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 This study aims to estimate incidence rates of venous thromboembolic
and objectives 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 / Talita Duarte-Salles Erasmus MC
Principal Investigator
Anton Barchuk
Data Scientist Cesar Barboza Erasmus MC
Ger Inberg
Maarten van Kessel
Adam Black
Ross Williams
Epidemiologist Berta Raventós Erasmus MC
Julieta Politi
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 Danish Medicines Agency
Tine Iskov Kopp
Cæcilie Brinth Christiansen
EBB Marek Oja
Estonian Biobank, Estonia
Raivo Kolde
FinOMOP-HILMO Anna Hammais Finnish Care Register for Health
Gustav Klingstedt Care, Finland
FinOMOP-HUS Eric Fey
Hospital District of Helsinki and
Kimmo Porkka
Uusimaa, Finland
Tiina Wahlfors
IQVIA DA Germany and IQVIA LPD Gargi Jadhav
Belgium
Isabella Kacmarczyl
Akram Mendez IQVIA
Hanne van Ballegooijen
Dina Vojinovic
IPCI Katia Verhamme Integrated Primary Care
Information, Netherlands
SIDIAP Anna Palomar-Cros
Irene López-Sánchez IDIAPJGol
Agustina Giuliodori
*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-
The study population will include all individuals aged 18 years and above
exclusion criteria):
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 For each outcome, study participants will be followed up from the date of
ends): 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-
The study population will include all individuals aged 18 years and above
exclusion criteria):
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 Medical History at the index date and medication at the index date and
ends): 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 Population-level cohort Off the shelf
epidemiology
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 Type of Data Number of Data lock for
setting active the last
subjects update
GB CPRD GOLD Covers primary care setting, data on Primary care, EHRs 17.5 M 2024-04-15
cancer diagnoses, comorbidities, hospital care
medications, and date of death. (OP)
DK DK-DHR National health data database All settings EHRs, 8.5 M 2024-05-15
which includes information on registries,
cancer diagnoses, staging and claims
medical history
EE EBB Contains health insurance claims, Primary care, EHRs, claims, 0.2 M 2023-03-20
digital prescriptions, discharge hospital care (IP registries,
information and causes of death and OP) biobank
through linkage with the national
death register. Data is linked to
cancer registry.
FI FinOMOP - HILMO Nation-wide hospital registry data Hospital care (IP EHRs, 7.1 M 2024-06-24
with high-quality information on and OP) registries
cancer diagnoses and mortality.
FI FinOMOP - HUS Hospital registry which includes Hospital care (IP EHRs 3.5 M 2024-05-03
information on cancer patients and and OP)
medical history.
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Country Name of Database Justification for Inclusion Health Care Type of Data Number of Data lock for
setting active the last
subjects update
DE IQVIA DA Germany Covers primary care setting with Primary care EHRs 43.1 M 2024-03-25
information on cancer diagnoses
and medical history.
BE IQVIA LPD Belgium Covers primary care setting with Primary care EHRs 1.1 M 2024-03-25
information on cancer diagnoses
and medical history.
NL IPCI Covers primary care setting, data on Primary care EHRs 2.9 M 2024-08-29
cancer diagnoses previously
validated, information available on
comorbidities, medications, and
date of death.
ES SIDIAP Covers primary care setting with Primary care EHRs 8.6 M 2023-03-20
information on cancer diagnoses
and medical history.
GB UKBB Genetic data on biobank Primary care, EHRs, 0.5 M 2024-02-16
participants linked to EHRs from hospital care (IP registries,
primary care, hospitalisations, and OP) biobank
cancer registrations and mortality.
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 Time Anchor Number of Type of Washout Care Code Diagnosis Incident Measureme Source
population Description entries entry window Settin Type2 position with nt of
name(s) (e.g. time 0) g1 respect characteristi algorith
to… cs/ m
validation
Cancer Date of Single Incident [any IP, SNOMED, Any Any N/A N/A
patients cancer entry time OP ICD-O-3 cancer
diagnosis , -1] , diagnos
OT is
except
non-
melano
ma skin
cancer
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 Assessment Care Code Diagnosis Applied to Measurement Source for
application window Settings¹ Type position study characteristics/ algorithm
populations:
validation
Patients newly Primary selected cancer After - IP, OP, SNOME N/A Cancer N/A N/A
diagnosed with selected OT D, ICD- patients
cancers O-3
Age Participants aged 18 or After At index IP, OP, N/A N/A Cancer N/A N/A
above date OT patients
Minimum prior Only participants with a Before 365 days OP, OT N/A N/A Cancer N/A N/A
observation period of minimum observation patients
365 days period of 365 days prior to
diagnosis of
chondrosarcoma (index
date) s
Minimum potential Only participants with a After [0, 365] IP, OP, N/A N/A Cancer N/A N/A
follow-up time cancer diagnosis (index OT patients
date) occurring one year
prior to end of data
availability in the database
will be included
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 Assessment Care Code Diagnosis Applied to Measurement Source
application window Settings¹ Type position study characteristics/ for
populations: validation algorithm
History of cancer Participants with a diagnosis of After Any time OP, IP, SNOMED, Any Cancer N/A N/A
diagnosis cancer (any, excluding non- prior to OT ICD-O-3 patients
melanoma skin cancer) any cancer
time prior index date diagnosis
Multiple primary Participants with a two After At index OP, IP, SNOMED, Any Cancer N/A N/A
tumors diagnoses of cancer (any, date OT ICD-O-3 patients
excluding non-melanoma skin
cancer) at index date
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 Details Primary Type of Washout Care Code Type Diagnosis Applied to Measurement Source of algorithm
name outcome outcome window Settings¹ Position study characteristics/
populations validation
DVT See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
PE See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
VTE See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
PVT See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
SVT See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
RTV See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
DIC See Appendix Yes Binary [-365,-1] IP, OP, OT SNOMED Any Cancer patients N/A N/A
I
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 Assessment Care Cod Diag Applied to Meas Sourc
variable window Settings e nosis study ureme e for
¹ Typ Posit population nt algorit
e ion s charac hm
teristi
cs/
valida
tion
Co- Large-scale patient- Binary [0,0] IP, OP, SNO N/A Cancer N/A N/A
morbidities level OT ME patients
characterisation D
with regard to
underlying
comorbidities
Medication Large-scale patient- Binary [0,0], [1,90] IP, OP, RxN N/A Cancer N/A N/A
use level OT orm patients
characterisation
with regard to use
of concomitant
drugs
1 IP = inpatient, OP = outpatient, OT = other, n/a = not applicable
8.
Table 8. Operational definitions of covariates.
Characteristic Details Type of Assessment Care Cod Diag Applied to Meas Sourc
variable window Settings e nosis study ureme e for
¹ Typ Posit population nt algorit
e ion s charac hm
teristi
cs/
valida
tion
Co- Large-scale patient- Binary [0,0] IP, OP, SNO N/A Cancer N/A N/A
morbidities level OT ME patients
characterisation D
with regard to
underlying
comorbidities
Medication Large-scale patient- Binary [0,0], [1,90] IP, OP, RxN N/A Cancer N/A N/A
use level OT orm patients
characterisation
with regard to use
of concomitant
drugs
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
Study type Type of analyses
classification
Patient-level Off-the-shelf - Large-scale characterisation
characterisation
- Patient-level characteristics
- Prognosis / progression to a pre-specified outcome
- Standard care description
Population-level Off-the-shelf - Incidence rates of the condition of interest
descriptive
- Prevalence rates of the condition of interest
epidemiology
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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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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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D2.2.2 - Study Protocol for P3-C3-005
Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
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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D2.2.2 - Study Protocol for P3-C3-005
Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
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
DARWIN EU® Coordination Centre 42/48
D2.2.2 - Study Protocol for P3-C3-005
Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
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
1.1.1 Start of data collection
5. Milestones,
1.1.2 End of data collection X
8.2 Data
1.1.3 Progress report(s)
Sources
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:
2.1.1 Why the study is conducted? (e.g. to address an
important public health concern, a risk identified in the X 7. Research
risk management plan, an emerging safety issue) question and
2.1.2 The objective(s) of the study? objectives
2.1.3 The target population? (i.e. population or subgroup
8. Research
to whom the study results are intended to be
methods
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, 8.1 Study type
cross-sectional, other design) X and Study
Design
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Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
3.2 Does the protocol specify whether the study is based on 8.2 Study
primary, secondary or combined data collection? X Setting and
Data Sources
3.3 Does the protocol specify measures of occurrence?
X 8.8 Analysis
(e.g., rate, risk, prevalence)
3.4 Does the protocol specify measure(s) of association?
(e.g. risk, odds ratio, excess risk, rate ratio, hazard ratio, X 8.8 Analysis
risk/rate difference, number needed to harm (NNH))
3.5 Does the protocol describe the approach for the collection
and reporting of adverse events/adverse reactions?
X
(e.g. adverse events that will not be collected in case of
primary data collection)
Comments:
Section 4: Source and study populations Yes No N/A Section Number
4.1 Is the source population described? 8.5 Study
X
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 8.6.3. Other
X
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 8.5 Study
population will be sampled from the source Population with
X
population? (e.g. event or inclusion/exclusion inclusion and
criteria) 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
X
categorising exposure, measurement of dose and duration of
drug exposure)
5.2 Does the protocol address the validity of the exposure
measurement? (e.g. precision, accuracy, use of validation sub- X
study)
5.3 Is exposure categorised according to time windows? X
5.4 Is intensity of exposure addressed?
X
(e.g. dose, duration)
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Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
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5.5 Is exposure categorised based on biological mechanism of
action and taking into account the pharmacokinetics and X
pharmacodynamics of the drug?
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 8.6.2.
X
applicable) outcome(s) to be investigated? Outcomes
6.2 Does the protocol describe how the outcomes are defined 8.6.2.
X
and measured? Outcomes
6.3 Does the protocol address the validity of outcome
measurement? (e.g. precision, accuracy, sensitivity, specificity, X
positive predictive value, use of validation sub-study)
6.4 Does the protocol describe specific outcomes relevant for
Health Technology Assessment? (e.g. HRQoL, QALYs, DALYS,
X
health care services utilisation, burden of disease or treatment,
compliance, disease management)
Comments:
Section 7: Bias Yes No N/A Section
Number
7.1 Does the protocol address ways to measure confounding?
X
(e.g. confounding by indication)
7.2 Does the protocol address selection bias? (e.g. healthy
X
user/adherer bias)
7.3 Does the protocol address information bias?
(e.g. misclassification of exposure and outcomes, time-related X
bias)
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 X
analyses, anticipated direction of effect)
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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D2.2.2 - Study Protocol for P3-C3-005
Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
9.1.1 Exposure? (e.g. pharmacy dispensing, general practice
X
prescribing, claims data, self-report, face-to-face interview)
9.1.2 Outcomes? (e.g. clinical records, laboratory markers or 8.2 Study
values, claims data, self-report, patient interview including scales X Setting and
and questionnaires, vital statistics) Data Sources
9.1.3 Covariates and other characteristics? 8.6.3. Other
X
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,
X
number of days of supply prescription, daily dosage, prescriber)
9.2.2 Outcomes? (e.g. date of occurrence, multiple event,
X
severity measures related to event)
9.2.3 Covariates and other characteristics? (e.g. age, sex, clinical 8.2 Study
and drug use history, co-morbidity, co-medications, lifestyle) X Setting and
Data Sources
9.3 Is a coding system described for:
9.3.1 Exposure? (e.g. WHO Drug Dictionary, Anatomical
X
Therapeutic Chemical (ATC) Classification System)
9.3.2 Outcomes? (e.g. International Classification of Diseases 8.6.2.
X
(ICD), Medical Dictionary for Regulatory Activities (MedDRA)) Outcomes
9.3.3 Covariates and other characteristics? 8.6.3. Other
X
covariates
9.4 Is a linkage method between data sources described?
X
(e.g. based on a unique identifier or other)
Comments:
Section 10: Analysis plan Yes No N/A Section
Number
10.1 Are the statistical methods and the reason for their choice
X 8.8 Analysis
described?
10.2 Is study size and/or statistical precision estimated? X
10.3 Are descriptive analyses included? 8.8.2
X Descriptive
statistics
10.4 Are stratified analyses included? X 8.8 Analysis
10.5 Does the plan describe methods for analytic control of
X
confounding?
10.6 Does the plan describe methods for analytic control of
X
outcome misclassification?
10.7 Does the plan describe methods for handling missing
X
data?
10.8 Are relevant sensitivity analyses described? X
Comments:
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D2.2.2 - Study Protocol for P3-C3-005
Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
Section 11: Data management and quality control Yes No N/A Section
Number
11.1 Does the protocol provide information on data storage?
9. Data
(e.g. software and IT environment, database maintenance and X
management
anti-fraud protection, archiving)
11.2 Are methods of quality assurance described? 10. Quality
X
Control
11.3 Is there a system in place for independent review of
X
study results?
Comments:
Section 12: Limitations Yes No N/A Section
Number
12.1 Does the protocol discuss the impact on the study
results of:
12.1.1 Selection bias?
11. Limitations of
12.1.2 Information bias? X
the research
12.1.3 Residual/unmeasured confounding? methods
(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 Table
size, anticipated exposure uptake, duration of follow-up in a 8.2. Description of
X
cohort study, patient recruitment, precision of the estimates) 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 13. Governance
X
Review Board been described? board aspects
13.2 Has any outcome of an ethical review procedure been
X
addressed?
13.3 Have data protection requirements been described? 9.2 Data storage
X
and protection
Comments:
Section 14: Amendments and deviations Yes No N/A Section
Number
14.1 Does the protocol include a section to document 4.
amendments and deviations? X Amendments
and updates
Comments:
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D2.2.2 - Study Protocol for P3-C3-005
Author(s): A. Barchuk, T. Duarte-Salles Version: 1.0
Dissemination level: Confidential
Section 15: Plans for communication of study results Yes No N/A Section
Number
15.1 Are plans described for communicating study results 14. Plans for
(e.g. to regulatory authorities)? disseminating
X and
communicating
study results
15.2 Are plans described for disseminating study results 14. Plans for
externally, including publication? disseminating
X 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 48/48
Saatja: "Raivo Kolde" <
[email protected]>
Saaja:
[email protected]
Teema: Taotlus Eesti bioeetika ja inimuuringute nõukogule (EBIN-le) uurimisprotokolli kooskõlastamiseks
Kuupäev: 2024-11-01 13:03
Tere
Sooviksime EMA DARWIN projekti raames (raamtaotluse otsus nr 1.1-12/1832), kiirkooskõlastada järgmise uuringu protokolli. Täpsemalt on eesmärgiks kirjeldada tromboembolismi levikut vähipatsientidel.
Lugupidamisega,
Raivo Kolde