal cancer),

• Malignant neoplasm of stomach (stomach cancer),

• Malignant neoplasm of colon, rectosigmoid junction, rectum,

anus and anal canal (colorectal cancer),

• Malignant neoplasm of liver and intrahepatic bile ducts (liver

cancer),

• Malignant neoplasm of pancreas (pancreatic cancer),

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• Malignant neoplasm of trachea, bronchus and lung (lung

cancer),

• Malignant neoplasms of bone and articular cartilage

• (bone cancer)

• Malignant melanoma of skin (skin melanoma)

• Malignant neoplasm of breast (breast cancer),

• Malignant neoplasm of corpus uteri (corpus uteri cancer

including endometrial cancer),

• Malignant neoplasm of ovary (ovarian cancer),

• Malignant neoplasm of prostate (prostate cancer),

• Malignant neoplasm of kidney, except renal pelvis (kidney

cancer),

• Malignant neoplasm of meninges, brain and spinal cord, cranial

nerves and other parts of central nervous system (brain cancer),

• Malignant neoplasms, stated or presumed to be primary, of

lymphoid, haematopoietic and related tissue (lymphoma and

leukaemia)

Only patients with the first and one cancer diagnosis (except non-

melanoma skin cancer) will be included. Additional eligibility of a

minimum of 1 year of potential follow-up time will be imposed to ensure

sufficient time to capture potential outcomes of interest.

Exposure: N/A

Comparator: N/A

Outcome: Venous thromboembolic events:

• deep vein thrombosis (DVT),

• pulmonary embolisms (PE),

• venous thromboembolism (VTE, composite of DVT and PE),

• pelvic venous thrombosis (PVT),

• splanchic vein thrombosis (SVT), including hepatic and extra-

hepatic vein thrombosis,

• retinal vein thrombosis (RVT), including retinal central vein

thrombosis

• disseminated intravascular coagulation (DIC)

Time (when follow up begins and

ends):

For each outcome, study participants will be followed up from the date of

the selected cancer diagnosis (index date) until the earliest of the

following events: occurrence of the outcome, end of follow up (1 year for

the main analysis or 2 years), loss to follow-up, end of data availability, or

date of death.

Setting: Routinely collected data from 10 databases in 8 European countries.

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Main measure of effect: Proportions, incidence rates and incidence rate ratios if information on

stage is available

B. Secondary research question and objective.

Objective: To characterise cancer patients at the time of cancer diagnosis in terms of

demographics, comorbidities, concomitant medications, as well as

treatments received in the first 90 days after cancer diagnosis.

Hypothesis: N/A

Population (mention key inclusion-

exclusion criteria): The study population will include all individuals aged 18 years and above

with a primary diagnosis of selected cancers in the study period from

01/01/2016 to 31/12/2023. Selected cancer types include:

• Malignant neoplasm of oesophagus (specified in the protocol as

oesophageal cancer, corresponding),

• Malignant neoplasm of stomach (stomach cancer),

• Malignant neoplasm of colon, rectosigmoid junction, rectum,

anus and anal canal ( colorectal cancer),

• Malignant neoplasm of liver and intrahepatic bile ducts (liver

cancer),

• Malignant neoplasm of pancreas (pancreatic cancer),

• Malignant neoplasm of trachea, bronchus and lung (lung

cancer),

• Malignant neoplasms of bone and articular cartilage

• (bone cancer)

• Malignant melanoma of skin (skin melanoma)

• Malignant neoplasm of breast (breast cancer),

• Malignant neoplasm of corpus uteri (corpus uteri cancer

including endometrial cancer),

• Malignant neoplasm of ovary (ovarian cancer),

• Malignant neoplasm of prostate (prostate cancer),

• Malignant neoplasm of kidney, except renal pelvis (kidney

cancer),

• Malignant neoplasm of meninges, brain and spinal cord, cranial

nerves and other parts of central nervous system (brain cancer),

• Malignant neoplasms, stated or presumed to be primary, of

lymphoid, haematopoietic and related tissue (Lymphoma and

leukaemia)

Only patients with the first and one cancer diagnosis (except non-

melanoma skin cancer) will be included. Additional eligibility of a

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minimum of 1 year of potential follow-up time will be imposed to ensure

sufficient time to capture potential outcomes of interest.

Exposure: N/A

Comparator: N/A

Outcome: Medical History: asthma, COPD, chronic liver disease, Crohn’s disease,

Diabetes mellitus, gastro-oesophageal reflux disease (GERD), GI-Bleeding,

Human Immunodeficiency Virus (HIV), Hyperlipidaemia, Hypertension,

Obesity, Osteoarthritis, Pneumonia, Psoriasis, Renal impairment,

Ulcerative Colitis, Viral Hepatitis, Visual system disorder [General] --

Schizophrenia, Dementia, Parkinson, Depressive disorder, Anxiety,

Attention Deficit Hyperactivity Disorder (ADHS) [Neurology],

thromboembolic events outcomes of interest mentioned above --- Any

cancer except non-melanoma skin cancer (for quality assessment

purposes, this should be 0 in our study population before index date).

Medication use: Agents acting on the renin-angiotensin system,

Antibacterials for systemic use, Antidepressants, Antiepileptics, Anti-

inflammatory and antirheumatic products, Antineoplastic agents, Anti-

psoriatic, Antithrombotic agents, Beta blocking agents, Calcium channel

blockers, Diuretics, Drugs for acid related disorders, Drugs for obstructive

airway diseases, Drugs used in diabetes, Immunosuppressants, Lipid

modifying agents, Opioids, Psycholeptics, Psychostimulants, agents used

for ADHD and nootropics [General] -- contraceptives [contraceptives].

Time (when follow up begins and

ends):

Medical History at the index date and medication at the index date and

from 1 to 90 days.

Setting: Routinely collected data from 10 databases in 8 European countries.

Main measure of effect: Proportions

8. RESEARCH METHODS

8.1 Study type and study design

This will be a population-level descriptive epidemiology and patient-level characterisation study. As described in the DARWIN EU® Complete Catalogue of Standard Data Analyses (Table 2). A retrospective cohort study of all newly diagnosed adult patients with selected cancers will be conducted.

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Table 2. Description of potential study types and related study designs.

Study type Study design Study classification

Population-level descriptive

epidemiology

Population-level cohort Off the shelf

Patient-level characterisation Cohort analysis Off the shelf

8.2 Study setting and data sources

This study will use routinely collected health data from 10 databases from 8 European countries. All of them have been previously mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The databases to be included in the study are:

1. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom 2. Danish Data Health Registries (DK-DHR), Denmark 3. Estonian Biobank (EBB), Estonia 4. Finnish Care Register for Health Care (FinOMOP-HILMO) Finland 5. Hospital District of Helsinki and Uusimaa (FinOMOP-HUS), Finland 6. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 7. IQVIA Longitudinal Patient Database Belgium (IQVIA - LPD Belgium), Belgium 8. Integrated Primary Care Information (IPCI),Netherlands 9. The Information System for Research in Primary Care (SIDIAP), Spain. 10. UK Biobank (UKBB), United Kingdom

Information on data sources is available in Table 3. Databases were selected based on the person count of cancer diagnoses under interest and thromboembolic events, and European representativeness. We also considered the lack of major issues and errors during the most recent internal and Darwin EU onboarding quality checks for the selected databases. Preliminary stratification by tumour stage will only be feasible in DK-DHR, EBB and FinOMOP-HUS.

The selected databases also fulfil the criteria required to capture outcomes of interest and relevant data to conduct a patient-level characterisation of newly diagnosed cancer patients across different European settings and regions. Not all databases have all outcomes of interest. DVT is not present in IQVIA - LPD Belgium. SVT counts are limited in EBB, IQVIA - LPD Belgium. RVT is present only in CPRD GOLD, SIDIAP and UKBB.

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Table 3. Description of the selected data sources.

Country Name of Database Justification for Inclusion Health Care

setting

Type of Data Number of

active

subjects

Data lock for

the last

update

GB CPRD GOLD Covers primary care setting, data on

cancer diagnoses, comorbidities,

medications, and date of death.

Primary care,

hospital care

(OP)

EHRs 17.5 M 2024-04-15

DK DK-DHR National health data database

which includes information on

cancer diagnoses, staging and

medical history

All settings EHRs,

registries,

claims

8.5 M 2024-05-15

EE EBB Contains health insurance claims,

digital prescriptions, discharge

information and causes of death

through linkage with the national

death register. Data is linked to

cancer registry.

Primary care,

hospital care (IP

and OP)

EHRs, claims,

registries,

biobank

0.2 M 2023-03-20

FI FinOMOP - HILMO Nation-wide hospital registry data

with high-quality information on

cancer diagnoses and mortality.

Hospital care (IP

and OP)

EHRs,

registries

7.1 M 2024-06-24

FI FinOMOP - HUS Hospital registry which includes

information on cancer patients and

medical history.

Hospital care (IP

and OP)

EHRs 3.5 M 2024-05-03

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Country Name of Database Justification for Inclusion Health Care

setting

Type of Data Number of

active

subjects

Data lock for

the last

update

DE IQVIA DA Germany Covers primary care setting with

information on cancer diagnoses

and medical history.

Primary care EHRs 43.1 M 2024-03-25

BE IQVIA LPD Belgium Covers primary care setting with

information on cancer diagnoses

and medical history.

Primary care EHRs 1.1 M 2024-03-25

NL IPCI Covers primary care setting, data on

cancer diagnoses previously

validated, information available on

comorbidities, medications, and

date of death.

Primary care EHRs 2.9 M 2024-08-29

ES SIDIAP Covers primary care setting with

information on cancer diagnoses

and medical history.

Primary care EHRs 8.6 M 2023-03-20

GB UKBB Genetic data on biobank

participants linked to EHRs from

primary care, hospitalisations,

cancer registrations and mortality.

Primary care,

hospital care (IP

and OP)

EHRs,

registries,

biobank

0.5 M 2024-02-16

BE = Belgium, DE = Germany, DK=Denmark, EE = Estonia, ES=Spain, EHR=Electronic Health Record, FR = France, GB = United Kingdom of Great Britain and

Northern Ireland, FI = Finland, IP = Inpatient, NL = the Netherlands, OP = outpatient

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Clinical Practice Research Datalink GOLD (Oxford) (CPRD GOLD)

The CPRD GOLD database collects data in the United Kingdom and is maintained by Clinical Practice Research Datalink. CPRD GOLD contains data from all four UK constituent countries (England, Scotland, and Northern Ireland). A database contains data from 1987 to present. Data are collected from general practitioner (GP) clinics that use the Vision® software system. GP clinics are responsible for non-emergency care and referrals. Data is collected from patient records stored at GP clinics. Over 98% of the UK population is registered with a GP. Covering 4.6% of the current UK population, CPRD GOLD includes 4.9% of contributing GP practices. Compared with the UK Census 2011, CPRD patients are broadly representative of the UK population in terms of age, sex, and ethnicity and comparable to the Health Survey for England for body mass index distribution. The CPRD GOLD may not be representative of all practices in the UK based on geography and size. It obtains data from electronic health records.

The database currently holds information about the person (demographics), visits (inpatient and outpatient), conditions and procedures (billing codes), drugs (outpatient prescriptions and inpatient orders and administrations), measurements (laboratory tests and vital signs), and dates of death (in or out- hospital death). CPRD GOLD uses SNOMED medical terms. Transformation-level validation checks for referential integrity between records ensure that no orphan records are included in the database (for example, all event records link to a patient). Duplicate records are identified and removed. In contrast, research-quality-level validation covers the actual content of the data. CPRD provides a patient-level data quality metric as a binary ‘acceptability’ flag. This is based on recording and internal consistency of key variables, including date of birth, practice registration date, and transfer out date. Validation of the CPRD has shown a high positive predictive value for some diagnoses, and where evaluated, comparisons of incidence with other UK data sources are also broadly similar. Research into data quality has shown large variations in the inter-practice recording of data. Established linkages include, but are not limited to, Hospital Episode Statistics (hospitalisation data), Office for National Statistics (mortality data including causes of death), Index of Multiple Deprivation and Townsend scores (deprivation data), and disease registries. Vital status (death date and causes) is obtained from the Office for National Statistics.

CPRD GOLD is limited to GP records. General practices receive information about patient contacts with secondary care, which must be manually entered into the patient record. However, the database also combines data from various sources through effective linkages. Primary care data quality is variable because GPs enter data during routine consultations, not for research. The database was described by Herrett et al., 2015. The database description is also available at cprd.com.

Danish Data Health Registries (DK-DHR)

Danish health data is collected, stored, and managed in national health registers at the Danish Health Data Authority. It covers the entire population, which makes it possible to study the development of diseases and their treatment over time. There are no gaps in terms of gender, age and geography in Danish health data due to mandatory reporting on all patients from cradle to grave in all hospitals and medical clinics. Personal identification numbers link data across registers, so we have data on all Danes throughout their lives, regardless of whether they have moved around the country. High data quality due to standardisation, digitisation and documentation means Danish health data is not based on interpretation. The Danish Health Data Authority is responsible for the national health registers and maintaining and developing standards and classifications in the Danish healthcare system. The legislation ensures a balance between personal data protection and use. In the present database, we have access to the following registries for the entire Danish population of 5.9 million persons from 1.1.1995: The Central Person Registry (CPR), The National Patient Registry (LPR), The Register of Pharmaceutical Sales (LSR), The National Cancer Register (CAR), The Cause of Death registry (DAR), The Clinical Laboratory Information Register (LAB), COVID-19 test and

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vaccination Registries (SSI-OVD, SSI-DDV), The complete Vaccination registry (DDV_all). All data registered from 1.1.1995 will be included.

Estonian Biobank (EBB)

The EBB collects data in Estonia and is maintained at the Estonian Genome Center, University of Tartu. It is a nationwide database, and the network of recruitment offices for EBB covers all 15 counties of the country. The Estonian Genome Project Foundation initiated the Estonian Biobank Project in 1999, which was transformed into the Estonian Genome Center of the University of Tartu (EGCUT) in 2007. The data is available from 2004 onwards. The Estonian Biobank cohort is a volunteer-based sample of the Estonian resident adult population. EBB represents an Estonian population-based cohort size of 52,000 participants aged 18 years and older recruited at GP offices, private practices, and hospitals or in the recruitment offices of the Estonian Genome Center. The age, sex, and geographical distribution closely reflect those of the Estonian adult population and encompass nearly 5% of the population. Overall, the representation of men in the biobank is 3.4%, and women’s is 5.5%. Older people tend to participate less frequently; however, all age groups are well represented. The database obtains data from the biobank records. All participants have undergone a standardised health assessment, including providing blood samples for purification of DNA, white blood cells, and plasma, and completed a questionnaire covering various health-related topics, such as lifestyle, diet, and clinical diagnoses. Diseases and health problems are recorded as ICD-10 codes and prescribed medicine according to the ATC classification.  

For all starting data collectors, the first ten questionnaires were monitored for completeness and illogical answers, and after that, 10% were selected randomly for monitoring; 21% were inspected and corrected when necessary. From the monitored questionnaires, 99% were classified as high quality, meaning all the fields were filled in, and the answers appeared logical. Follow‐up data are available via linkage with national health‐related registries and re‐examination of participants. Furthermore, electronic health records are updated every half year for phenotypic outcome information. The EBB database is regularly linked with national registries, hospital databases, and the national health insurance fund database, which holds treatment and service bills. Vital status (death date and causes) is obtained from the Causes of Death Registry. Participation in the EBB cohort is voluntary; therefore, the biobank does not represent a classical random sample and could be subject to recruitment bias. Although recruitment was open to everyone, there is a disproportion of ethnic Estonians and ethnic Russians in the biobank, with Estonians being overrepresented and Russians underrepresented. Also, the limited depth of collected data can sometimes limit the number of projects in which the data can be used. The database was described by Leitsalu et al., 2015, 10.1093/ije/dyt268. The database description is also available at genomics.ut.ee/en/content/estonian-biobank.

Finnish Care Register for Health Care (FinOMOP - HILMO)

The Finnish Care Register for Health Care (fi: Hoitoilmoitusrekisteri) continues the former Hospital Discharge Register, which originally gathered data on hospital discharge (Sund et al., 2012). The Care Register has comprehensive data on services and service users nationwide, including Finnish public inpatient and outpatient primary and specialised care. Since 1998, the register has covered public outpatient and inpatient specialised care and private inpatient care (TerveysHilmo). Since 2011, the register has covered public primary care (AvoHilmo). Since 2020, the register has covered private outpatient care and occupational care. The CDM is currently produced from the data collection on inpatient and outpatient specialised care (TerveysHilmo) and is limited to observation periods commencing after 01/01/2015. The Register of Primary Health Care Visits (AvoHilmo) is currently outside the scope of the CDM and will be added to CDM during the remainder of 2023. The inclusion of data collected before 2015 is also being planned. The National Population Registry is also used as a source for the CDM database. The National

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Population Registry data forms the basis for forming the patient population. This ensures up-to-date location (municipality of residence) of patients and complete death occurrences (although not the cause of death). Using the complete population as a basis for the person table also facilitates calculations on a population level, e.g. incidence rates. The HILMO database has been used to assess the quality of cancer registry data in Finland (Leinonen et al., 2017).

Hospital District of Helsinki and Uusimaa (FinOMOP - HUS)

The HUS data lake is a comprehensive, integrated data source derived in real-time from all patients who visit the HUS hospitals and receive treatment (Vikkula et al., 2023). HUS is responsible for specialised healthcare in Finlands’ Uusimaa region and the treatment of many rare and severe diseases nationally centralised to HUS. HUS’s catchment area covers about 2.2 million people. In 2023, there were 2.43 million booked appointments and 255,896 emergency department visits for specialist medical care. 691,702 patients received any treatment in HUS specialist medical care and at emergency departments, and 86,849 surgical procedures were performed. All visits, examinations, laboratory tests, procedures, and treatments are recorded in the HUS IT systems and integrated into the data lake. The data lake stores decades of clinical information in digital format, and data from past and current source systems are available.

IQVIA Disease Analyzer Germany (IQVIA DA Germany)

Germany DA is collected from extracts of patient management software used by GPs and specialists from ambulatory care settings. Patients visiting multiple providers are not cross-identified for data protection reasons and, therefore, recorded as separate in the system. Dates of service include from 1992 through the present. The first and last consultation dates define observation time. Germany has no mandatory GP system, and patients can choose specialists. Drugs are recorded as prescriptions of marketed products. No explicit registration or approval is needed for drug utilisation studies.

IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium)

Belgium Longitudinal patient data (LPD) is collected from GP prescribing systems and contains patient records on all signs and symptoms, diagnoses and prescribed medications. The information recorded allows patients and doctors to be monitored longitudinally. Data are recorded directly in real-time during patient consultations via a practice management software system. It is used in studies to provide various market insights such as treatment trends, patient pathway analysis and treatment compliance. The panel of contributing physicians (a stable 300 GPs) is maintained as a representative sample of Belgium's primary care physician population according to three criteria known to influence prescribing: age, sex and geographical distribution. Currently, the database covers 1.1 M cumulative patients from 2012 through to the present. The panel consists of a stable 300 GPs that are geographically well-spread. The total number of active GPs in Belgium is 15.602. The regional geographical spread of physicians in the LPD data is also representative of the distribution across the country: 57% GPs in the North (compared to 54% nationally), 31% in the South (33% nationally) and 12% in Brussels (13%). The data provider has more than 2.250 GPs under contract, so a replacement is easily found in case of a dropout. Drugs obtained over the counter by the patient outside the prescription system are not reported. No explicit registration or approval is needed for drug utilisation studies.

Integrated Primary Care Information (IPCI)

The database collects data in the Netherlands. It was started in 1992 by the Department of Medical Informatics of the Erasmus University Medical Center in Rotterdam, the Netherlands. The current database contains patient records from 2006 onwards when the size of the database started to increase significantly. IPCI is a nationwide Dutch database. However, it mainly covers the central part of the country, including the most densely populated and non-urban areas. The IPCI database contains data from records of general

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practitioners' (GPs) practices. It contains information on all patients registered with GPs responsible for non-emergency care and referrals. More than 99% of the Dutch population has health insurance, and almost all citizens are registered with a general practitioner. Over 12 months, around 78% of the population has at least one contact with their GP. IPCI included around 350 GP practices out of around 5000 in the country (~ 7%). The demographic composition of the IPCI population mirrors that of the general Dutch population in terms of age and sex.

IPCI obtains data from computer-based patient records. Patient-level data includes demographic information, complaints and symptoms, diagnoses, laboratory test results, lifestyle factors, and correspondence with secondary care, such as referral and discharge letters. Dutch GPs use the International Classification of Primary Care (ICPC-1) coding for complaints, symptoms, and diagnoses, an international standard developed and updated by the World Organization of Family Doctors (WONCA) International Classification Committee.

Extensive quality control steps are performed before each data release. These include comparing patient characteristics between practices and checks to identify abnormal temporal data patterns in practices. Additional checks include over 200 indicators related to population characteristics (e.g., reliability of birth and mortality rates) and medical data (e.g., availability of durations of prescriptions, completeness of laboratory results, availability of hospital letters and prescriptions, the proportion of patients with blood pressure measurement, etc.).

IPCI is not linked with other databases. Vital status (death date and cause) is collected based on GP records. The main limitation is that IPCI is limited to GP records, and although it contains information on referrals and discharge letters, it may not capture specific hospital information. The database profile was described by de Ritter et al., 2022. The database description is also available at ipci.nl.

The Information System for Research on Primary Care (SIDIAP)

The database collects data in Spain and is maintained by the SIDIAP team, supported by the Catalan Health Institute and the Institute for Primary Health Care Research Jordi Gol i Gurina. It contains patient records from 2005 onwards and is updated every six months. It is a regional database covering the region of Catalonia. SIDIAP collects data in the primary care setting.  It contains data from the population registered in over 280 primary care practices throughout Catalonia.  Approximately 80% of the population registered with primary care is covered by SIDIAP. The demographic composition within SIDIAP closely mirrors that of the broader Catalan population, encompassing a representative spectrum of geographic distribution, age, and sex proportions. SIDIAP obtains data from electronic health records.  The dataset covers demographics, all-cause mortality, disease diagnoses, prescription and dispensation records of drugs, results of laboratory tests, socio-economic indicators, vaccination records, lifestyle information, parent-child linkage, and various clinical parameters.  Diseases are classified under the International Classification of Diseases 10th revision (ICD-10)

Quality checks have been implemented, including central identification of duplicate patient IDs and visual inspection for temporal patterns in the registry of a certain variable. Furthermore, the data undergoes assessment for availability (longitudinally and reliability), plausibility (range checks and unusual values), and visualisation tools. Specifically, for biochemistry data, consistency for measurements taken in different laboratories is assessed, and unit conversion is undertaken when needed. SIDIAP is linked with numerous other databases. It integrates data from external sources, including laboratory biomarker data, drug prescription and dispensation records, hospital discharge records, mental health centres, and other specific disease registries.  Vital status (death date and cause) is collected through linkage with the civil registry. The main limitation is that SIDIAP covers only primary health care records. However, it is combined with data

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from various other sources through effective linkages.  The database profile was described by de Recalde et al., 2022.  The database description is also available at sidiap.org.

UK BioBank (UKBB)

UK Biobank is a powerful biomedical database that can be accessed globally to enable discoveries to improve public health. UK Biobank contains in-depth genetic, biomarker, imaging and health information from over half a million volunteers living in the UK aged 40–69 years at the time of recruitment (2006– 2010). UK Biobank has collected unprecedented biological and medical data for a large-scale, long-term prospective study. With their consent, they regularly provide blood, urine and saliva samples and detailed information about their lifestyle, which is then linked to their health-related records (e.g. primary care data, hospital data, cancer registry) to provide a deeper understanding of how individuals experience diseases. Since 2012, the UK Biobank database, the largest and richest of its kind, has been open to applications from researchers. The resource is available in anonymised format to scientists from the UK and worldwide, subject to verification that the research is health-related and in the public interest. Researchers are required to publish their results in an open-source publication site or an academic journal and return their findings to the UK Biobank. At the time of writing, nearly 3,600 research applications have been approved for using UK Biobank data, and 3,239 peer-reviewed articles based on them have been published.

8.3 Study period

The study period will be from 01/01/2016 to 31/12/2023 or the end of available data in each source if it

comes earlier (see Table 3 Data lock for the last update column for more details).

8.4 Follow-up

Study participants will be followed up from the date of cancer diagnosis (index date, Table 4) until the first

occurrence of any of the following events: occurrence of the outcome, loss to follow-up, end of follow-up

(one or two years), end of data availability, or date of death.

Table 4. Operational definition of time 0 (index date) and other primary time anchors.

Study

population

name(s)

Time Anchor

Description

(e.g. time 0)

Number of

entries

Type of

entry

Washout

window

Care

Settin

g1

Code

Type2

Diagnosis

position

Incident

with

respect

to…

Measureme

nt

characteristi

cs/

validation

Source

of

algorith

m

Cancer

patients

Date of

cancer

diagnosis

Single

entry

Incident [any

time

, -1]

IP,

OP

,

OT

SNOMED,

ICD-O-3

Any Any

cancer

diagnos

is

except

non-

melano

ma skin

cancer

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

2 SNOMED = Systematized Nomenclature of Medicine, ICD-O-3: International Classification of Diseases for Oncology, 3rd Edition

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8.5 Study population with inclusion and exclusion criteria

The study population will include all individuals aged 18 years and above with a primary diagnosis of selected cancers in the study period. Cancer types will include lung, breast, ovary, endometrium, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia. Only patients with the first and one cancer diagnosis (except non-melanoma skin cancer) will be included. Cancer cases and thromboembolic events will be identified based an appropriate computable phenotyping algorithms. Conditions in the OMOP CDM use the Systematised Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. For cancer diagnoses, the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) will also be considered. Algorithms to reproduce cancer phenotypes will be shown along with the study results.

Additional eligibility of a minimum of 1 year of potential follow-up time will be imposed to ensure sufficient time to capture potential outcomes of interest. For instance, in a database with data up to 31/12/2023, cancer cases diagnosed from 01/01/2016 up to 01/01/2023 will be included. A prior history requirement of one year of observation prior to the index date will be imposed. This is especially relevant for primary care databases, where a minimum observation period of one year before a cancer diagnosis is required to detect prevalent cases.

Appendix II provides a preliminary code list for each cancer. The code list might be modified after cohort diagnostics

The operational definitions of the inclusion and exclusion criteria are presented in Error! Reference source not found. and Table 6, respectively.

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Table 5. Operational definitions of inclusion criteria.

Criterion Details Order of application

Assessment window

Care Settings¹

Code Type

Diagnosis position

Applied to study

populations:

Measurement characteristics/

validation

Source for algorithm

Patients newly diagnosed with selected cancers

Primary selected cancer After - IP, OP, OT

SNOME D, ICD- O-3

N/A Cancer patients

N/A N/A

Age Participants aged 18 or above

After At index date

IP, OP, OT

N/A N/A Cancer patients

N/A N/A

Minimum prior observation period of 365 days

Only participants with a minimum observation period of 365 days prior to diagnosis of chondrosarcoma (index date) s

Before 365 days OP, OT N/A N/A Cancer patients

N/A N/A

Minimum potential follow-up time

Only participants with a cancer diagnosis (index date) occurring one year prior to end of data availability in the database will be included

After [0, 365] IP, OP, OT

N/A N/A Cancer patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable.

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Table 6. Operational definitions of exclusion criteria.

Criterion Details Order of

application

Assessment

window

Care

Settings¹

Code

Type

Diagnosis

position

Applied to

study

populations:

Measurement

characteristics/

validation

Source

for

algorithm

History of cancer

diagnosis

Participants with a diagnosis of

cancer (any, excluding non-

melanoma skin cancer) any

time prior index date

After Any time

prior to

cancer

diagnosis

OP, IP,

OT

SNOMED,

ICD-O-3

Any Cancer

patients

N/A N/A

Multiple primary

tumors

Participants with a two

diagnoses of cancer (any,

excluding non-melanoma skin

cancer) at index date

After At index

date

OP, IP,

OT

SNOMED,

ICD-O-3

Any Cancer

patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

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8.6 Variables

8.6.1. Exposures

None.

8.6.2. Outcomes

Conditions of interest will include thromboembolic events, in particular, (deep vein thrombosis (DVT),

pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous

thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis,

retinal vein thrombosis (RVT), including retinal central vein thrombosis, and disseminated intravascular

coagulation (DIC).

The operational definition of the outcomes is presented in the Table .

A preliminary list of codes is provided in the Appendix I.

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Table 7. Operational definitions of outcome.

Outcome

name

Details Primary

outcome

Type of

outcome

Washout

window

Care

Settings¹

Code Type Diagnosis

Position

Applied to

study

populations

Measurement

characteristics/

validation

Source of algorithm

DVT See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

PE See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

VTE See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

PVT See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

SVT See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

RTV See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

DIC See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable, DVT = deep vein thrombosis, PE = pulmonary embolisms, VTE = venous thromboembolism, PVT = pelvic venous thrombosis,

SVT = splanchic vein thrombosis, RVT = retinal vein thrombosis, DIC = DIC.

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8.6.3. Other covariates, including confounders, effect modifiers and other variables

The age at the index date (primary cancer diagnosis) will be described. The following age grouping will be used: 18-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90 and over. The sex (male/female) of study participants will also be identified.

Large-scale patient-level characterisation will be conducted at the time of diagnosis. Age and sex at the time of cancer diagnosis will be described for each generated study cohort. Medical history will be assessed at the time of diagnosis. Medication use history will be reported at the time of diagnosis. We will also report medication use for 1 to 90 days post-index date.

A list of pre-specified co-morbidities and co-medications will be described. Co-morbidities and co- medications were selected based on definitions that were previously used in other DARWIN EU studies. These will include:

• Medical History: asthma, COPD, chronic liver disease, Crohn’s disease, Diabetes mellitus, gastro- oesophageal reflux disease (GERD), GI-Bleeding, Human Immunodeficiency Virus (HIV), Hyperlipidaemia, Hypertension, Obesity, Osteoarthritis, Pneumonia, Psoriasis, Renal impairment, Ulcerative Colitis, Viral Hepatitis, Visual system disorder [General] -- Schizophrenia, Dementia, Parkinson, Depressive disorder, Anxiety, Attention Deficit Hyperactivity Disorder (ADHS) [Neurology], thromboembolic events outcomes of interest mentioned above --- Any cancer except non-melanoma skin cancer (for quality assessment purposes, this should be 0 in our study population before index date).

• Medication use: Agents acting on the renin-angiotensin system, Antibacterials for systemic use, Antidepressants, Antiepileptics, Anti-inflammatory and antirheumatic products, Antineoplastic agents, Anti-psoriatic, Antithrombotic agents, Beta blocking agents, Calcium channel blockers, Diuretics, Drugs for acid related disorders, Drugs for obstructive airway diseases, Drugs used in diabetes, Immunosuppressants, Lipid modifying agents, Opioids, Psycholeptics, Psychostimulants, agents used for ADHD and nootropics [General] -- contraceptives [contraceptives].

If available, AJCC\UICC TNM stage groups, and WHO/ECOG performance status will also be described and used for stratification in objective 1.

The operational definition of the covariates is described in the

Table 8. Operational definitions of covariates.

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Characteristic Details Type of

variable

Assessment

window

Care

Settings

¹

Cod

e

Typ

e

Diag

nosis

Posit

ion

Applied to

study

population

s

Meas

ureme

nt

charac

teristi

cs/

valida

tion

Sourc

e for

algorit

hm

Co-

morbidities

Large-scale patient-

level

characterisation

with regard to

underlying

comorbidities

Binary [0,0] IP, OP,

OT

SNO

ME

D

N/A Cancer

patients

N/A N/A

Medication

use

Large-scale patient-

level

characterisation

with regard to use

of concomitant

drugs

Binary [0,0], [1,90] IP, OP,

OT

RxN

orm

N/A

Cancer

patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

8.

Table 8. Operational definitions of covariates.

Characteristic Details Type of

variable

Assessment

window

Care

Settings

¹

Cod

e

Typ

e

Diag

nosis

Posit

ion

Applied to

study

population

s

Meas

ureme

nt

charac

teristi

cs/

valida

tion

Sourc

e for

algorit

hm

Co-

morbidities

Large-scale patient-

level

characterisation

with regard to

underlying

comorbidities

Binary [0,0] IP, OP,

OT

SNO

ME

D

N/A Cancer

patients

N/A N/A

Medication

use

Large-scale patient-

level

characterisation

with regard to use

of concomitant

drugs

Binary [0,0], [1,90] IP, OP,

OT

RxN

orm

N/A

Cancer

patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

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8.7 Study size

No sample size will be calculated as this is a descriptive study. However, analysis by strata will be limited to

databases with enough cases (5 or more) to provide meaningful results. Summary measures of occurrence

will not be calculated for strata with counts less than 5.

8.8 Analysis

8.1.1. Federated network analyses

Analyses will be conducted separately for each database and will be carried out in a federated manner, allowing analyses to be run locally without sharing patient-level data. Before study initiation, the analytics are tested on a subset of the data sources or on a simulated set of patients, and quality control checks are performed. Once all the tests are passed, the final study code is released in the version-controlled Study Repository for execution against all the participating data sources.

The data partners locally execute the analytics against the OMOP CDM in R Studio and review and approve the default aggregated results before returning them to the Coordination Centre. Sometimes, multiple execution iterations are performed, and additional fine-tuning of the code base is needed. A service desk will be available for support during the study execution.

The study results of all data sources are checked, after which they are made available to the team in the

Digital Research Environment (DRE), and the Study Dissemination Phase can start. All results are locked and

timestamped for reproducibility and transparency.

8.8.2 Patient privacy protection

Cell suppression will be applied as required by databases to protect people’s privacy. Cell counts < 5 will be

masked.

8.8.3 Statistical model specification and assumptions of the analytical approach considered

Data analyses

This study will involve the analysis described in Table 9.

Table 9. Description of study types and type of analysis.

Study type Study

classification Type of analyses

Patient-level

characterisation

Off-the-shelf - Large-scale characterisation

- Patient-level characteristics

- Prognosis / progression to a pre-specified outcome

- Standard care description

Population-level

descriptive

epidemiology

Off-the-shelf - Incidence rates of the condition of interest

- Prevalence rates of the condition of interest

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The incidence rates of thromboembolic events (Objective 1) will be estimated over one and two years after the selected cancer diagnosis. The denominator will include the person-time contributed by the cancer patients from the date of diagnosis till the event or end of the respective period (one or two years). The numerator will include the outcomes of interest. Incidence rates will be presented per person-time with 95% confidence intervals derived using the exact method. Additionally, incidence rate ratios will be calculated when information on cancer stage is available with group AJCC/UICC stage as a reference. Each cancer type and outcome will be assessed separately.

Large-scale patient-level characterisation (Objective 2) will be conducted at the index date. Age and sex will be described at the time of diagnosis as well as follow-up time after the index date. The medical history and medication will be assessed at the index date. Medication use will be at the index date and from 1 to 90 days after diagnosis.

For all analyses, absolute counts and proportions will be reported. A minimum cell count of 5 will be used when reporting results, with any smaller counts reported as “<5” and zero counts as “0”. Overall analyses will be done separately for each database.

Further stratification by age category (18-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90 and over), sex,

study sub-period, and cancer stage will be conducted when possible (minimum cell count reached and data

available). The following sub-periods will also be used: 2016-2019 and 2020-2023.

R-packages

The following R packages will be used: “CohortDiagnostics” (https://github.com/OHDSI/CohortDiagnostics/) to evaluate the phenotype algorithms developed for the study.

“IncidencePrevalence” (https://github.com/darwin-eu/IncidencePrevalence) to estimate incidence rates by dividing the number of events by the person-time. Incidence rates will be calculated using function estimateIncidence() with denominator person-time specified by generateDenominatorCohortSet().

“PatientProfiles” (https://github.com/darwin-eu-dev/PatientProfiles) and “CohortCharacteristics” (https://github.com/darwin-eu-dev/CohortCharacteristics) will be used for summarising characteristics of cohorts of patients with cancer.

8.9 Evidence synthesis

Results from analyses described in section 8.8 will be presented separately for each database and no meta-

analysis of results will be conducted.

9. DATA MANAGEMENT

9.1 Data management

All databases are mapped to the OMOP common data model. This enables the use of standardised analytics and tools across the network since the structure of the data and the terminology system is harmonised. The OMOP CDM is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) initiative and is described in detail on the wiki page of the CDM: https://ohdsi.github.io/CommonDataModel and in The Book of OHDSI: http://book.ohdsi.org

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The analytic code for this study will be written in R. Each data partner will execute the study code against their database containing patient-level data and will then return the results set which will only contain aggregated data. The results from each of the contributing data sites will then be combined in tables and figures for the study report.

9.2 Data storage and protection

For this study, participants from various EU member states will process personal data from individuals which is collected in national/regional electronic health record databases. Due to the sensitive nature of this personal medical data, it is important to be fully aware of ethical and regulatory aspects and to strive to take all reasonable measures to ensure compliance with ethical and regulatory issues on privacy.

All databases used in this study are already used for pharmaco-epidemiological research and have a well- developed mechanism to ensure that European and local regulations dealing with ethical use of the data and adequate privacy control are adhered to. In agreement with these regulations, rather than combining person level data and performing only a central analysis, local analyses will be run, which generate non- identifiable aggregate summary results.

The output files are stored in the DARWIN Digital Research Environment. These output files do not contain

any data that allows the identification of subjects included in the study. The DRE implements further

security measures to ensure a high level of stored data protection to comply with the local implementation

of the General Data Protection Regulation (GDPR) (EU) 679/20161 in the various member states.

10. QUALITY CONTROL

General database quality control

Several open-source quality control mechanisms for the OMOP CDM have been developed (see Chapter 15 of The Book of OHDSI http://book.ohdsi.org/DataQuality.html). In particular, data partners are expected to run the OHDSI Data Quality Dashboard tool (https://github.com/OHDSI/DataQualityDashboard). This tool provides numerous checks relating to the conformance, completeness and plausibility of the mapped data. Conformance focuses on checks that describe the compliance of the representation of data against internal or external formatting, relational, or computational definitions; completeness in the sense of data quality is solely focused on quantifying missingness or the absence of data, while plausibility seeks to determine the believability or truthfulness of data values. Each of these categories has one or more subcategories and are evaluated in two contexts: validation and verification. Validation relates to how well data aligns with external benchmarks with expectations derived from known true standards. In contrast, verification relates to how well data conforms to local knowledge, metadata descriptions, and system assumptions.

Study-specific quality control

When defining selected cancers, outcomes and co-morbidities, a systematic search of possible codes for inclusion was identified using CodelistGenerator R package (https://github.com/darwin- eu/CodelistGenerator). This software allows the user to define a search strategy and using this will then query the vocabulary tables of the OMOP CDM to find potentially relevant codes. The codes returned will be then reviewed by two clinical epidemiologists to consider their relevance. In addition, the CohortDiagnostics R package (https://github.com/OHDSI/CohortDiagnostics) will be run to assess the use of different codes across the databases contributing to the study and identify any codes potentially omitted in error. This will allow for a consideration of the validity of the study cohort of patients with the selected

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cancers and co-morbidities in each of the databases and inform decisions around whether multiple definitions are required.

The study code will be based on three R packages currently being developed to (1) estimate incidence rates of thromboembolic events (“IncidencePrevalence”), (2) characterise demographic and clinical characteristics ("PatientProfiles” and “CohortCharacteristics”). These packages will include numerous automated unit tests to ensure the validity of the codes, alongside software peer review and user testing. The study code will be made publicly available via GitHub.

11. LIMITATIONS OF THE RESEARCH METHODS

The study will be informed by routinely collected health care data, and so data quality issues must be considered. In particular, the identification of cancer patients and thromboembolic events may vary across databases. While relatively few false positives would be expected, false negatives may be more likely, especially for databases without patient-level linkage to secondary care data. Underestimation of thromboembolic events is also possible, particularly for rare events with complex diagnoses.

Given the large number and diverse nature of participating data sources, it is important to note that differences in patient representations might arise from disparate coding practices and specifics of data capture. The granularity or detail of concepts representing clinical facts can vary across source terminologies (e.g., ICD-10, Read codes), influencing how information is later transformed into standardised vocabularies (Ostropolets et al., 2021). The preliminary code lists created to identify cancer patients include codes from standard vocabularies used in tumour registries, such as ICD-O-3 codes. However, most databases will only have information on cancer diagnoses using SNOMED codes, which may not be granular enough to cover all the topology and histology details of cancer (Campbell et al., 2014). ICD-O-3 codes are only available at DK-DHR, EBB and UKBB.

The large-scale characterisation will provide an overview of the characteristics, comorbidities, and medication use, including anticancer treatment is available, of cancer patients. However, our study will not differentiate the time before and after cancer treatment initiation, and therefore it will not be able to disentangle the risk of thromboembolic events posed by cancer treatments from the risk posed by cancer itself.

In addition, for the calculation of incidence rates (Objective 1), we will apply a one-year washout to exclude patients who experienced the thromboembolic events under study prior to index date. Given that acute thromboembolic events can be the first manifestation of an occult malignancy, we will miss patients with thromboembolic events who are subsequently diagnosed with cancer.

12. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE

REACTIONS

Adverse events/adverse reactions will not be collected or analysed as part of this evaluation. The nature of

this non-interventional evaluation, through the use of secondary data, does not fulfil the criteria for

reporting adverse events, according to module VI, VI.C.1.2.1.2 of the Good Pharmacovigilance Practices

(https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-good-

pharmacovigilance-practices-gvp-module-vi-collection-management-submission-reports_en.pdf).

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13. GOVERNANCE BOARD ASPECTS

All data sources require approval from their respective IRB boards, with the exception of IQVIA DA

Germany and IQVIA LPD Belgium which will not require any further specific approvals to undertake this

study. 

14. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY

RESULTS

A PDF report, including an executive summary and the specified tables and/or figures, will be submitted to EMA by the DARWIN EU® CC upon completion of the study.

An interactive dashboard incorporating all the results (tables and figures) will be provided alongside the PDF report. If requested, the full set of underlying aggregated data used in the dashboard will also be made available.

15. OTHER ASPECTS

None.

16. REFERENCES

Timp, J. F., Braekkan, S. K., Versteeg, H. H., & Cannegieter, S. C. (2013). Epidemiology of cancer-associated

venous thrombosis. Blood, The Journal of the American Society of Hematology, 122(10), 1712-1723.

Blom, J. W., Doggen, C. J., Osanto, S., & Rosendaal, F. R. (2005). Malignancies, prothrombotic mutations,

and the risk of venous thrombosis. Jama, 293(6), 715-722.

Björklund, J., Rautiola, J., Zelic, R., Edgren, G., Bottai, M., Nilsson, M., ... & Akre, O. (2024). Risk of Venous

Thromboembolic Events After Surgery for Cancer. JAMA Network Open, 7(2), e2354352-e2354352.

Khorana, A. A., Dalal, M., Lin, J., & Connolly, G. C. (2013). Incidence and predictors of venous

thromboembolism (VTE) among ambulatory high‐risk cancer patients undergoing chemotherapy in the

United States. Cancer, 119(3), 648-655.

Nalluri, S. R., Chu, D., Keresztes, R., Zhu, X., & Wu, S. (2008). Risk of venous thromboembolism with the

angiogenesis inhibitor bevacizumab in cancer patients: a meta-analysis. Jama, 300(19), 2277-2285.

Ohashi, Y., Ikeda, M., Kunitoh, H., Sasako, M., Okusaka, T., Mukai, H., ... & Sakon, M. (2020). Venous

thromboembolism in cancer patients: report of baseline data from the multicentre, prospective Cancer-VTE

Registry. Japanese journal of clinical oncology, 50(11), 1246-1253.

Petterson, T. M., Marks, R. S., Ashrani, A. A., Bailey, K. R., & Heit, J. A. (2015). Risk of site-specific cancer in

incident venous thromboembolism: a population-based study. Thrombosis research, 135(3), 472-478.

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van Hylckama Vlieg, M. A., Nasserinejad, K., Visser, C., Bramer, W. M., Ashrani, A. A., Bosson, J. L., ... &

Geijteman, E. C. (2023). The risk of recurrent venous thromboembolism after discontinuation of

anticoagulant therapy in patients with cancer-associated thrombosis: a systematic review and meta-

analysis. EClinicalMedicine, 64.

White, R. H., Chew, H. K., Zhou, H., Parikh-Patel, A., Harris, D., Harvey, D., & Wun, T. (2005). Incidence of

venous thromboembolism in the year before the diagnosis of cancer in 528 693 adults. Archives of internal

medicine, 165(15), 1782-1787.

Wang, T. F., Li, A., & Garcia, D. (2018). Managing thrombosis in cancer patients. Research and Practice in

Thrombosis and Haemostasis, 2(3), 429-438.

Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data

Resource Profile: Clinical Practice Research Datalink (CPRD). International journal of epidemiology, 44(3),

827–836.

de Ridder, M. A. J., de Wilde, M., de Ben, C., Leyba, A. R., Mosseveld, B. M. T., Verhamme, K. M. C., van der

Lei, J., & Rijnbeek, P. R. (2022). Data Resource Profile: The Integrated Primary Care Information (IPCI)

database, The Netherlands. International journal of epidemiology, 51(6), e314–e323.

Leinonen, M. K., Miettinen, J., Heikkinen, S., Pitkäniemi, J., & Malila, N. (2017). Quality measures of the

population-based Finnish Cancer Registry indicate sound data quality for solid malignant

tumours. European journal of cancer (Oxford, England: 1990), 77, 31–39.

Vikkula, J., Uusi-Rauva, K., Ranki, T., Toppila, I., Aalto-Setälä, M., Pousar, K., Vassilev, L., Porkka, K.,

Silvennoinen, R., & Brück, O. (2023). Real-world evidence of multiple myeloma treated from 2013 to 2019

in the Hospital District of Helsinki and Uusimaa, Finland. Future oncology (London, England), 19(30), 2029–

2043.

Sund R. (2012). Quality of the Finnish Hospital Discharge Register: a systematic review. Scandinavian journal

of public health, 40(6), 505–515.

Recalde, M., Davila-Batista, V., Díaz, Y., Leitzmann, M., Romieu, I., Freisling, H., & Duarte-Salles, T. (2021).

Body mass index and waist circumference concerning the risk of 26 types of cancer: a prospective cohort

study of 3.5 million adults in Spain. BMC medicine, 19(1), 10.

Ostropolets A, Reich C, Ryan P, et al. Characterizing database granularity using SNOMED-CT

hierarchy. AMIA Annu Symp Proc. 2021;2020:983-992. Published 2021 Jan 25.

Campbell, W. S., Campbell, J. R., West, W. W., McClay, J. C., & Hinrichs, S. H. (2014). Semantic analysis of

SNOMED CT for a post-coordinated database of histopathology findings. Journal of the American Medical

Informatics Association : JAMIA, 21(5), 885–892.

17. ANNEXES

Appendix I

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Table A1 Preliminary code list for deep vein thrombosis (DVT)

Concept Id SNOMED Code Concept Name

762047 286411000119108 Acute bilateral thrombosis of subclavian veins

762148 293471000119106 Acute deep vein thrombosis of bilateral iliac veins

761444 15711361000119101 Acute deep vein thrombosis of bilateral lower limbs following coronary artery bypass graft

35616028 293491000119107 Acute deep vein thrombosis of left iliac vein

35615035 15711401000119105 Acute deep vein thrombosis of left lower limb following procedure

761416 15708441000119103 Acute deep vein thrombosis of left upper limb following coronary artery bypass graft

35615031 15708401000119100 Acute deep vein thrombosis of left upper limb following procedure

43531681 651000119108 Acute deep vein thrombosis of lower limb

35616027 293481000119109 Acute deep vein thrombosis of right iliac vein

35615034 15711241000119106 Acute deep vein thrombosis of right lower limb following procedure

761415 15708281000119109 Acute deep vein thrombosis of right upper limb following coronary artery bypass graft

35615030 15708201000119101 Acute deep vein thrombosis of right upper limb following procedure

44782746 132281000119108 Acute deep venous thrombosis

44782751 134961000119104 Acute deep venous thrombosis of axillary vein

762008 285321000119103 Acute deep venous thrombosis of bilateral axillary veins

760875 12237551000119104 Acute deep venous thrombosis of bilateral calves

765155 285441000119102 Acute deep venous thrombosis of bilateral ileofemoral veins

762017 285501000119103 Acute deep venous thrombosis of bilateral internal jugular veins

762417 350291000119100 Acute deep venous thrombosis of bilateral legs

762020 285561000119102 Acute deep venous thrombosis of bilateral popliteal veins

765546 285621000119106 Acute deep venous thrombosis of bilateral tibial veins

762004 285261000119104 Acute deep venous thrombosis of both upper extremities

44782742 132241000119103 Acute deep venous thrombosis of calf

44782747 132291000119106 Acute deep venous thrombosis of femoral vein

762015 285451000119100 Acute deep venous thrombosis of ileofemoral vein of left leg

765541 285461000119103 Acute deep venous thrombosis of ileofemoral vein of right lower extremity

44782748 132301000119107 Acute deep venous thrombosis of iliofemoral vein

44782752 135001000119100 Acute deep venous thrombosis of internal jugular vein

762009 285331000119100 Acute deep venous thrombosis of left axillary vein

760876 12237631000119109 Acute deep venous thrombosis of left calf

765540 285391000119101 Acute deep venous thrombosis of left femoral vein

765922 285511000119100 Acute deep venous thrombosis of left internal jugular vein

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762418 350301000119104 Acute deep venous thrombosis of left lower extremity

765537 285271000119105 Acute deep venous thrombosis of left upper extremity

44782767 136781000119101 Acute deep venous thrombosis of lower extremity as complication of procedure

46270071 132111000119107 Acute deep venous thrombosis of lower limb due to and following coronary artery bypass

grafting

762022 285581000119106 Acute deep venous thrombosis of politeal vein of right leg

44782743 132251000119101 Acute deep venous thrombosis of popliteal vein

762021 285571000119108 Acute deep venous thrombosis of popliteal vein of left leg

762010 285341000119109 Acute deep venous thrombosis of right axillary vein

760877 12237711000119106 Acute deep venous thrombosis of right calf

762013 285401000119104 Acute deep venous thrombosis of right femoral vein

762018 285521000119107 Acute deep venous thrombosis of right internal jugular vein

762419 350311000119101 Acute deep venous thrombosis of right lower extremity

762005 285281000119108 Acute deep venous thrombosis of right upper extremity

44782745 132271000119105 Acute deep venous thrombosis of thigh

44782744 132261000119104 Acute deep venous thrombosis of tibial vein

762026 285631000119109 Acute deep venous thrombosis of tibial vein of left leg

765156 285641000119100 Acute deep venous thrombosis of tibial vein of right leg

44782421 132321000119103 Acute deep venous thrombosis of upper extremity

764016 449691000124103 Acute deep venous thrombosis of upper extremity after coronary artery bypass graft

44782766 136771000119104 Acute deep venous thrombosis of upper extremity as complication of procedure

762048 286421000119101 Acute thrombosis of left subclavian vein

45757410 133421000119101 Acute thrombosis of mesenteric vein

762049 286431000119103 Acute thrombosis of right subclavian vein

36712892 143561000119108 Acute thrombosis of splenic vein

44782762 132611000119104 Acute thrombosis of subclavian vein

435887 49956009 Antepartum deep vein thrombosis

4179911 297156001 Axillary vein thrombosis

37109253 285381000119104 Bilateral acute deep vein thrombosis of femoral veins

40478951 444325005 Bilateral deep vein thrombosis of lower extremities

4042396 16750002 Deep thrombophlebitis

4046884 134399007 Deep vein thrombosis of leg related to air travel

3655221 860699005 Deep vein thrombosis of lower extremity due to intravenous drug use

4133004 128053003 Deep venous thrombosis

4181315 428781001 Deep venous thrombosis associated with coronary artery bypass graft

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438820 56272000 Deep venous thrombosis in puerperium

45773536 703277001 Deep venous thrombosis of femoropopliteal vein

763942 448841000124100 Deep venous thrombosis of left lower extremity

761980 25820001000004100 Deep venous thrombosis of left upper extremity

443537 404223003 Deep venous thrombosis of lower extremity

4133975 128055005 Deep venous thrombosis of pelvic vein

40480555 443210003 Deep venous thrombosis of peroneal vein

4322565 427775006 Deep venous thrombosis of profunda femoris vein

763941 448831000124105 Deep venous thrombosis of right lower extremity

761928 20850001000004108 Deep venous thrombosis of right upper extremity

4207899 438785004 Deep venous thrombosis of tibial vein

4028057 128054009 Deep venous thrombosis of upper extremity

435565 195437003 Embolism and thrombosis of the vena cava

40481089 444816006 Embolism from thrombosis of vein of lower extremity

4119760 234044007 Iliofemoral deep vein thrombosis

4124856 234041004 Inferior mesenteric vein thrombosis

4096099 25114006 Phlebitis of deep veins of lower extremity

4281689 66923004 Phlegmasia alba dolens

4284538 66877004 Phlegmasia cerulea dolens

4309333 213220000 Postoperative deep vein thrombosis

46285905 978441000000108 Provoked deep vein thrombosis

46271900 710167004 Recurrent deep vein thrombosis

4033521 14534009 Splenic vein thrombosis

4055089 197001004 Superior mesenteric vein thrombosis

4230403 438646004 Thrombophlebitis of axillary vein

4069561 1748006 Thrombophlebitis of deep femoral vein

761831 16014391000119106 Thrombophlebitis of deep vein of bilateral lower limbs

761830 16014351000119101 Thrombophlebitis of deep vein of left lower limb

761808 16006271000119105 Thrombophlebitis of deep vein of left upper limb

761832 16014431000119101 Thrombophlebitis of deep vein of right lower limb

761809 16006311000119105 Thrombophlebitis of deep vein of right upper limb

4221821 40198004 Thrombophlebitis of deep veins of lower extremity

440750 95452006 Thrombophlebitis of deep veins of upper extremities

4176614 42861008 Thrombophlebitis of iliac vein

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761821 16012151000119109 Thrombophlebitis of left deep femoral vein

761819 16012071000119101 Thrombophlebitis of left femoral vein

761820 16012111000119108 Thrombophlebitis of right deep femoral vein

761818 16011991000119109 Thrombophlebitis of right femoral vein

4110339 195412008 Thrombophlebitis of the anterior tibial vein

4111868 195425000 Thrombophlebitis of the common iliac vein

4110343 195427008 Thrombophlebitis of the external iliac vein

439314 195410000 Thrombophlebitis of the femoral vein

4109877 195426004 Thrombophlebitis of the internal iliac vein

4112171 195411001 Thrombophlebitis of the popliteal vein

4112172 195414009 Thrombophlebitis of the posterior tibial vein

4250765 7387004 Thrombophlebitis of tibial vein

42538533 762256003 Thrombosis of iliac vein

44811347 864191000000104 Thrombosis of internal jugular vein

765049 16730001000004104 Thrombosis of left peroneal vein

4317289 95446005 Thrombosis of mesenteric vein

4203836 438647008 Thrombosis of subclavian vein

4175649 427776007 Thrombosis of the popliteal vein

4149782 309735004 Thrombosis of vein of lower limb

4153353 371051005 Traumatic thrombosis of axillary vein

46285904 978421000000101 Unprovoked deep vein thrombosis

77310 266267005 Deep vein phlebitis and thrombophlebitis of the leg

4189004 413956008 Deep vein thrombosis of leg related to intravenous drug use

Table A2 Preliminary code list for pulmonary embolism (PE)

Concept Id SNOMED code Concept name

608954 15964661000119102 Acute cor pulmonale due to septic pulmonary embolism

4120091 233936003 Acute massive pulmonary embolism

45768439 706870000 Acute pulmonary embolism

45768888 707414004 Acute pulmonary thromboembolism

44782732 133971000119108 Chronic pulmonary embolism

45768887 707412000 Chronic pulmonary thromboembolism

45771016 707413005 Chronic pulmonary thromboembolism without pulmonary hypertension

4219469 82153002 Miscarriage with pulmonary embolism

4108681 194883006 Postoperative pulmonary embolus

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4091708 280964006 Pulmonary air embolism

440417 59282003 Pulmonary embolism

37109911 723859005 Pulmonary embolism due to and following acute myocardial infarction

45757145 10759311000119104 Pulmonary embolism in childbirth

37016922 713078005 Pulmonary embolism on long-term anticoagulation therapy

43530605 1001000119102 Pulmonary embolism with pulmonary infarction

4119608 233938002 Pulmonary fat embolism

4253796 74315008 Pulmonary microemboli

45766471 703636009 Pulmonary oil microembolism

4121618 233935004 Pulmonary thromboembolism

4119610 233940007 Pulmonary tumor embolism

4236271 438773007 Recurrent pulmonary embolism

36713113 328511000119109 Saddle embolus of pulmonary artery

35615055 15964701000119109 Saddle embolus of pulmonary artery with acute cor pulmonale

40479606 441557008 Septic pulmonary embolism

4119607 233937007 Subacute massive pulmonary embolism

4119609 233939005 Subacute pulmonary fat embolism

Table A3 Preliminary code list for pelvic venous thrombosis (PVT)

Concept Id SNOMED Code Concept Name

762148 293471000119106 Acute deep vein thrombosis of bilateral iliac veins

35616028 293491000119107 Acute deep vein thrombosis of left iliac vein

35616027 293481000119109 Acute deep vein thrombosis of right iliac vein

765155 285441000119102 Acute deep venous thrombosis of bilateral ileofemoral veins

761461 15712201000119101 Acute deep venous thrombosis of bilateral pelvic veins

762015 285451000119100 Acute deep venous thrombosis of ileofemoral vein of left leg

765541 285461000119103 Acute deep venous thrombosis of ileofemoral vein of right lower extremity

44782748 132301000119107 Acute deep venous thrombosis of iliofemoral vein

761462 15712241000119104 Acute deep venous thrombosis of left pelvic vein

44782761 132601000119102 Acute deep venous thrombosis of pelvic vein

765229 15712281000119109 Acute deep venous thrombosis of right pelvic vein

608965 15968901000119104 Bilateral iliac vein thrombophlebitis

765152 293441000119104 Chronic deep vein thrombosis of bilateral iliac veins

35616026 293461000119100 Chronic deep vein thrombosis of left iliac vein

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761439 15711001000119104 Chronic deep vein thrombosis of left pelvic vein

46271548 709687000 Chronic deep vein thrombosis of pelvic vein

35616025 293451000119102 Chronic deep vein thrombosis of right iliac vein

761441 15711081000119107 Chronic deep vein thrombosis of right pelvic vein

765542 285471000119109 Chronic deep venous thrombosis of bilateral ileofemoral veins

761440 15711041000119102 Chronic deep venous thrombosis of bilateral pelvic veins

44782740 132201000119100 Chronic deep venous thrombosis of iliofemoral vein

765543 285481000119107 Chronic deep venous thrombosis of left ileofemoral vein

762016 285491000119105 Chronic deep venous thrombosis of right ileofemoral vein

761013 132541000119101 Deep venous thrombosis of bilateral pelvic veins

4133975 128055005 Deep venous thrombosis of pelvic vein

4119760 234044007 Iliofemoral deep vein thrombosis

608964 15968861000119105 Left iliac vein thrombophlebitis

4158798 361278002 Mondor's phlebitis of the penis

4285751 67486009 Pelvic thrombophlebitis in puerperium

608963 15968821000119100 Right iliac vein thrombophlebitis

4176614 42861008 Thrombophlebitis of iliac vein

4317290 95449003 Thrombophlebitis of pelvic vein

4111868 195425000 Thrombophlebitis of the common iliac vein

4110343 195427008 Thrombophlebitis of the external iliac vein

4109877 195426004 Thrombophlebitis of the internal iliac vein

201045 26373009 Thrombosed external hemorrhoids

195294 75955007 Thrombosed hemorrhoids

608966 15969021000119101 Thrombosed internal hemorrhoid grade IV

201595 52931009 Thrombosed internal hemorrhoids

42538533 762256003 Thrombosis of iliac vein

606527 1145183007 Thrombosis of pampiniform plexus

4319327 95448006 Thrombosis of pelvic vein

4295878 76598006 Thrombosis of penile vein

762443 368351000119106 Thrombosis of superficial vein of penis

Table A4 Preliminary code list for splanchic vein thrombosis, including hepatic and extrahepatic (STV)

Concept Id SNOMED Code Concept Name

45757410 133421000119101 Acute thrombosis of mesenteric vein

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36712892 143561000119108 Acute thrombosis of splenic vein

196715 82385007 Budd-Chiari syndrome

45757409 133411000119108 Chronic thrombosis of mesenteric vein

36712891 143551000119106 Chronic thrombosis of splenic vein

4301208 38739001 Hepatic vein thrombosis

4124856 234041004 Inferior mesenteric vein thrombosis

199837 17920008 Portal vein thrombosis

4033521 14534009 Splenic vein thrombosis

4055089 197001004 Superior mesenteric vein thrombosis

4318407 95447001 Thrombophlebitis of mesenteric vein

4317289 95446005 Thrombosis of mesenteric vein

Table A5 Preliminary code list for retinal vein thrombosis, including retinal central vein thrombosis (RVT)

Concept Id Concept Code Concept Name

4339013 232048009 Branch retinal vein occlusion with macular edema

4334248 232046008 Branch retinal vein occlusion with neovascularization

4199035 314000002 Branch retinal vein occlusion with no neovascularization

313761 68478007 Central retinal vein occlusion

4208221 312997008 Central retinal vein occlusion - ischemic

4339011 232040002 Central retinal vein occlusion - juvenile

4335591 232042005 Central retinal vein occlusion - juvenile with macular edema

4334888 232041003 Central retinal vein occlusion - juvenile with neovascularization

4208222 312998003 Central retinal vein occlusion - non-ischemic

4339010 232039004 Central retinal vein occlusion with macular edema

4334246 232038007 Central retinal vein occlusion with neovascularization

42535735 733325006 Combined occlusion by thrombus of retinal artery and retinal vein

4334247 232043000 Hemispheric retinal vein occlusion

4335592 232045007 Hemispheric retinal vein occlusion with macular edema

4336005 232044006 Hemispheric retinal vein occlusion with neovascularization

4216561 71719003 Thrombophlebitis of retinal vein

4187790 46085004 Thrombosis of retinal vein

312622 24596005 Venous retinal branch occlusion

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Table A6 Preliminary code list for disseminated intravascular coagulation (DIC)

Concept Id Concept Code Concept Name

436093 67406007 Disseminated intravascular coagulation

4028488 13507004 Purpura fulminans

Appendix II

Preliminary code list for selected cancer types is attached as a standalone document

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Appendix III: ENCePP checklist for study protocols

ENCePP Checklist for Study Protocols (Revision 4)

Study title: DARWIN EU® – Incidence rates of venous thromboembolic events in cancer patients

EU PAS Register® number: N/A

Study reference number (if applicable): P3-C3-005

Section 1: Milestones  Yes  No  N/A  Section

Number  1. Does the protocol specify

timelines for  

5. Milestones,  

8.2 Data

Sources 

1.1.1 Start of data collection       

1.1.2 End of data collection  X     

1.1.3 Progress report(s)       

1.1.4 Interim report(s)       

1.1.5 Registration in the EU PAS Register®       

1.1.6 Final report of study results.       

Comments: 

Section 2: Research question  Yes  No  N/A  Section

Number  2.1 Does the formulation of the research question and

objectives clearly explain:        

7. Research

question and

objectives  8. Research

methods

2.1.1 Why the study is conducted? (e.g. to address an

important public health concern, a risk identified in the

risk management plan, an emerging safety issue)  X     

2.1.2 The objective(s) of the study?       

2.1.3 The target population? (i.e. population or subgroup

to whom the study results are intended to be

generalised)       

2.1.4 Which hypothesis(-es) is (are) to be tested?       

2.1.5 If applicable, that there is no a priori hypothesis?       

Comments: 

Section 3: Study design  Yes  No  N/A  Section

Number  3.1 Is the study design described? (e.g. cohort, case-control,

cross-sectional, other design)   X      8.1 Study type

and Study

Design 

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3.2 Does the protocol specify whether the study is based on

primary, secondary or combined data collection?   X     8.2 Study

Setting and

Data Sources  3.3 Does the protocol specify measures of occurrence?

(e.g., rate, risk, prevalence)   X     8.8 Analysis 

3.4 Does the protocol specify measure(s) of association?

(e.g. risk, odds ratio, excess risk, rate ratio, hazard ratio,

risk/rate difference, number needed to harm (NNH))   X     8.8 Analysis 

3.5 Does the protocol describe the approach for the collection

and reporting of adverse events/adverse reactions?

(e.g. adverse events that will not be collected in case of

primary data collection) 

    X   

Comments: 

Section 4: Source and study populations  Yes  No  N/A  Section Number 

4.1 Is the source population described?  X     

8.5 Study

Population  4.2 Is the planned study population defined in terms

of:         

4.2.1 Study time period        8.3 Study Period 

4.2.2 Age and sex   X    

8.6.3. Other

covariates  4.2.3 Country of origin 

      8.2 Study Setting

and Data Sources  4.2.4 Disease/indication        8.6.1. Exposures 

4.2.5 Duration of follow-up        8.4 Follow-up 

4.3 Does the protocol define how the study

population will be sampled from the source

population? (e.g. event or inclusion/exclusion

criteria) 

 X    

8.5 Study

Population with

inclusion and

exclusion criteria  Comments: 

Section 5: Exposure definition and measurement  Yes  No  N/A  Section

Number  5.1 Does the protocol describe how the study exposure is

defined and measured? (e.g. operational details for defining and

categorising exposure, measurement of dose and duration of

drug exposure) 

     X

5.2 Does the protocol address the validity of the exposure

measurement? (e.g. precision, accuracy, use of validation sub-

study)       X  

5.3 Is exposure categorised according to time windows?         X

5.4 Is intensity of exposure addressed?   (e.g. dose, duration) 

      X

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5.5 Is exposure categorised based on biological mechanism of

action and taking into account the pharmacokinetics and

pharmacodynamics of the drug?       X  

5.6 Is (are) (an) appropriate comparator(s) identified?        X

Comments: 

Section 6: Outcome definition and measurement  Yes  No  N/A  Section

Number  6.1 Does the protocol specify the primary and secondary (if

applicable) outcome(s) to be investigated?    X     8.6.2.

Outcomes 6.2 Does the protocol describe how the outcomes are defined

and measured?     X     8.6.2.

Outcomes 6.3 Does the protocol address the validity of outcome

measurement? (e.g. precision, accuracy, sensitivity, specificity,

positive predictive value, use of validation sub-study)      X    

6.4 Does the protocol describe specific outcomes relevant for

Health Technology Assessment? (e.g. HRQoL, QALYs, DALYS,

health care services utilisation, burden of disease or treatment,

compliance, disease management) 

    X    

Comments: 

Section 7: Bias  Yes  No  N/A  Section

Number  7.1 Does the protocol address ways to measure confounding?

(e.g. confounding by indication)       X

7.2 Does the protocol address selection bias? (e.g. healthy

user/adherer bias)       X

7.3 Does the protocol address information bias?

(e.g. misclassification of exposure and outcomes, time-related

bias)       X

Comments: 

Section 8: Effect measure modification  Yes  No  N/A  Section

Number  8.1 Does the protocol address effect modifiers?

(e.g. collection of data on known effect modifiers, sub-group

analyses, anticipated direction of effect)        X

Comments: 

Section 9: Data sources  Yes  No  N/A  Section

Number  9.1 Does the protocol describe the data source(s) used in the

study for the ascertainment of:         

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9.1.1 Exposure? (e.g. pharmacy dispensing, general practice

prescribing, claims data, self-report, face-to-face interview)        X

9.1.2 Outcomes? (e.g. clinical records, laboratory markers or

values, claims data, self-report, patient interview including scales

and questionnaires, vital statistics)    X    

8.2 Study

Setting and

Data Sources  9.1.3 Covariates and other characteristics? 

 X     8.6.3. Other

covariates  9.2 Does the protocol describe the information available from

the data source(s) on:         

9.2.1 Exposure? (e.g. date of dispensing, drug quantity, dose,

number of days of supply prescription, daily dosage,  prescriber)        X

9.2.2 Outcomes? (e.g. date of occurrence, multiple event,

severity measures related to event)       X

9.2.3 Covariates and other characteristics? (e.g. age, sex, clinical

and drug use history, co-morbidity, co-medications, lifestyle)   X     8.2 Study

Setting and

Data Sources  9.3 Is a coding system described for:          

9.3.1 Exposure? (e.g. WHO Drug Dictionary, Anatomical

Therapeutic Chemical (ATC) Classification System)        X

9.3.2 Outcomes? (e.g. International Classification of Diseases

(ICD), Medical Dictionary for Regulatory Activities (MedDRA))    X     8.6.2.

Outcomes 9.3.3 Covariates and other characteristics? 

 X     8.6.3. Other

covariates  9.4 Is a linkage method between data sources described?

(e.g. based on a unique identifier or other)        X  

Comments: 

Section 10: Analysis plan  Yes  No  N/A  Section

Number  10.1 Are the statistical methods and the reason for their choice

described?    X     8.8 Analysis 

10.2 Is study size and/or statistical precision estimated?       X  

10.3 Are descriptive analyses included?   X    

8.8.2

Descriptive

statistics  10.4 Are stratified analyses included?   X     8.8 Analysis 

10.5 Does the plan describe methods for analytic control of

confounding?       X

10.6 Does the plan describe methods for analytic control of

outcome misclassification?       X

10.7 Does the plan describe methods for handling missing

data?       X  

10.8 Are relevant sensitivity analyses described?       X

Comments: 

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Section 11: Data management and quality control  Yes  No  N/A  Section

Number  11.1 Does the protocol provide information on data storage?

(e.g. software and IT environment, database maintenance and

anti-fraud protection, archiving)   X    

9. Data

management 

11.2 Are methods of quality assurance described?   X    

10. Quality

Control  11.3 Is there a system in place for independent review of

study results?        X  

Comments: 

Section 12: Limitations  Yes  No  N/A  Section 

Number  12.1 Does the protocol discuss the impact on the study

results of:       

11. Limitations of

the research

methods 

12.1.1 Selection bias?       

12.1.2 Information bias?   X    

12.1.3 Residual/unmeasured confounding?  (e.g. anticipated direction and magnitude of such biases,

validation sub-study, use of validation and external data,

analytical methods). 

12.2 Does the protocol discuss study feasibility? (e.g. study

size, anticipated exposure uptake, duration of follow-up in a

cohort study, patient recruitment, precision of the estimates)   X    

Table

8.2. Description of

the selected Data

Sources. Comments: 

Section 13: Ethical/data protection issues  Yes  No  N/A  Section 

Number  13.1 Have requirements of Ethics Committee/ Institutional

Review Board been described?   X    

13. Governance

board aspects  13.2 Has any outcome of an ethical review procedure been

addressed?       X

13.3 Have data protection requirements been described?   X    

9.2 Data storage

and protection  Comments: 

Section 14: Amendments and deviations  Yes  No  N/A  Section

Number  14.1 Does the protocol include a section to document

amendments and deviations?    X     4.

Amendments

and updates  Comments: 

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Section 15: Plans for communication of study results  Yes  No  N/A  Section

Number  15.1 Are plans described for communicating study results

(e.g. to regulatory authorities)?    X    

14. Plans for

disseminating

and

communicating

study results  15.2 Are plans described for disseminating study results

externally, including publication?   X    

14. Plans for

disseminating

and

communicating

study results  Comments: 

Name of the main author of the protocol: Talita Duarte-Salles & Anton Barchuk

Date: 30/10/2024

Signature:

DARWIN EU® Coordination Centre 1/48

Study Protocol P3-C3-005

DARWIN EU® – Incidence rates of venous

thromboembolic events in cancer

patients

30/10/2024

Version 1.0

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Table of Contents

LIST OF ABBREVIATIONS ......................................................................................................................... 4

1. TITLE .............................................................................................................................................. 5

2. RESPONSIBLE PARTIES – STUDY TEAM ............................................................................................. 5

3. ABSTRACT ...................................................................................................................................... 7

4. AMENDMENTS AND UPDATES ......................................................................................................... 8

5. MILESTONES ................................................................................................................................... 9

6. RATIONALE AND BACKGROUND ...................................................................................................... 9

7. RESEARCH QUESTION AND OBJECTIVES ......................................................................................... 10

8. RESEARCH METHODS .................................................................................................................... 13 8.1 Study type and study design .......................................................................................................... 13 8.2 Study setting and data sources ...................................................................................................... 14 8.3 Study period ................................................................................................................................... 21 8.4 Follow-up ....................................................................................................................................... 21 8.5 Study population with inclusion and exclusion criteria ................................................................. 22 8.6 Variables ......................................................................................................................................... 25 8.7 Study size........................................................................................................................................ 28 8.8 Analysis ........................................................................................................................................... 28 8.9 Evidence synthesis ......................................................................................................................... 30

9. DATA MANAGEMENT ................................................................................................................... 30 9.1 Data management ................................................................................................................................. 30 9.2 Data storage and protection ................................................................................................................. 30

10. QUALITY CONTROL ................................................................................................................... 31

11. LIMITATIONS OF THE RESEARCH METHODS............................................................................... 31

12. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE REACTIONS ........................... 32

13. GOVERNANCE BOARD ASPECTS ................................................................................................ 32

14. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS ....................................... 32

15. OTHER ASPECTS ....................................................................................................................... 32

16. REFERENCES ............................................................................................................................. 33

17. ANNEXES ................................................................................................................................. 34

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Study Title DARWIN EU® – Incidence rates of venous thromboembolic events in

cancer patients

Protocol version 1.0

Date 30/10/2024

EU PAS number Study not yet registered

Active substance None

Medicinal product None

Research question

and objectives

This study aims to estimate incidence rates of venous thromboembolic

events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous

thromboembolism (VTE, composite of DVT and PE), pelvic venous

thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and

extra-hepatic vein thrombosis, retinal vein thrombosis (RVT), including

retinal central vein thrombosis, and disseminated intravascular

coagulation (DIC) in adult patients (aged 18 and above) newly diagnosed

with selected cancers in 2016-2023 (lung, breast, ovary, corpus uteri,

prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone,

kidney, melanoma, lymphoma and leukaemia) and to describe their

characteristics at the time of cancer diagnosis.

The specific objectives of the study are:

1. To estimate the incidence rates of thromboembolic events in

patients newly diagnosed with each type of selected cancers

stratified by country/database, age group, sex, study sub-period,

and cancer stage (when available) one and two years after cancer

diagnosis.

2. To characterise cancer patients at the time of cancer diagnosis in

terms of demographics, comorbidities, concomitant medications,

as well as treatments received in the first 90 days after cancer

diagnosis.

Country(ies) of study Belgium, Denmark, Estonia, Finland, Germany, The Netherlands, Spain,

United Kingdom

Author Anton Barchuk ([email protected])

Talita Duarte-Salles ([email protected])

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LIST OF ABBREVIATIONS

Abbreviation Name

ATC Anatomical Therapeutic Chemical

CDM Common Data Model

CPRD Clinical Practice Research Datalink

DARWIN EU® Data Analysis and Real World Interrogation Network

DK-DHR Danish Data Health Registries

DOI Declaration Of Interests

DQD Data Quality Dashboard

DRE Digital Research Environment

DVT Deep Venous Thrombosis

DIC Disseminated Intravascular Coagulation

GP General Practitioner

EHR Electronic Health Record

EMA European Medicines Agency

EBB Estonian Biobank

ECOG Eastern Cooperative Oncology Group

EGCUT Estonian Genome Center at the University of Tartu

ENCePP European Network of Centres for Pharmacoepidemiology and

Pharmacovigilance

ICD-O-3 International Classification of Diseases for Oncology, 3rd Edition

ICD-10 International Classification of Diseases, 10th revision

ICPC-1 International Classification of Primary Care

IPCI Integrated Primary Care Information Project

LPD Longitudinal Patient Database

OHDSI Observational Health Data Sciences and Informatics

OMOP Observational Medical Outcomes Partnership

PE Pulmonary Embolism

PVT Pelvic Venous Thrombosis

RVT Retinal vein thrombosis

SNOMED Systematized Nomenclature of Medicine

SVT Splanchic Vein Thrombosis

UKBB UK Biobank

VTE Venous Thromboembolism

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1. TITLE

DARWIN EU® – Incidence rates of venous thromboembolic events in cancer patients

2. RESPONSIBLE PARTIES – STUDY TEAM

Study team role Names Organisation

Study Project Manager /

Principal Investigator

Talita Duarte-Salles

Anton Barchuk

Erasmus MC

Data Scientist Cesar Barboza

Ger Inberg

Maarten van Kessel

Adam Black

Ross Williams

Erasmus MC

Epidemiologist Berta Raventós

Julieta Politi

Erasmus MC

Clinical Domain Expert Anton Barchuk Erasmus MC

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Data Partner* Names Organisation

CPRD GOLD and UKBB Antonella Delmestri University of Oxford

DK-DHR Claus Møldrup

Elvira Bräuner

Susanne Bruun

Tine Iskov Kopp

Cæcilie Brinth Christiansen

Danish Medicines Agency

EBB Marek Oja

Raivo Kolde Estonian Biobank, Estonia

FinOMOP-HILMO Anna Hammais

Gustav Klingstedt

Finnish Care Register for Health

Care, Finland

FinOMOP-HUS Eric Fey

Kimmo Porkka

Tiina Wahlfors

Hospital District of Helsinki and

Uusimaa, Finland

IQVIA DA Germany and IQVIA LPD

Belgium

Gargi Jadhav

Isabella Kacmarczyl

Akram Mendez

Hanne van Ballegooijen

Dina Vojinovic

IQVIA

IPCI Katia Verhamme Integrated Primary Care

Information, Netherlands

SIDIAP Anna Palomar-Cros

Irene López-Sánchez

Agustina Giuliodori

IDIAPJGol

*Data partners’ role is only to execute code at their data source, review and approve their results. These people do not have an

investigator role.

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3. ABSTRACT

Title

DARWIN EU® – Incidence rates of venous thromboembolic events in cancer patients

Rationale

Cancer-associated venous thrombosis is relatively common: from 20% to 30% of all primary venous thromboembolic events are cancer-associated. While cancer patients have an increased risk of developing venous thromboembolism (VTE) compared to individuals without underlying malignancies, it is also recognised as one of the major causes of death in cancer patients. Still, the reported incidence varies across different populations and cancer types and can also be attributed to variations in patient characteristics, management options, and the cancer stage at diagnosis. The incidence of VTE was found to be higher in cases of renal cell, ovarian, pancreatic, stomach, and lung cancers, as well as acute myelogenous leukaemia and non-Hodgkin lymphoma during the four months immediately preceding the cancer diagnosis. When investigating a safety signal, reliable information on background risk is crucial to assess potential associations with oncological treatments.

Research Objectives

This study aims to estimate incidence rates of venous thromboembolic events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis, retinal vein thrombosis (RVT), including retinal central vein thrombosis, and disseminated intravascular coagulation (DIC) in adult patients (aged 18 and above) newly diagnosed with selected cancers in 2016- 2023 (lung, breast, ovary, corpus uteri, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia) and to describe their characteristics at the time of cancer diagnosis.

The specific objectives of the study are:

1. To estimate the incidence rates of thromboembolic events in patients newly diagnosed with each type of selected cancers stratified by country/database, age group, sex, study sub-period, and cancer stage (when available) one and two years after cancer diagnosis.

2. To characterise cancer patients at the time of cancer diagnosis in terms of demographics, comorbidities, concomitant medications, as well as treatments received in the first 90 days after cancer diagnosis.

Research Methods

Study design

Population-based cohort study.

Population

The study population will include all individuals aged 18 years and above with a primary diagnosis of one of the selected cancers (lung, breast, ovary, corpus uteri, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia) in the study period from 01/01/2016 to 31/12/2023. Only patients with the first and one cancer diagnosis (except non-melanoma skin cancer) will be included. Cancer cases and thromboembolic events will be identified based on appropriate computable phenotyping algorithms. Conditions in the OMOP CDM use the Systematised

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Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. The International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) will also be considered for cancer diagnoses. Additional eligibility of a minimum of 1 year of potential follow-up time will be imposed to ensure sufficient time to capture potential outcomes of interest.

Variables

Outcomes will include thromboembolic events, in particular, DVT, PE, VTE (composite of DVT and PE), PVT, SVT, RVT, DIC.

Data sources

1. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom 2. Danish Data Health Registries (DK-DHR), Denmark 3. Estonian Biobank (EBB), Estonia 4. Finnish Care Register for Health Care (FinOMOP-HILMO) Finland 5. Hospital District of Helsinki and Uusimaa (FinOMOP-HUS), Finland 6. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 7. IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium), Belgium 8. Integrated Primary Care Information (IPCI), Netherlands 9. The Information System for Research in Primary Care (SIDIAP), Spain. 10. UK Biobank (UKBB), United Kingdom

Sample size

No sample size will be calculated as this is a descriptive study. However, analysis by strata will be limited to databases with enough cases (5 or more) to provide meaningful results. Summary measures of occurrence will not be calculated for strata with counts less than 5.

Data analyses

Analyses will be conducted separately for each database and carried out in a federated manner, allowing analyses to be run locally without sharing patient-level data.

The incidence of thromboembolic events (Objective 1) will be estimated over one and two years after the selected cancer diagnosis. Each cancer type and outcome will be assessed separately. Large-scale patient- level characterisation (Objective 2) will be conducted at the index date. Age and sex will be described at the time of diagnosis. The medical history and medication will be assessed at the index date.

For all analyses, absolute and relative frequencies will be reported. A minimum cell count of 5 will be used when reporting results, with any smaller counts reported as “<5” and zero counts as “0”. Overall analyses will be done separately for each database. Further stratification by age category (18-29; 30-39; 40-49; 50- 59; 60-69; 70-79; 80-89; 90 and over), sex, study sub-period, cancer stage will be conducted when possible (minimum cell count reached and data available). The following sub-periods will be also used: 2016-2019, 2020-2023.

4. AMENDMENTS AND UPDATES

None.

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5. MILESTONES

Study deliverables Timeline

Draft Study Protocol October 2024

Final Study Protocol October/November 2024 (or upon EMA acceptance)

Creation of Analytical code and Phenotyping November/December 2024

Execution of Analytical Code on the data January 2024 (depending on IRB approval dates)

Draft Study Report February 2024

Final Study Report February 2024

6. RATIONALE AND BACKGROUND

Cancer-associated venous thrombosis is relatively common: from 20% to 30% of all primary venous thromboembolic events are cancer-associated (Timp, 2013). While cancer patients have an increased risk of developing venous thromboembolism (VTE) compared to individuals without underlying malignancies (Blom, 2005), it is also recognised as one of the major causes of death in cancer patients (Wang, 2017). Still, the reported incidence varies across different populations and cancer types and can also be attributed to variations in patient characteristics, management options, and the cancer stage at diagnosis (Timp, 2013).

The association between cancer and thromboembolic events was assessed in various studies. In a large cohort of cancer patients, the incidence of thromboembolic events was found to be higher in cases of renal cell, ovarian, pancreatic, stomach, and lung cancers, as well as acute myelogenous leukaemia and non- Hodgkin lymphoma during the four months immediately preceding the cancer diagnosis (White, 2005). The prevalence of thromboembolic events at the time of diagnosis was highest for pancreatic cancer and lowest for breast cancer in another registry-based study (Ohashi, 2020). All cancer sites showed an increased prevalence in the thromboembolic events incidence cohort (Petterson, 2015). The incidence of thromboembolic events was notably high during the first few months of chemotherapy in a cohort of cancer patients followed for up to 12 months, with higher odds observed in pancreatic, gastric, and lung cancers (Khorana, 2013). Certain cancer medications have also been associated with higher risks of thromboembolic events (Nalluri, 2008; Khorana, 2013). Additionally, major surgery is known to be associated with thromboembolic events, with an increased risk that persists for 90 to 120 days post-surgery (Björklund, 2024). A high rate of recurrent thromboembolic events is observed over time following the discontinuation of anticoagulant therapy in patients with cancer-associated thrombosis (van Hylckama Vlieg, 2023).

When a safety signal of this nature appears in cancer populations, it can be challenging to assess a potential association with new treatment options without reliable information on background risk. This study

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addresses this knowledge gap by generating background incidence rates of thromboembolic events events among patients with selected cancer types.

7. RESEARCH QUESTION AND OBJECTIVES

This study aims to estimate incidence rates of venous thromboembolic events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis, retinal vein thrombosis (RVT), including retinal central vein thrombosis, and disseminated intravascular coagulation (DIC) in adult patients (aged 18 and above) newly diagnosed with selected cancers in 2016- 2023 (lung, breast, ovary, corpus uteri, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia) and to describe their characteristics at the time of cancer diagnosis.

The specific objectives of the study are:

1. To estimate the incidence rates of thromboembolic events in patients newly diagnosed with each type of selected cancers stratified by country/database, age group, sex, study sub-period, and cancer stage (when available) one and two years after cancer diagnosis.

2. To characterise cancer patients at the time of cancer diagnosis in terms of demographics, comorbidities, concomitant medications, as well as treatments received in the first 90 days after cancer diagnosis.

A description of the proposed objectives to be achieved in the study is found in Table 1.

Table 1. Primary and secondary research questions and objectives.

A. Primary research question and objective.

Objective: To estimate the incidence rates of thromboembolic events in patients

newly diagnosed with each type of selected cancers stratified by

country/database, age group, sex, study sub-period, and cancer stage

(when available) one and two years after cancer diagnosis.

Hypothesis: N/A

Population (mention key inclusion-

exclusion criteria): The study population will include all individuals aged 18 years and above

with a primary diagnosis of selected cancers in the study period from

01/01/2016 to 31/12/2023. Selected cancer types include:

• Malignant neoplasm of oesophagus (specified in the protocol as

oesophageal cancer),

• Malignant neoplasm of stomach (stomach cancer),

• Malignant neoplasm of colon, rectosigmoid junction, rectum,

anus and anal canal (colorectal cancer),

• Malignant neoplasm of liver and intrahepatic bile ducts (liver

cancer),

• Malignant neoplasm of pancreas (pancreatic cancer),

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• Malignant neoplasm of trachea, bronchus and lung (lung

cancer),

• Malignant neoplasms of bone and articular cartilage

• (bone cancer)

• Malignant melanoma of skin (skin melanoma)

• Malignant neoplasm of breast (breast cancer),

• Malignant neoplasm of corpus uteri (corpus uteri cancer

including endometrial cancer),

• Malignant neoplasm of ovary (ovarian cancer),

• Malignant neoplasm of prostate (prostate cancer),

• Malignant neoplasm of kidney, except renal pelvis (kidney

cancer),

• Malignant neoplasm of meninges, brain and spinal cord, cranial

nerves and other parts of central nervous system (brain cancer),

• Malignant neoplasms, stated or presumed to be primary, of

lymphoid, haematopoietic and related tissue (lymphoma and

leukaemia)

Only patients with the first and one cancer diagnosis (except non-

melanoma skin cancer) will be included. Additional eligibility of a

minimum of 1 year of potential follow-up time will be imposed to ensure

sufficient time to capture potential outcomes of interest.

Exposure: N/A

Comparator: N/A

Outcome: Venous thromboembolic events:

• deep vein thrombosis (DVT),

• pulmonary embolisms (PE),

• venous thromboembolism (VTE, composite of DVT and PE),

• pelvic venous thrombosis (PVT),

• splanchic vein thrombosis (SVT), including hepatic and extra-

hepatic vein thrombosis,

• retinal vein thrombosis (RVT), including retinal central vein

thrombosis

• disseminated intravascular coagulation (DIC)

Time (when follow up begins and

ends):

For each outcome, study participants will be followed up from the date of

the selected cancer diagnosis (index date) until the earliest of the

following events: occurrence of the outcome, end of follow up (1 year for

the main analysis or 2 years), loss to follow-up, end of data availability, or

date of death.

Setting: Routinely collected data from 10 databases in 8 European countries.

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Main measure of effect: Proportions, incidence rates and incidence rate ratios if information on

stage is available

B. Secondary research question and objective.

Objective: To characterise cancer patients at the time of cancer diagnosis in terms of

demographics, comorbidities, concomitant medications, as well as

treatments received in the first 90 days after cancer diagnosis.

Hypothesis: N/A

Population (mention key inclusion-

exclusion criteria): The study population will include all individuals aged 18 years and above

with a primary diagnosis of selected cancers in the study period from

01/01/2016 to 31/12/2023. Selected cancer types include:

• Malignant neoplasm of oesophagus (specified in the protocol as

oesophageal cancer, corresponding),

• Malignant neoplasm of stomach (stomach cancer),

• Malignant neoplasm of colon, rectosigmoid junction, rectum,

anus and anal canal ( colorectal cancer),

• Malignant neoplasm of liver and intrahepatic bile ducts (liver

cancer),

• Malignant neoplasm of pancreas (pancreatic cancer),

• Malignant neoplasm of trachea, bronchus and lung (lung

cancer),

• Malignant neoplasms of bone and articular cartilage

• (bone cancer)

• Malignant melanoma of skin (skin melanoma)

• Malignant neoplasm of breast (breast cancer),

• Malignant neoplasm of corpus uteri (corpus uteri cancer

including endometrial cancer),

• Malignant neoplasm of ovary (ovarian cancer),

• Malignant neoplasm of prostate (prostate cancer),

• Malignant neoplasm of kidney, except renal pelvis (kidney

cancer),

• Malignant neoplasm of meninges, brain and spinal cord, cranial

nerves and other parts of central nervous system (brain cancer),

• Malignant neoplasms, stated or presumed to be primary, of

lymphoid, haematopoietic and related tissue (Lymphoma and

leukaemia)

Only patients with the first and one cancer diagnosis (except non-

melanoma skin cancer) will be included. Additional eligibility of a

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minimum of 1 year of potential follow-up time will be imposed to ensure

sufficient time to capture potential outcomes of interest.

Exposure: N/A

Comparator: N/A

Outcome: Medical History: asthma, COPD, chronic liver disease, Crohn’s disease,

Diabetes mellitus, gastro-oesophageal reflux disease (GERD), GI-Bleeding,

Human Immunodeficiency Virus (HIV), Hyperlipidaemia, Hypertension,

Obesity, Osteoarthritis, Pneumonia, Psoriasis, Renal impairment,

Ulcerative Colitis, Viral Hepatitis, Visual system disorder [General] --

Schizophrenia, Dementia, Parkinson, Depressive disorder, Anxiety,

Attention Deficit Hyperactivity Disorder (ADHS) [Neurology],

thromboembolic events outcomes of interest mentioned above --- Any

cancer except non-melanoma skin cancer (for quality assessment

purposes, this should be 0 in our study population before index date).

Medication use: Agents acting on the renin-angiotensin system,

Antibacterials for systemic use, Antidepressants, Antiepileptics, Anti-

inflammatory and antirheumatic products, Antineoplastic agents, Anti-

psoriatic, Antithrombotic agents, Beta blocking agents, Calcium channel

blockers, Diuretics, Drugs for acid related disorders, Drugs for obstructive

airway diseases, Drugs used in diabetes, Immunosuppressants, Lipid

modifying agents, Opioids, Psycholeptics, Psychostimulants, agents used

for ADHD and nootropics [General] -- contraceptives [contraceptives].

Time (when follow up begins and

ends):

Medical History at the index date and medication at the index date and

from 1 to 90 days.

Setting: Routinely collected data from 10 databases in 8 European countries.

Main measure of effect: Proportions

8. RESEARCH METHODS

8.1 Study type and study design

This will be a population-level descriptive epidemiology and patient-level characterisation study. As described in the DARWIN EU® Complete Catalogue of Standard Data Analyses (Table 2). A retrospective cohort study of all newly diagnosed adult patients with selected cancers will be conducted.

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Table 2. Description of potential study types and related study designs.

Study type Study design Study classification

Population-level descriptive

epidemiology

Population-level cohort Off the shelf

Patient-level characterisation Cohort analysis Off the shelf

8.2 Study setting and data sources

This study will use routinely collected health data from 10 databases from 8 European countries. All of them have been previously mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The databases to be included in the study are:

1. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom 2. Danish Data Health Registries (DK-DHR), Denmark 3. Estonian Biobank (EBB), Estonia 4. Finnish Care Register for Health Care (FinOMOP-HILMO) Finland 5. Hospital District of Helsinki and Uusimaa (FinOMOP-HUS), Finland 6. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany 7. IQVIA Longitudinal Patient Database Belgium (IQVIA - LPD Belgium), Belgium 8. Integrated Primary Care Information (IPCI),Netherlands 9. The Information System for Research in Primary Care (SIDIAP), Spain. 10. UK Biobank (UKBB), United Kingdom

Information on data sources is available in Table 3. Databases were selected based on the person count of cancer diagnoses under interest and thromboembolic events, and European representativeness. We also considered the lack of major issues and errors during the most recent internal and Darwin EU onboarding quality checks for the selected databases. Preliminary stratification by tumour stage will only be feasible in DK-DHR, EBB and FinOMOP-HUS.

The selected databases also fulfil the criteria required to capture outcomes of interest and relevant data to conduct a patient-level characterisation of newly diagnosed cancer patients across different European settings and regions. Not all databases have all outcomes of interest. DVT is not present in IQVIA - LPD Belgium. SVT counts are limited in EBB, IQVIA - LPD Belgium. RVT is present only in CPRD GOLD, SIDIAP and UKBB.

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Table 3. Description of the selected data sources.

Country Name of Database Justification for Inclusion Health Care

setting

Type of Data Number of

active

subjects

Data lock for

the last

update

GB CPRD GOLD Covers primary care setting, data on

cancer diagnoses, comorbidities,

medications, and date of death.

Primary care,

hospital care

(OP)

EHRs 17.5 M 2024-04-15

DK DK-DHR National health data database

which includes information on

cancer diagnoses, staging and

medical history

All settings EHRs,

registries,

claims

8.5 M 2024-05-15

EE EBB Contains health insurance claims,

digital prescriptions, discharge

information and causes of death

through linkage with the national

death register. Data is linked to

cancer registry.

Primary care,

hospital care (IP

and OP)

EHRs, claims,

registries,

biobank

0.2 M 2023-03-20

FI FinOMOP - HILMO Nation-wide hospital registry data

with high-quality information on

cancer diagnoses and mortality.

Hospital care (IP

and OP)

EHRs,

registries

7.1 M 2024-06-24

FI FinOMOP - HUS Hospital registry which includes

information on cancer patients and

medical history.

Hospital care (IP

and OP)

EHRs 3.5 M 2024-05-03

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Country Name of Database Justification for Inclusion Health Care

setting

Type of Data Number of

active

subjects

Data lock for

the last

update

DE IQVIA DA Germany Covers primary care setting with

information on cancer diagnoses

and medical history.

Primary care EHRs 43.1 M 2024-03-25

BE IQVIA LPD Belgium Covers primary care setting with

information on cancer diagnoses

and medical history.

Primary care EHRs 1.1 M 2024-03-25

NL IPCI Covers primary care setting, data on

cancer diagnoses previously

validated, information available on

comorbidities, medications, and

date of death.

Primary care EHRs 2.9 M 2024-08-29

ES SIDIAP Covers primary care setting with

information on cancer diagnoses

and medical history.

Primary care EHRs 8.6 M 2023-03-20

GB UKBB Genetic data on biobank

participants linked to EHRs from

primary care, hospitalisations,

cancer registrations and mortality.

Primary care,

hospital care (IP

and OP)

EHRs,

registries,

biobank

0.5 M 2024-02-16

BE = Belgium, DE = Germany, DK=Denmark, EE = Estonia, ES=Spain, EHR=Electronic Health Record, FR = France, GB = United Kingdom of Great Britain and

Northern Ireland, FI = Finland, IP = Inpatient, NL = the Netherlands, OP = outpatient

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Clinical Practice Research Datalink GOLD (Oxford) (CPRD GOLD)

The CPRD GOLD database collects data in the United Kingdom and is maintained by Clinical Practice Research Datalink. CPRD GOLD contains data from all four UK constituent countries (England, Scotland, and Northern Ireland). A database contains data from 1987 to present. Data are collected from general practitioner (GP) clinics that use the Vision® software system. GP clinics are responsible for non-emergency care and referrals. Data is collected from patient records stored at GP clinics. Over 98% of the UK population is registered with a GP. Covering 4.6% of the current UK population, CPRD GOLD includes 4.9% of contributing GP practices. Compared with the UK Census 2011, CPRD patients are broadly representative of the UK population in terms of age, sex, and ethnicity and comparable to the Health Survey for England for body mass index distribution. The CPRD GOLD may not be representative of all practices in the UK based on geography and size. It obtains data from electronic health records.

The database currently holds information about the person (demographics), visits (inpatient and outpatient), conditions and procedures (billing codes), drugs (outpatient prescriptions and inpatient orders and administrations), measurements (laboratory tests and vital signs), and dates of death (in or out- hospital death). CPRD GOLD uses SNOMED medical terms. Transformation-level validation checks for referential integrity between records ensure that no orphan records are included in the database (for example, all event records link to a patient). Duplicate records are identified and removed. In contrast, research-quality-level validation covers the actual content of the data. CPRD provides a patient-level data quality metric as a binary ‘acceptability’ flag. This is based on recording and internal consistency of key variables, including date of birth, practice registration date, and transfer out date. Validation of the CPRD has shown a high positive predictive value for some diagnoses, and where evaluated, comparisons of incidence with other UK data sources are also broadly similar. Research into data quality has shown large variations in the inter-practice recording of data. Established linkages include, but are not limited to, Hospital Episode Statistics (hospitalisation data), Office for National Statistics (mortality data including causes of death), Index of Multiple Deprivation and Townsend scores (deprivation data), and disease registries. Vital status (death date and causes) is obtained from the Office for National Statistics.

CPRD GOLD is limited to GP records. General practices receive information about patient contacts with secondary care, which must be manually entered into the patient record. However, the database also combines data from various sources through effective linkages. Primary care data quality is variable because GPs enter data during routine consultations, not for research. The database was described by Herrett et al., 2015. The database description is also available at cprd.com.

Danish Data Health Registries (DK-DHR)

Danish health data is collected, stored, and managed in national health registers at the Danish Health Data Authority. It covers the entire population, which makes it possible to study the development of diseases and their treatment over time. There are no gaps in terms of gender, age and geography in Danish health data due to mandatory reporting on all patients from cradle to grave in all hospitals and medical clinics. Personal identification numbers link data across registers, so we have data on all Danes throughout their lives, regardless of whether they have moved around the country. High data quality due to standardisation, digitisation and documentation means Danish health data is not based on interpretation. The Danish Health Data Authority is responsible for the national health registers and maintaining and developing standards and classifications in the Danish healthcare system. The legislation ensures a balance between personal data protection and use. In the present database, we have access to the following registries for the entire Danish population of 5.9 million persons from 1.1.1995: The Central Person Registry (CPR), The National Patient Registry (LPR), The Register of Pharmaceutical Sales (LSR), The National Cancer Register (CAR), The Cause of Death registry (DAR), The Clinical Laboratory Information Register (LAB), COVID-19 test and

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vaccination Registries (SSI-OVD, SSI-DDV), The complete Vaccination registry (DDV_all). All data registered from 1.1.1995 will be included.

Estonian Biobank (EBB)

The EBB collects data in Estonia and is maintained at the Estonian Genome Center, University of Tartu. It is a nationwide database, and the network of recruitment offices for EBB covers all 15 counties of the country. The Estonian Genome Project Foundation initiated the Estonian Biobank Project in 1999, which was transformed into the Estonian Genome Center of the University of Tartu (EGCUT) in 2007. The data is available from 2004 onwards. The Estonian Biobank cohort is a volunteer-based sample of the Estonian resident adult population. EBB represents an Estonian population-based cohort size of 52,000 participants aged 18 years and older recruited at GP offices, private practices, and hospitals or in the recruitment offices of the Estonian Genome Center. The age, sex, and geographical distribution closely reflect those of the Estonian adult population and encompass nearly 5% of the population. Overall, the representation of men in the biobank is 3.4%, and women’s is 5.5%. Older people tend to participate less frequently; however, all age groups are well represented. The database obtains data from the biobank records. All participants have undergone a standardised health assessment, including providing blood samples for purification of DNA, white blood cells, and plasma, and completed a questionnaire covering various health-related topics, such as lifestyle, diet, and clinical diagnoses. Diseases and health problems are recorded as ICD-10 codes and prescribed medicine according to the ATC classification.  

For all starting data collectors, the first ten questionnaires were monitored for completeness and illogical answers, and after that, 10% were selected randomly for monitoring; 21% were inspected and corrected when necessary. From the monitored questionnaires, 99% were classified as high quality, meaning all the fields were filled in, and the answers appeared logical. Follow‐up data are available via linkage with national health‐related registries and re‐examination of participants. Furthermore, electronic health records are updated every half year for phenotypic outcome information. The EBB database is regularly linked with national registries, hospital databases, and the national health insurance fund database, which holds treatment and service bills. Vital status (death date and causes) is obtained from the Causes of Death Registry. Participation in the EBB cohort is voluntary; therefore, the biobank does not represent a classical random sample and could be subject to recruitment bias. Although recruitment was open to everyone, there is a disproportion of ethnic Estonians and ethnic Russians in the biobank, with Estonians being overrepresented and Russians underrepresented. Also, the limited depth of collected data can sometimes limit the number of projects in which the data can be used. The database was described by Leitsalu et al., 2015, 10.1093/ije/dyt268. The database description is also available at genomics.ut.ee/en/content/estonian-biobank.

Finnish Care Register for Health Care (FinOMOP - HILMO)

The Finnish Care Register for Health Care (fi: Hoitoilmoitusrekisteri) continues the former Hospital Discharge Register, which originally gathered data on hospital discharge (Sund et al., 2012). The Care Register has comprehensive data on services and service users nationwide, including Finnish public inpatient and outpatient primary and specialised care. Since 1998, the register has covered public outpatient and inpatient specialised care and private inpatient care (TerveysHilmo). Since 2011, the register has covered public primary care (AvoHilmo). Since 2020, the register has covered private outpatient care and occupational care. The CDM is currently produced from the data collection on inpatient and outpatient specialised care (TerveysHilmo) and is limited to observation periods commencing after 01/01/2015. The Register of Primary Health Care Visits (AvoHilmo) is currently outside the scope of the CDM and will be added to CDM during the remainder of 2023. The inclusion of data collected before 2015 is also being planned. The National Population Registry is also used as a source for the CDM database. The National

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Population Registry data forms the basis for forming the patient population. This ensures up-to-date location (municipality of residence) of patients and complete death occurrences (although not the cause of death). Using the complete population as a basis for the person table also facilitates calculations on a population level, e.g. incidence rates. The HILMO database has been used to assess the quality of cancer registry data in Finland (Leinonen et al., 2017).

Hospital District of Helsinki and Uusimaa (FinOMOP - HUS)

The HUS data lake is a comprehensive, integrated data source derived in real-time from all patients who visit the HUS hospitals and receive treatment (Vikkula et al., 2023). HUS is responsible for specialised healthcare in Finlands’ Uusimaa region and the treatment of many rare and severe diseases nationally centralised to HUS. HUS’s catchment area covers about 2.2 million people. In 2023, there were 2.43 million booked appointments and 255,896 emergency department visits for specialist medical care. 691,702 patients received any treatment in HUS specialist medical care and at emergency departments, and 86,849 surgical procedures were performed. All visits, examinations, laboratory tests, procedures, and treatments are recorded in the HUS IT systems and integrated into the data lake. The data lake stores decades of clinical information in digital format, and data from past and current source systems are available.

IQVIA Disease Analyzer Germany (IQVIA DA Germany)

Germany DA is collected from extracts of patient management software used by GPs and specialists from ambulatory care settings. Patients visiting multiple providers are not cross-identified for data protection reasons and, therefore, recorded as separate in the system. Dates of service include from 1992 through the present. The first and last consultation dates define observation time. Germany has no mandatory GP system, and patients can choose specialists. Drugs are recorded as prescriptions of marketed products. No explicit registration or approval is needed for drug utilisation studies.

IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium)

Belgium Longitudinal patient data (LPD) is collected from GP prescribing systems and contains patient records on all signs and symptoms, diagnoses and prescribed medications. The information recorded allows patients and doctors to be monitored longitudinally. Data are recorded directly in real-time during patient consultations via a practice management software system. It is used in studies to provide various market insights such as treatment trends, patient pathway analysis and treatment compliance. The panel of contributing physicians (a stable 300 GPs) is maintained as a representative sample of Belgium's primary care physician population according to three criteria known to influence prescribing: age, sex and geographical distribution. Currently, the database covers 1.1 M cumulative patients from 2012 through to the present. The panel consists of a stable 300 GPs that are geographically well-spread. The total number of active GPs in Belgium is 15.602. The regional geographical spread of physicians in the LPD data is also representative of the distribution across the country: 57% GPs in the North (compared to 54% nationally), 31% in the South (33% nationally) and 12% in Brussels (13%). The data provider has more than 2.250 GPs under contract, so a replacement is easily found in case of a dropout. Drugs obtained over the counter by the patient outside the prescription system are not reported. No explicit registration or approval is needed for drug utilisation studies.

Integrated Primary Care Information (IPCI)

The database collects data in the Netherlands. It was started in 1992 by the Department of Medical Informatics of the Erasmus University Medical Center in Rotterdam, the Netherlands. The current database contains patient records from 2006 onwards when the size of the database started to increase significantly. IPCI is a nationwide Dutch database. However, it mainly covers the central part of the country, including the most densely populated and non-urban areas. The IPCI database contains data from records of general

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practitioners' (GPs) practices. It contains information on all patients registered with GPs responsible for non-emergency care and referrals. More than 99% of the Dutch population has health insurance, and almost all citizens are registered with a general practitioner. Over 12 months, around 78% of the population has at least one contact with their GP. IPCI included around 350 GP practices out of around 5000 in the country (~ 7%). The demographic composition of the IPCI population mirrors that of the general Dutch population in terms of age and sex.

IPCI obtains data from computer-based patient records. Patient-level data includes demographic information, complaints and symptoms, diagnoses, laboratory test results, lifestyle factors, and correspondence with secondary care, such as referral and discharge letters. Dutch GPs use the International Classification of Primary Care (ICPC-1) coding for complaints, symptoms, and diagnoses, an international standard developed and updated by the World Organization of Family Doctors (WONCA) International Classification Committee.

Extensive quality control steps are performed before each data release. These include comparing patient characteristics between practices and checks to identify abnormal temporal data patterns in practices. Additional checks include over 200 indicators related to population characteristics (e.g., reliability of birth and mortality rates) and medical data (e.g., availability of durations of prescriptions, completeness of laboratory results, availability of hospital letters and prescriptions, the proportion of patients with blood pressure measurement, etc.).

IPCI is not linked with other databases. Vital status (death date and cause) is collected based on GP records. The main limitation is that IPCI is limited to GP records, and although it contains information on referrals and discharge letters, it may not capture specific hospital information. The database profile was described by de Ritter et al., 2022. The database description is also available at ipci.nl.

The Information System for Research on Primary Care (SIDIAP)

The database collects data in Spain and is maintained by the SIDIAP team, supported by the Catalan Health Institute and the Institute for Primary Health Care Research Jordi Gol i Gurina. It contains patient records from 2005 onwards and is updated every six months. It is a regional database covering the region of Catalonia. SIDIAP collects data in the primary care setting.  It contains data from the population registered in over 280 primary care practices throughout Catalonia.  Approximately 80% of the population registered with primary care is covered by SIDIAP. The demographic composition within SIDIAP closely mirrors that of the broader Catalan population, encompassing a representative spectrum of geographic distribution, age, and sex proportions. SIDIAP obtains data from electronic health records.  The dataset covers demographics, all-cause mortality, disease diagnoses, prescription and dispensation records of drugs, results of laboratory tests, socio-economic indicators, vaccination records, lifestyle information, parent-child linkage, and various clinical parameters.  Diseases are classified under the International Classification of Diseases 10th revision (ICD-10)

Quality checks have been implemented, including central identification of duplicate patient IDs and visual inspection for temporal patterns in the registry of a certain variable. Furthermore, the data undergoes assessment for availability (longitudinally and reliability), plausibility (range checks and unusual values), and visualisation tools. Specifically, for biochemistry data, consistency for measurements taken in different laboratories is assessed, and unit conversion is undertaken when needed. SIDIAP is linked with numerous other databases. It integrates data from external sources, including laboratory biomarker data, drug prescription and dispensation records, hospital discharge records, mental health centres, and other specific disease registries.  Vital status (death date and cause) is collected through linkage with the civil registry. The main limitation is that SIDIAP covers only primary health care records. However, it is combined with data

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from various other sources through effective linkages.  The database profile was described by de Recalde et al., 2022.  The database description is also available at sidiap.org.

UK BioBank (UKBB)

UK Biobank is a powerful biomedical database that can be accessed globally to enable discoveries to improve public health. UK Biobank contains in-depth genetic, biomarker, imaging and health information from over half a million volunteers living in the UK aged 40–69 years at the time of recruitment (2006– 2010). UK Biobank has collected unprecedented biological and medical data for a large-scale, long-term prospective study. With their consent, they regularly provide blood, urine and saliva samples and detailed information about their lifestyle, which is then linked to their health-related records (e.g. primary care data, hospital data, cancer registry) to provide a deeper understanding of how individuals experience diseases. Since 2012, the UK Biobank database, the largest and richest of its kind, has been open to applications from researchers. The resource is available in anonymised format to scientists from the UK and worldwide, subject to verification that the research is health-related and in the public interest. Researchers are required to publish their results in an open-source publication site or an academic journal and return their findings to the UK Biobank. At the time of writing, nearly 3,600 research applications have been approved for using UK Biobank data, and 3,239 peer-reviewed articles based on them have been published.

8.3 Study period

The study period will be from 01/01/2016 to 31/12/2023 or the end of available data in each source if it

comes earlier (see Table 3 Data lock for the last update column for more details).

8.4 Follow-up

Study participants will be followed up from the date of cancer diagnosis (index date, Table 4) until the first

occurrence of any of the following events: occurrence of the outcome, loss to follow-up, end of follow-up

(one or two years), end of data availability, or date of death.

Table 4. Operational definition of time 0 (index date) and other primary time anchors.

Study

population

name(s)

Time Anchor

Description

(e.g. time 0)

Number of

entries

Type of

entry

Washout

window

Care

Settin

g1

Code

Type2

Diagnosis

position

Incident

with

respect

to…

Measureme

nt

characteristi

cs/

validation

Source

of

algorith

m

Cancer

patients

Date of

cancer

diagnosis

Single

entry

Incident [any

time

, -1]

IP,

OP

,

OT

SNOMED,

ICD-O-3

Any Any

cancer

diagnos

is

except

non-

melano

ma skin

cancer

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

2 SNOMED = Systematized Nomenclature of Medicine, ICD-O-3: International Classification of Diseases for Oncology, 3rd Edition

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8.5 Study population with inclusion and exclusion criteria

The study population will include all individuals aged 18 years and above with a primary diagnosis of selected cancers in the study period. Cancer types will include lung, breast, ovary, endometrium, prostate, pancreas, colorectal, stomach, oesophageal, liver, brain, bone, kidney, melanoma, lymphoma and leukaemia. Only patients with the first and one cancer diagnosis (except non-melanoma skin cancer) will be included. Cancer cases and thromboembolic events will be identified based an appropriate computable phenotyping algorithms. Conditions in the OMOP CDM use the Systematised Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. For cancer diagnoses, the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) will also be considered. Algorithms to reproduce cancer phenotypes will be shown along with the study results.

Additional eligibility of a minimum of 1 year of potential follow-up time will be imposed to ensure sufficient time to capture potential outcomes of interest. For instance, in a database with data up to 31/12/2023, cancer cases diagnosed from 01/01/2016 up to 01/01/2023 will be included. A prior history requirement of one year of observation prior to the index date will be imposed. This is especially relevant for primary care databases, where a minimum observation period of one year before a cancer diagnosis is required to detect prevalent cases.

Appendix II provides a preliminary code list for each cancer. The code list might be modified after cohort diagnostics

The operational definitions of the inclusion and exclusion criteria are presented in Error! Reference source not found. and Table 6, respectively.

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Table 5. Operational definitions of inclusion criteria.

Criterion Details Order of application

Assessment window

Care Settings¹

Code Type

Diagnosis position

Applied to study

populations:

Measurement characteristics/

validation

Source for algorithm

Patients newly diagnosed with selected cancers

Primary selected cancer After - IP, OP, OT

SNOME D, ICD- O-3

N/A Cancer patients

N/A N/A

Age Participants aged 18 or above

After At index date

IP, OP, OT

N/A N/A Cancer patients

N/A N/A

Minimum prior observation period of 365 days

Only participants with a minimum observation period of 365 days prior to diagnosis of chondrosarcoma (index date) s

Before 365 days OP, OT N/A N/A Cancer patients

N/A N/A

Minimum potential follow-up time

Only participants with a cancer diagnosis (index date) occurring one year prior to end of data availability in the database will be included

After [0, 365] IP, OP, OT

N/A N/A Cancer patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable.

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Table 6. Operational definitions of exclusion criteria.

Criterion Details Order of

application

Assessment

window

Care

Settings¹

Code

Type

Diagnosis

position

Applied to

study

populations:

Measurement

characteristics/

validation

Source

for

algorithm

History of cancer

diagnosis

Participants with a diagnosis of

cancer (any, excluding non-

melanoma skin cancer) any

time prior index date

After Any time

prior to

cancer

diagnosis

OP, IP,

OT

SNOMED,

ICD-O-3

Any Cancer

patients

N/A N/A

Multiple primary

tumors

Participants with a two

diagnoses of cancer (any,

excluding non-melanoma skin

cancer) at index date

After At index

date

OP, IP,

OT

SNOMED,

ICD-O-3

Any Cancer

patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

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8.6 Variables

8.6.1. Exposures

None.

8.6.2. Outcomes

Conditions of interest will include thromboembolic events, in particular, (deep vein thrombosis (DVT),

pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous

thrombosis (PVT), splanchic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis,

retinal vein thrombosis (RVT), including retinal central vein thrombosis, and disseminated intravascular

coagulation (DIC).

The operational definition of the outcomes is presented in the Table .

A preliminary list of codes is provided in the Appendix I.

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Table 7. Operational definitions of outcome.

Outcome

name

Details Primary

outcome

Type of

outcome

Washout

window

Care

Settings¹

Code Type Diagnosis

Position

Applied to

study

populations

Measurement

characteristics/

validation

Source of algorithm

DVT See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

PE See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

VTE See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

PVT See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

SVT See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

RTV See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

DIC See Appendix

I

Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable, DVT = deep vein thrombosis, PE = pulmonary embolisms, VTE = venous thromboembolism, PVT = pelvic venous thrombosis,

SVT = splanchic vein thrombosis, RVT = retinal vein thrombosis, DIC = DIC.

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8.6.3. Other covariates, including confounders, effect modifiers and other variables

The age at the index date (primary cancer diagnosis) will be described. The following age grouping will be used: 18-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90 and over. The sex (male/female) of study participants will also be identified.

Large-scale patient-level characterisation will be conducted at the time of diagnosis. Age and sex at the time of cancer diagnosis will be described for each generated study cohort. Medical history will be assessed at the time of diagnosis. Medication use history will be reported at the time of diagnosis. We will also report medication use for 1 to 90 days post-index date.

A list of pre-specified co-morbidities and co-medications will be described. Co-morbidities and co- medications were selected based on definitions that were previously used in other DARWIN EU studies. These will include:

• Medical History: asthma, COPD, chronic liver disease, Crohn’s disease, Diabetes mellitus, gastro- oesophageal reflux disease (GERD), GI-Bleeding, Human Immunodeficiency Virus (HIV), Hyperlipidaemia, Hypertension, Obesity, Osteoarthritis, Pneumonia, Psoriasis, Renal impairment, Ulcerative Colitis, Viral Hepatitis, Visual system disorder [General] -- Schizophrenia, Dementia, Parkinson, Depressive disorder, Anxiety, Attention Deficit Hyperactivity Disorder (ADHS) [Neurology], thromboembolic events outcomes of interest mentioned above --- Any cancer except non-melanoma skin cancer (for quality assessment purposes, this should be 0 in our study population before index date).

• Medication use: Agents acting on the renin-angiotensin system, Antibacterials for systemic use, Antidepressants, Antiepileptics, Anti-inflammatory and antirheumatic products, Antineoplastic agents, Anti-psoriatic, Antithrombotic agents, Beta blocking agents, Calcium channel blockers, Diuretics, Drugs for acid related disorders, Drugs for obstructive airway diseases, Drugs used in diabetes, Immunosuppressants, Lipid modifying agents, Opioids, Psycholeptics, Psychostimulants, agents used for ADHD and nootropics [General] -- contraceptives [contraceptives].

If available, AJCC\UICC TNM stage groups, and WHO/ECOG performance status will also be described and used for stratification in objective 1.

The operational definition of the covariates is described in the

Table 8. Operational definitions of covariates.

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Characteristic Details Type of

variable

Assessment

window

Care

Settings

¹

Cod

e

Typ

e

Diag

nosis

Posit

ion

Applied to

study

population

s

Meas

ureme

nt

charac

teristi

cs/

valida

tion

Sourc

e for

algorit

hm

Co-

morbidities

Large-scale patient-

level

characterisation

with regard to

underlying

comorbidities

Binary [0,0] IP, OP,

OT

SNO

ME

D

N/A Cancer

patients

N/A N/A

Medication

use

Large-scale patient-

level

characterisation

with regard to use

of concomitant

drugs

Binary [0,0], [1,90] IP, OP,

OT

RxN

orm

N/A

Cancer

patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

8.

Table 8. Operational definitions of covariates.

Characteristic Details Type of

variable

Assessment

window

Care

Settings

¹

Cod

e

Typ

e

Diag

nosis

Posit

ion

Applied to

study

population

s

Meas

ureme

nt

charac

teristi

cs/

valida

tion

Sourc

e for

algorit

hm

Co-

morbidities

Large-scale patient-

level

characterisation

with regard to

underlying

comorbidities

Binary [0,0] IP, OP,

OT

SNO

ME

D

N/A Cancer

patients

N/A N/A

Medication

use

Large-scale patient-

level

characterisation

with regard to use

of concomitant

drugs

Binary [0,0], [1,90] IP, OP,

OT

RxN

orm

N/A

Cancer

patients

N/A N/A

1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable

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8.7 Study size

No sample size will be calculated as this is a descriptive study. However, analysis by strata will be limited to

databases with enough cases (5 or more) to provide meaningful results. Summary measures of occurrence

will not be calculated for strata with counts less than 5.

8.8 Analysis

8.1.1. Federated network analyses

Analyses will be conducted separately for each database and will be carried out in a federated manner, allowing analyses to be run locally without sharing patient-level data. Before study initiation, the analytics are tested on a subset of the data sources or on a simulated set of patients, and quality control checks are performed. Once all the tests are passed, the final study code is released in the version-controlled Study Repository for execution against all the participating data sources.

The data partners locally execute the analytics against the OMOP CDM in R Studio and review and approve the default aggregated results before returning them to the Coordination Centre. Sometimes, multiple execution iterations are performed, and additional fine-tuning of the code base is needed. A service desk will be available for support during the study execution.

The study results of all data sources are checked, after which they are made available to the team in the

Digital Research Environment (DRE), and the Study Dissemination Phase can start. All results are locked and

timestamped for reproducibility and transparency.

8.8.2 Patient privacy protection

Cell suppression will be applied as required by databases to protect people’s privacy. Cell counts < 5 will be

masked.

8.8.3 Statistical model specification and assumptions of the analytical approach considered

Data analyses

This study will involve the analysis described in Table 9.

Table 9. Description of study types and type of analysis.

Study type Study

classification Type of analyses

Patient-level

characterisation

Off-the-shelf - Large-scale characterisation

- Patient-level characteristics

- Prognosis / progression to a pre-specified outcome

- Standard care description

Population-level

descriptive

epidemiology

Off-the-shelf - Incidence rates of the condition of interest

- Prevalence rates of the condition of interest

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The incidence rates of thromboembolic events (Objective 1) will be estimated over one and two years after the selected cancer diagnosis. The denominator will include the person-time contributed by the cancer patients from the date of diagnosis till the event or end of the respective period (one or two years). The numerator will include the outcomes of interest. Incidence rates will be presented per person-time with 95% confidence intervals derived using the exact method. Additionally, incidence rate ratios will be calculated when information on cancer stage is available with group AJCC/UICC stage as a reference. Each cancer type and outcome will be assessed separately.

Large-scale patient-level characterisation (Objective 2) will be conducted at the index date. Age and sex will be described at the time of diagnosis as well as follow-up time after the index date. The medical history and medication will be assessed at the index date. Medication use will be at the index date and from 1 to 90 days after diagnosis.

For all analyses, absolute counts and proportions will be reported. A minimum cell count of 5 will be used when reporting results, with any smaller counts reported as “<5” and zero counts as “0”. Overall analyses will be done separately for each database.

Further stratification by age category (18-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90 and over), sex,

study sub-period, and cancer stage will be conducted when possible (minimum cell count reached and data

available). The following sub-periods will also be used: 2016-2019 and 2020-2023.

R-packages

The following R packages will be used: “CohortDiagnostics” (https://github.com/OHDSI/CohortDiagnostics/) to evaluate the phenotype algorithms developed for the study.

“IncidencePrevalence” (https://github.com/darwin-eu/IncidencePrevalence) to estimate incidence rates by dividing the number of events by the person-time. Incidence rates will be calculated using function estimateIncidence() with denominator person-time specified by generateDenominatorCohortSet().

“PatientProfiles” (https://github.com/darwin-eu-dev/PatientProfiles) and “CohortCharacteristics” (https://github.com/darwin-eu-dev/CohortCharacteristics) will be used for summarising characteristics of cohorts of patients with cancer.

8.9 Evidence synthesis

Results from analyses described in section 8.8 will be presented separately for each database and no meta-

analysis of results will be conducted.

9. DATA MANAGEMENT

9.1 Data management

All databases are mapped to the OMOP common data model. This enables the use of standardised analytics and tools across the network since the structure of the data and the terminology system is harmonised. The OMOP CDM is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) initiative and is described in detail on the wiki page of the CDM: https://ohdsi.github.io/CommonDataModel and in The Book of OHDSI: http://book.ohdsi.org

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The analytic code for this study will be written in R. Each data partner will execute the study code against their database containing patient-level data and will then return the results set which will only contain aggregated data. The results from each of the contributing data sites will then be combined in tables and figures for the study report.

9.2 Data storage and protection

For this study, participants from various EU member states will process personal data from individuals which is collected in national/regional electronic health record databases. Due to the sensitive nature of this personal medical data, it is important to be fully aware of ethical and regulatory aspects and to strive to take all reasonable measures to ensure compliance with ethical and regulatory issues on privacy.

All databases used in this study are already used for pharmaco-epidemiological research and have a well- developed mechanism to ensure that European and local regulations dealing with ethical use of the data and adequate privacy control are adhered to. In agreement with these regulations, rather than combining person level data and performing only a central analysis, local analyses will be run, which generate non- identifiable aggregate summary results.

The output files are stored in the DARWIN Digital Research Environment. These output files do not contain

any data that allows the identification of subjects included in the study. The DRE implements further

security measures to ensure a high level of stored data protection to comply with the local implementation

of the General Data Protection Regulation (GDPR) (EU) 679/20161 in the various member states.

10. QUALITY CONTROL

General database quality control

Several open-source quality control mechanisms for the OMOP CDM have been developed (see Chapter 15 of The Book of OHDSI http://book.ohdsi.org/DataQuality.html). In particular, data partners are expected to run the OHDSI Data Quality Dashboard tool (https://github.com/OHDSI/DataQualityDashboard). This tool provides numerous checks relating to the conformance, completeness and plausibility of the mapped data. Conformance focuses on checks that describe the compliance of the representation of data against internal or external formatting, relational, or computational definitions; completeness in the sense of data quality is solely focused on quantifying missingness or the absence of data, while plausibility seeks to determine the believability or truthfulness of data values. Each of these categories has one or more subcategories and are evaluated in two contexts: validation and verification. Validation relates to how well data aligns with external benchmarks with expectations derived from known true standards. In contrast, verification relates to how well data conforms to local knowledge, metadata descriptions, and system assumptions.

Study-specific quality control

When defining selected cancers, outcomes and co-morbidities, a systematic search of possible codes for inclusion was identified using CodelistGenerator R package (https://github.com/darwin- eu/CodelistGenerator). This software allows the user to define a search strategy and using this will then query the vocabulary tables of the OMOP CDM to find potentially relevant codes. The codes returned will be then reviewed by two clinical epidemiologists to consider their relevance. In addition, the CohortDiagnostics R package (https://github.com/OHDSI/CohortDiagnostics) will be run to assess the use of different codes across the databases contributing to the study and identify any codes potentially omitted in error. This will allow for a consideration of the validity of the study cohort of patients with the selected

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cancers and co-morbidities in each of the databases and inform decisions around whether multiple definitions are required.

The study code will be based on three R packages currently being developed to (1) estimate incidence rates of thromboembolic events (“IncidencePrevalence”), (2) characterise demographic and clinical characteristics ("PatientProfiles” and “CohortCharacteristics”). These packages will include numerous automated unit tests to ensure the validity of the codes, alongside software peer review and user testing. The study code will be made publicly available via GitHub.

11. LIMITATIONS OF THE RESEARCH METHODS

The study will be informed by routinely collected health care data, and so data quality issues must be considered. In particular, the identification of cancer patients and thromboembolic events may vary across databases. While relatively few false positives would be expected, false negatives may be more likely, especially for databases without patient-level linkage to secondary care data. Underestimation of thromboembolic events is also possible, particularly for rare events with complex diagnoses.

Given the large number and diverse nature of participating data sources, it is important to note that differences in patient representations might arise from disparate coding practices and specifics of data capture. The granularity or detail of concepts representing clinical facts can vary across source terminologies (e.g., ICD-10, Read codes), influencing how information is later transformed into standardised vocabularies (Ostropolets et al., 2021). The preliminary code lists created to identify cancer patients include codes from standard vocabularies used in tumour registries, such as ICD-O-3 codes. However, most databases will only have information on cancer diagnoses using SNOMED codes, which may not be granular enough to cover all the topology and histology details of cancer (Campbell et al., 2014). ICD-O-3 codes are only available at DK-DHR, EBB and UKBB.

The large-scale characterisation will provide an overview of the characteristics, comorbidities, and medication use, including anticancer treatment is available, of cancer patients. However, our study will not differentiate the time before and after cancer treatment initiation, and therefore it will not be able to disentangle the risk of thromboembolic events posed by cancer treatments from the risk posed by cancer itself.

In addition, for the calculation of incidence rates (Objective 1), we will apply a one-year washout to exclude patients who experienced the thromboembolic events under study prior to index date. Given that acute thromboembolic events can be the first manifestation of an occult malignancy, we will miss patients with thromboembolic events who are subsequently diagnosed with cancer.

12. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE

REACTIONS

Adverse events/adverse reactions will not be collected or analysed as part of this evaluation. The nature of

this non-interventional evaluation, through the use of secondary data, does not fulfil the criteria for

reporting adverse events, according to module VI, VI.C.1.2.1.2 of the Good Pharmacovigilance Practices

(https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-good-

pharmacovigilance-practices-gvp-module-vi-collection-management-submission-reports_en.pdf).

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13. GOVERNANCE BOARD ASPECTS

All data sources require approval from their respective IRB boards, with the exception of IQVIA DA

Germany and IQVIA LPD Belgium which will not require any further specific approvals to undertake this

study. 

14. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY

RESULTS

A PDF report, including an executive summary and the specified tables and/or figures, will be submitted to EMA by the DARWIN EU® CC upon completion of the study.

An interactive dashboard incorporating all the results (tables and figures) will be provided alongside the PDF report. If requested, the full set of underlying aggregated data used in the dashboard will also be made available.

15. OTHER ASPECTS

None.

16. REFERENCES

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van Hylckama Vlieg, M. A., Nasserinejad, K., Visser, C., Bramer, W. M., Ashrani, A. A., Bosson, J. L., ... &

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Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data

Resource Profile: Clinical Practice Research Datalink (CPRD). International journal of epidemiology, 44(3),

827–836.

de Ridder, M. A. J., de Wilde, M., de Ben, C., Leyba, A. R., Mosseveld, B. M. T., Verhamme, K. M. C., van der

Lei, J., & Rijnbeek, P. R. (2022). Data Resource Profile: The Integrated Primary Care Information (IPCI)

database, The Netherlands. International journal of epidemiology, 51(6), e314–e323.

Leinonen, M. K., Miettinen, J., Heikkinen, S., Pitkäniemi, J., & Malila, N. (2017). Quality measures of the

population-based Finnish Cancer Registry indicate sound data quality for solid malignant

tumours. European journal of cancer (Oxford, England: 1990), 77, 31–39.

Vikkula, J., Uusi-Rauva, K., Ranki, T., Toppila, I., Aalto-Setälä, M., Pousar, K., Vassilev, L., Porkka, K.,

Silvennoinen, R., & Brück, O. (2023). Real-world evidence of multiple myeloma treated from 2013 to 2019

in the Hospital District of Helsinki and Uusimaa, Finland. Future oncology (London, England), 19(30), 2029–

2043.

Sund R. (2012). Quality of the Finnish Hospital Discharge Register: a systematic review. Scandinavian journal

of public health, 40(6), 505–515.

Recalde, M., Davila-Batista, V., Díaz, Y., Leitzmann, M., Romieu, I., Freisling, H., & Duarte-Salles, T. (2021).

Body mass index and waist circumference concerning the risk of 26 types of cancer: a prospective cohort

study of 3.5 million adults in Spain. BMC medicine, 19(1), 10.

Ostropolets A, Reich C, Ryan P, et al. Characterizing database granularity using SNOMED-CT

hierarchy. AMIA Annu Symp Proc. 2021;2020:983-992. Published 2021 Jan 25.

Campbell, W. S., Campbell, J. R., West, W. W., McClay, J. C., & Hinrichs, S. H. (2014). Semantic analysis of

SNOMED CT for a post-coordinated database of histopathology findings. Journal of the American Medical

Informatics Association : JAMIA, 21(5), 885–892.

17. ANNEXES

Appendix I

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 35/48

Table A1 Preliminary code list for deep vein thrombosis (DVT)

Concept Id SNOMED Code Concept Name

762047 286411000119108 Acute bilateral thrombosis of subclavian veins

762148 293471000119106 Acute deep vein thrombosis of bilateral iliac veins

761444 15711361000119101 Acute deep vein thrombosis of bilateral lower limbs following coronary artery bypass graft

35616028 293491000119107 Acute deep vein thrombosis of left iliac vein

35615035 15711401000119105 Acute deep vein thrombosis of left lower limb following procedure

761416 15708441000119103 Acute deep vein thrombosis of left upper limb following coronary artery bypass graft

35615031 15708401000119100 Acute deep vein thrombosis of left upper limb following procedure

43531681 651000119108 Acute deep vein thrombosis of lower limb

35616027 293481000119109 Acute deep vein thrombosis of right iliac vein

35615034 15711241000119106 Acute deep vein thrombosis of right lower limb following procedure

761415 15708281000119109 Acute deep vein thrombosis of right upper limb following coronary artery bypass graft

35615030 15708201000119101 Acute deep vein thrombosis of right upper limb following procedure

44782746 132281000119108 Acute deep venous thrombosis

44782751 134961000119104 Acute deep venous thrombosis of axillary vein

762008 285321000119103 Acute deep venous thrombosis of bilateral axillary veins

760875 12237551000119104 Acute deep venous thrombosis of bilateral calves

765155 285441000119102 Acute deep venous thrombosis of bilateral ileofemoral veins

762017 285501000119103 Acute deep venous thrombosis of bilateral internal jugular veins

762417 350291000119100 Acute deep venous thrombosis of bilateral legs

762020 285561000119102 Acute deep venous thrombosis of bilateral popliteal veins

765546 285621000119106 Acute deep venous thrombosis of bilateral tibial veins

762004 285261000119104 Acute deep venous thrombosis of both upper extremities

44782742 132241000119103 Acute deep venous thrombosis of calf

44782747 132291000119106 Acute deep venous thrombosis of femoral vein

762015 285451000119100 Acute deep venous thrombosis of ileofemoral vein of left leg

765541 285461000119103 Acute deep venous thrombosis of ileofemoral vein of right lower extremity

44782748 132301000119107 Acute deep venous thrombosis of iliofemoral vein

44782752 135001000119100 Acute deep venous thrombosis of internal jugular vein

762009 285331000119100 Acute deep venous thrombosis of left axillary vein

760876 12237631000119109 Acute deep venous thrombosis of left calf

765540 285391000119101 Acute deep venous thrombosis of left femoral vein

765922 285511000119100 Acute deep venous thrombosis of left internal jugular vein

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 36/48

762418 350301000119104 Acute deep venous thrombosis of left lower extremity

765537 285271000119105 Acute deep venous thrombosis of left upper extremity

44782767 136781000119101 Acute deep venous thrombosis of lower extremity as complication of procedure

46270071 132111000119107 Acute deep venous thrombosis of lower limb due to and following coronary artery bypass

grafting

762022 285581000119106 Acute deep venous thrombosis of politeal vein of right leg

44782743 132251000119101 Acute deep venous thrombosis of popliteal vein

762021 285571000119108 Acute deep venous thrombosis of popliteal vein of left leg

762010 285341000119109 Acute deep venous thrombosis of right axillary vein

760877 12237711000119106 Acute deep venous thrombosis of right calf

762013 285401000119104 Acute deep venous thrombosis of right femoral vein

762018 285521000119107 Acute deep venous thrombosis of right internal jugular vein

762419 350311000119101 Acute deep venous thrombosis of right lower extremity

762005 285281000119108 Acute deep venous thrombosis of right upper extremity

44782745 132271000119105 Acute deep venous thrombosis of thigh

44782744 132261000119104 Acute deep venous thrombosis of tibial vein

762026 285631000119109 Acute deep venous thrombosis of tibial vein of left leg

765156 285641000119100 Acute deep venous thrombosis of tibial vein of right leg

44782421 132321000119103 Acute deep venous thrombosis of upper extremity

764016 449691000124103 Acute deep venous thrombosis of upper extremity after coronary artery bypass graft

44782766 136771000119104 Acute deep venous thrombosis of upper extremity as complication of procedure

762048 286421000119101 Acute thrombosis of left subclavian vein

45757410 133421000119101 Acute thrombosis of mesenteric vein

762049 286431000119103 Acute thrombosis of right subclavian vein

36712892 143561000119108 Acute thrombosis of splenic vein

44782762 132611000119104 Acute thrombosis of subclavian vein

435887 49956009 Antepartum deep vein thrombosis

4179911 297156001 Axillary vein thrombosis

37109253 285381000119104 Bilateral acute deep vein thrombosis of femoral veins

40478951 444325005 Bilateral deep vein thrombosis of lower extremities

4042396 16750002 Deep thrombophlebitis

4046884 134399007 Deep vein thrombosis of leg related to air travel

3655221 860699005 Deep vein thrombosis of lower extremity due to intravenous drug use

4133004 128053003 Deep venous thrombosis

4181315 428781001 Deep venous thrombosis associated with coronary artery bypass graft

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 37/48

438820 56272000 Deep venous thrombosis in puerperium

45773536 703277001 Deep venous thrombosis of femoropopliteal vein

763942 448841000124100 Deep venous thrombosis of left lower extremity

761980 25820001000004100 Deep venous thrombosis of left upper extremity

443537 404223003 Deep venous thrombosis of lower extremity

4133975 128055005 Deep venous thrombosis of pelvic vein

40480555 443210003 Deep venous thrombosis of peroneal vein

4322565 427775006 Deep venous thrombosis of profunda femoris vein

763941 448831000124105 Deep venous thrombosis of right lower extremity

761928 20850001000004108 Deep venous thrombosis of right upper extremity

4207899 438785004 Deep venous thrombosis of tibial vein

4028057 128054009 Deep venous thrombosis of upper extremity

435565 195437003 Embolism and thrombosis of the vena cava

40481089 444816006 Embolism from thrombosis of vein of lower extremity

4119760 234044007 Iliofemoral deep vein thrombosis

4124856 234041004 Inferior mesenteric vein thrombosis

4096099 25114006 Phlebitis of deep veins of lower extremity

4281689 66923004 Phlegmasia alba dolens

4284538 66877004 Phlegmasia cerulea dolens

4309333 213220000 Postoperative deep vein thrombosis

46285905 978441000000108 Provoked deep vein thrombosis

46271900 710167004 Recurrent deep vein thrombosis

4033521 14534009 Splenic vein thrombosis

4055089 197001004 Superior mesenteric vein thrombosis

4230403 438646004 Thrombophlebitis of axillary vein

4069561 1748006 Thrombophlebitis of deep femoral vein

761831 16014391000119106 Thrombophlebitis of deep vein of bilateral lower limbs

761830 16014351000119101 Thrombophlebitis of deep vein of left lower limb

761808 16006271000119105 Thrombophlebitis of deep vein of left upper limb

761832 16014431000119101 Thrombophlebitis of deep vein of right lower limb

761809 16006311000119105 Thrombophlebitis of deep vein of right upper limb

4221821 40198004 Thrombophlebitis of deep veins of lower extremity

440750 95452006 Thrombophlebitis of deep veins of upper extremities

4176614 42861008 Thrombophlebitis of iliac vein

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 38/48

761821 16012151000119109 Thrombophlebitis of left deep femoral vein

761819 16012071000119101 Thrombophlebitis of left femoral vein

761820 16012111000119108 Thrombophlebitis of right deep femoral vein

761818 16011991000119109 Thrombophlebitis of right femoral vein

4110339 195412008 Thrombophlebitis of the anterior tibial vein

4111868 195425000 Thrombophlebitis of the common iliac vein

4110343 195427008 Thrombophlebitis of the external iliac vein

439314 195410000 Thrombophlebitis of the femoral vein

4109877 195426004 Thrombophlebitis of the internal iliac vein

4112171 195411001 Thrombophlebitis of the popliteal vein

4112172 195414009 Thrombophlebitis of the posterior tibial vein

4250765 7387004 Thrombophlebitis of tibial vein

42538533 762256003 Thrombosis of iliac vein

44811347 864191000000104 Thrombosis of internal jugular vein

765049 16730001000004104 Thrombosis of left peroneal vein

4317289 95446005 Thrombosis of mesenteric vein

4203836 438647008 Thrombosis of subclavian vein

4175649 427776007 Thrombosis of the popliteal vein

4149782 309735004 Thrombosis of vein of lower limb

4153353 371051005 Traumatic thrombosis of axillary vein

46285904 978421000000101 Unprovoked deep vein thrombosis

77310 266267005 Deep vein phlebitis and thrombophlebitis of the leg

4189004 413956008 Deep vein thrombosis of leg related to intravenous drug use

Table A2 Preliminary code list for pulmonary embolism (PE)

Concept Id SNOMED code Concept name

608954 15964661000119102 Acute cor pulmonale due to septic pulmonary embolism

4120091 233936003 Acute massive pulmonary embolism

45768439 706870000 Acute pulmonary embolism

45768888 707414004 Acute pulmonary thromboembolism

44782732 133971000119108 Chronic pulmonary embolism

45768887 707412000 Chronic pulmonary thromboembolism

45771016 707413005 Chronic pulmonary thromboembolism without pulmonary hypertension

4219469 82153002 Miscarriage with pulmonary embolism

4108681 194883006 Postoperative pulmonary embolus

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 39/48

4091708 280964006 Pulmonary air embolism

440417 59282003 Pulmonary embolism

37109911 723859005 Pulmonary embolism due to and following acute myocardial infarction

45757145 10759311000119104 Pulmonary embolism in childbirth

37016922 713078005 Pulmonary embolism on long-term anticoagulation therapy

43530605 1001000119102 Pulmonary embolism with pulmonary infarction

4119608 233938002 Pulmonary fat embolism

4253796 74315008 Pulmonary microemboli

45766471 703636009 Pulmonary oil microembolism

4121618 233935004 Pulmonary thromboembolism

4119610 233940007 Pulmonary tumor embolism

4236271 438773007 Recurrent pulmonary embolism

36713113 328511000119109 Saddle embolus of pulmonary artery

35615055 15964701000119109 Saddle embolus of pulmonary artery with acute cor pulmonale

40479606 441557008 Septic pulmonary embolism

4119607 233937007 Subacute massive pulmonary embolism

4119609 233939005 Subacute pulmonary fat embolism

Table A3 Preliminary code list for pelvic venous thrombosis (PVT)

Concept Id SNOMED Code Concept Name

762148 293471000119106 Acute deep vein thrombosis of bilateral iliac veins

35616028 293491000119107 Acute deep vein thrombosis of left iliac vein

35616027 293481000119109 Acute deep vein thrombosis of right iliac vein

765155 285441000119102 Acute deep venous thrombosis of bilateral ileofemoral veins

761461 15712201000119101 Acute deep venous thrombosis of bilateral pelvic veins

762015 285451000119100 Acute deep venous thrombosis of ileofemoral vein of left leg

765541 285461000119103 Acute deep venous thrombosis of ileofemoral vein of right lower extremity

44782748 132301000119107 Acute deep venous thrombosis of iliofemoral vein

761462 15712241000119104 Acute deep venous thrombosis of left pelvic vein

44782761 132601000119102 Acute deep venous thrombosis of pelvic vein

765229 15712281000119109 Acute deep venous thrombosis of right pelvic vein

608965 15968901000119104 Bilateral iliac vein thrombophlebitis

765152 293441000119104 Chronic deep vein thrombosis of bilateral iliac veins

35616026 293461000119100 Chronic deep vein thrombosis of left iliac vein

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 40/48

761439 15711001000119104 Chronic deep vein thrombosis of left pelvic vein

46271548 709687000 Chronic deep vein thrombosis of pelvic vein

35616025 293451000119102 Chronic deep vein thrombosis of right iliac vein

761441 15711081000119107 Chronic deep vein thrombosis of right pelvic vein

765542 285471000119109 Chronic deep venous thrombosis of bilateral ileofemoral veins

761440 15711041000119102 Chronic deep venous thrombosis of bilateral pelvic veins

44782740 132201000119100 Chronic deep venous thrombosis of iliofemoral vein

765543 285481000119107 Chronic deep venous thrombosis of left ileofemoral vein

762016 285491000119105 Chronic deep venous thrombosis of right ileofemoral vein

761013 132541000119101 Deep venous thrombosis of bilateral pelvic veins

4133975 128055005 Deep venous thrombosis of pelvic vein

4119760 234044007 Iliofemoral deep vein thrombosis

608964 15968861000119105 Left iliac vein thrombophlebitis

4158798 361278002 Mondor's phlebitis of the penis

4285751 67486009 Pelvic thrombophlebitis in puerperium

608963 15968821000119100 Right iliac vein thrombophlebitis

4176614 42861008 Thrombophlebitis of iliac vein

4317290 95449003 Thrombophlebitis of pelvic vein

4111868 195425000 Thrombophlebitis of the common iliac vein

4110343 195427008 Thrombophlebitis of the external iliac vein

4109877 195426004 Thrombophlebitis of the internal iliac vein

201045 26373009 Thrombosed external hemorrhoids

195294 75955007 Thrombosed hemorrhoids

608966 15969021000119101 Thrombosed internal hemorrhoid grade IV

201595 52931009 Thrombosed internal hemorrhoids

42538533 762256003 Thrombosis of iliac vein

606527 1145183007 Thrombosis of pampiniform plexus

4319327 95448006 Thrombosis of pelvic vein

4295878 76598006 Thrombosis of penile vein

762443 368351000119106 Thrombosis of superficial vein of penis

Table A4 Preliminary code list for splanchic vein thrombosis, including hepatic and extrahepatic (STV)

Concept Id SNOMED Code Concept Name

45757410 133421000119101 Acute thrombosis of mesenteric vein

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 41/48

36712892 143561000119108 Acute thrombosis of splenic vein

196715 82385007 Budd-Chiari syndrome

45757409 133411000119108 Chronic thrombosis of mesenteric vein

36712891 143551000119106 Chronic thrombosis of splenic vein

4301208 38739001 Hepatic vein thrombosis

4124856 234041004 Inferior mesenteric vein thrombosis

199837 17920008 Portal vein thrombosis

4033521 14534009 Splenic vein thrombosis

4055089 197001004 Superior mesenteric vein thrombosis

4318407 95447001 Thrombophlebitis of mesenteric vein

4317289 95446005 Thrombosis of mesenteric vein

Table A5 Preliminary code list for retinal vein thrombosis, including retinal central vein thrombosis (RVT)

Concept Id Concept Code Concept Name

4339013 232048009 Branch retinal vein occlusion with macular edema

4334248 232046008 Branch retinal vein occlusion with neovascularization

4199035 314000002 Branch retinal vein occlusion with no neovascularization

313761 68478007 Central retinal vein occlusion

4208221 312997008 Central retinal vein occlusion - ischemic

4339011 232040002 Central retinal vein occlusion - juvenile

4335591 232042005 Central retinal vein occlusion - juvenile with macular edema

4334888 232041003 Central retinal vein occlusion - juvenile with neovascularization

4208222 312998003 Central retinal vein occlusion - non-ischemic

4339010 232039004 Central retinal vein occlusion with macular edema

4334246 232038007 Central retinal vein occlusion with neovascularization

42535735 733325006 Combined occlusion by thrombus of retinal artery and retinal vein

4334247 232043000 Hemispheric retinal vein occlusion

4335592 232045007 Hemispheric retinal vein occlusion with macular edema

4336005 232044006 Hemispheric retinal vein occlusion with neovascularization

4216561 71719003 Thrombophlebitis of retinal vein

4187790 46085004 Thrombosis of retinal vein

312622 24596005 Venous retinal branch occlusion

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 42/48

Table A6 Preliminary code list for disseminated intravascular coagulation (DIC)

Concept Id Concept Code Concept Name

436093 67406007 Disseminated intravascular coagulation

4028488 13507004 Purpura fulminans

Appendix II

Preliminary code list for selected cancer types is attached as a standalone document

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 43/48

Appendix III: ENCePP checklist for study protocols

ENCePP Checklist for Study Protocols (Revision 4)

Study title: DARWIN EU® – Incidence rates of venous thromboembolic events in cancer patients

EU PAS Register® number: N/A

Study reference number (if applicable): P3-C3-005

Section 1: Milestones  Yes  No  N/A  Section

Number  1. Does the protocol specify

timelines for  

5. Milestones,  

8.2 Data

Sources 

1.1.1 Start of data collection       

1.1.2 End of data collection  X     

1.1.3 Progress report(s)       

1.1.4 Interim report(s)       

1.1.5 Registration in the EU PAS Register®       

1.1.6 Final report of study results.       

Comments: 

Section 2: Research question  Yes  No  N/A  Section

Number  2.1 Does the formulation of the research question and

objectives clearly explain:        

7. Research

question and

objectives  8. Research

methods

2.1.1 Why the study is conducted? (e.g. to address an

important public health concern, a risk identified in the

risk management plan, an emerging safety issue)  X     

2.1.2 The objective(s) of the study?       

2.1.3 The target population? (i.e. population or subgroup

to whom the study results are intended to be

generalised)       

2.1.4 Which hypothesis(-es) is (are) to be tested?       

2.1.5 If applicable, that there is no a priori hypothesis?       

Comments: 

Section 3: Study design  Yes  No  N/A  Section

Number  3.1 Is the study design described? (e.g. cohort, case-control,

cross-sectional, other design)   X      8.1 Study type

and Study

Design 

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

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3.2 Does the protocol specify whether the study is based on

primary, secondary or combined data collection?   X     8.2 Study

Setting and

Data Sources  3.3 Does the protocol specify measures of occurrence?

(e.g., rate, risk, prevalence)   X     8.8 Analysis 

3.4 Does the protocol specify measure(s) of association?

(e.g. risk, odds ratio, excess risk, rate ratio, hazard ratio,

risk/rate difference, number needed to harm (NNH))   X     8.8 Analysis 

3.5 Does the protocol describe the approach for the collection

and reporting of adverse events/adverse reactions?

(e.g. adverse events that will not be collected in case of

primary data collection) 

    X   

Comments: 

Section 4: Source and study populations  Yes  No  N/A  Section Number 

4.1 Is the source population described?  X     

8.5 Study

Population  4.2 Is the planned study population defined in terms

of:         

4.2.1 Study time period        8.3 Study Period 

4.2.2 Age and sex   X    

8.6.3. Other

covariates  4.2.3 Country of origin 

      8.2 Study Setting

and Data Sources  4.2.4 Disease/indication        8.6.1. Exposures 

4.2.5 Duration of follow-up        8.4 Follow-up 

4.3 Does the protocol define how the study

population will be sampled from the source

population? (e.g. event or inclusion/exclusion

criteria) 

 X    

8.5 Study

Population with

inclusion and

exclusion criteria  Comments: 

Section 5: Exposure definition and measurement  Yes  No  N/A  Section

Number  5.1 Does the protocol describe how the study exposure is

defined and measured? (e.g. operational details for defining and

categorising exposure, measurement of dose and duration of

drug exposure) 

     X

5.2 Does the protocol address the validity of the exposure

measurement? (e.g. precision, accuracy, use of validation sub-

study)       X  

5.3 Is exposure categorised according to time windows?         X

5.4 Is intensity of exposure addressed?   (e.g. dose, duration) 

      X

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

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DARWIN EU® Coordination Centre 45/48

5.5 Is exposure categorised based on biological mechanism of

action and taking into account the pharmacokinetics and

pharmacodynamics of the drug?       X  

5.6 Is (are) (an) appropriate comparator(s) identified?        X

Comments: 

Section 6: Outcome definition and measurement  Yes  No  N/A  Section

Number  6.1 Does the protocol specify the primary and secondary (if

applicable) outcome(s) to be investigated?    X     8.6.2.

Outcomes 6.2 Does the protocol describe how the outcomes are defined

and measured?     X     8.6.2.

Outcomes 6.3 Does the protocol address the validity of outcome

measurement? (e.g. precision, accuracy, sensitivity, specificity,

positive predictive value, use of validation sub-study)      X    

6.4 Does the protocol describe specific outcomes relevant for

Health Technology Assessment? (e.g. HRQoL, QALYs, DALYS,

health care services utilisation, burden of disease or treatment,

compliance, disease management) 

    X    

Comments: 

Section 7: Bias  Yes  No  N/A  Section

Number  7.1 Does the protocol address ways to measure confounding?

(e.g. confounding by indication)       X

7.2 Does the protocol address selection bias? (e.g. healthy

user/adherer bias)       X

7.3 Does the protocol address information bias?

(e.g. misclassification of exposure and outcomes, time-related

bias)       X

Comments: 

Section 8: Effect measure modification  Yes  No  N/A  Section

Number  8.1 Does the protocol address effect modifiers?

(e.g. collection of data on known effect modifiers, sub-group

analyses, anticipated direction of effect)        X

Comments: 

Section 9: Data sources  Yes  No  N/A  Section

Number  9.1 Does the protocol describe the data source(s) used in the

study for the ascertainment of:         

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

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DARWIN EU® Coordination Centre 46/48

9.1.1 Exposure? (e.g. pharmacy dispensing, general practice

prescribing, claims data, self-report, face-to-face interview)        X

9.1.2 Outcomes? (e.g. clinical records, laboratory markers or

values, claims data, self-report, patient interview including scales

and questionnaires, vital statistics)    X    

8.2 Study

Setting and

Data Sources  9.1.3 Covariates and other characteristics? 

 X     8.6.3. Other

covariates  9.2 Does the protocol describe the information available from

the data source(s) on:         

9.2.1 Exposure? (e.g. date of dispensing, drug quantity, dose,

number of days of supply prescription, daily dosage,  prescriber)        X

9.2.2 Outcomes? (e.g. date of occurrence, multiple event,

severity measures related to event)       X

9.2.3 Covariates and other characteristics? (e.g. age, sex, clinical

and drug use history, co-morbidity, co-medications, lifestyle)   X     8.2 Study

Setting and

Data Sources  9.3 Is a coding system described for:          

9.3.1 Exposure? (e.g. WHO Drug Dictionary, Anatomical

Therapeutic Chemical (ATC) Classification System)        X

9.3.2 Outcomes? (e.g. International Classification of Diseases

(ICD), Medical Dictionary for Regulatory Activities (MedDRA))    X     8.6.2.

Outcomes 9.3.3 Covariates and other characteristics? 

 X     8.6.3. Other

covariates  9.4 Is a linkage method between data sources described?

(e.g. based on a unique identifier or other)        X  

Comments: 

Section 10: Analysis plan  Yes  No  N/A  Section

Number  10.1 Are the statistical methods and the reason for their choice

described?    X     8.8 Analysis 

10.2 Is study size and/or statistical precision estimated?       X  

10.3 Are descriptive analyses included?   X    

8.8.2

Descriptive

statistics  10.4 Are stratified analyses included?   X     8.8 Analysis 

10.5 Does the plan describe methods for analytic control of

confounding?       X

10.6 Does the plan describe methods for analytic control of

outcome misclassification?       X

10.7 Does the plan describe methods for handling missing

data?       X  

10.8 Are relevant sensitivity analyses described?       X

Comments: 

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 47/48

Section 11: Data management and quality control  Yes  No  N/A  Section

Number  11.1 Does the protocol provide information on data storage?

(e.g. software and IT environment, database maintenance and

anti-fraud protection, archiving)   X    

9. Data

management 

11.2 Are methods of quality assurance described?   X    

10. Quality

Control  11.3 Is there a system in place for independent review of

study results?        X  

Comments: 

Section 12: Limitations  Yes  No  N/A  Section 

Number  12.1 Does the protocol discuss the impact on the study

results of:       

11. Limitations of

the research

methods 

12.1.1 Selection bias?       

12.1.2 Information bias?   X    

12.1.3 Residual/unmeasured confounding?  (e.g. anticipated direction and magnitude of such biases,

validation sub-study, use of validation and external data,

analytical methods). 

12.2 Does the protocol discuss study feasibility? (e.g. study

size, anticipated exposure uptake, duration of follow-up in a

cohort study, patient recruitment, precision of the estimates)   X    

Table

8.2. Description of

the selected Data

Sources. Comments: 

Section 13: Ethical/data protection issues  Yes  No  N/A  Section 

Number  13.1 Have requirements of Ethics Committee/ Institutional

Review Board been described?   X    

13. Governance

board aspects  13.2 Has any outcome of an ethical review procedure been

addressed?       X

13.3 Have data protection requirements been described?   X    

9.2 Data storage

and protection  Comments: 

Section 14: Amendments and deviations  Yes  No  N/A  Section

Number  14.1 Does the protocol include a section to document

amendments and deviations?    X     4.

Amendments

and updates  Comments: 

D2.2.2 - Study Protocol for P3-C3-005

Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0

Dissemination level: Confidential

DARWIN EU® Coordination Centre 48/48

Section 15: Plans for communication of study results  Yes  No  N/A  Section

Number  15.1 Are plans described for communicating study results

(e.g. to regulatory authorities)?    X    

14. Plans for

disseminating

and

communicating

study results  15.2 Are plans described for disseminating study results

externally, including publication?   X    

14. Plans for

disseminating

and

communicating

study results  Comments: 

Name of the main author of the protocol: Talita Duarte-Salles & Anton Barchuk

Date: 30/10/2024

Signature: