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Saatja: "Kaia Kask" <[email protected]> Saaja: "Info - SOM" <[email protected]> Teema: Kiirmenetlus uurimisprotokolli DARWIN thromboembolic events kooskõlastamiseks Kuupäev: 2025-10-23 12:45 Tähelepanu! Tegemist on välisvõrgust saabunud kirjaga. Tundmatu saatja korral palume linke ja faile mitte avada. Tere! Euroopa ravimiameti (EMA) DARWIN projektile on EBIN väljastanud raamloa (otsus nr 1.1-12/1832), kuid iga uuringu jaoks on vaja kooskõlastada kiirmenetlusega ka uuringu protokoll. Seekord soovib EMA uurida „Time to onset of thromboembolic events in adults with selected types of cancer“. Vastava uuringu protokoll on manuses ja me sooviks seda kiirmenetluse korras kooskõlastada. Palun lisage kooskõlastuse konteinerisse ka kaasa pandud protokoll. Ette tänades Kaia Kask, PhD Juhiabi/Assistant Andmeteaduse õppetool/Chair of Data Science Arvutiteaduse instituut/ Institute of Computer Science Tartu Ülikool/ University of Tartu Narva mnt 18 51009 Tartu ESTONIA Study Protocol P4-C2-017 DARWIN EU® - Time to onset of thromboembolic events in adults with selected types of cancer 1 0 / 10 / 2025 Versio n 1.0 Authors: Melissa Leung, Cesar Barboza, Ionna Nika, Anton Barchuk, Talita Duarte-Salles Confidential CONTENTS TOC \o "1-7" \h \z \u LIST OF ABBREVIATIONS PAGEREF _Toc211005564 \h 5 1. TITLE PAGEREF _Toc211005565 \h 7 2. DESCRIPTION OF THE STUDY TEAM PAGEREF _Toc211005566 \h 7 3. ABSTRACT PAGEREF _Toc211005567 \h 8 4. AMENDMENTS AND UPDATES PAGEREF _Toc211005568 \h 11 5. MILESTONES PAGEREF _Toc211005569 \h 11 6. RATIONALE AND BACKGROUND PAGEREF _Toc211005570 \h 11 7. RESEARCH QUESTION AND OBJECTIVES PAGEREF _Toc211005571 \h 11 8. RESEARCH METHODS PAGEREF _Toc211005572 \h 11 8.1. Study design PAGEREF _Toc211005573 \h 11 Figure 1. Graphical depiction of the study design. PAGEREF _Toc211005574 \h 12 8.2. Follow-up PAGEREF _Toc211005575 \h 12 8.3. Study population with inclusion and exclusion criteria PAGEREF _Toc211005576 \h 12 8.4. Study setting and data sources PAGEREF _Toc211005577 \h 13 Table 1. Data sources. PAGEREF _Toc211005578 \h 13 8.5. Study period PAGEREF _Toc211005579 \h 14 8.6. Variables PAGEREF _Toc211005580 \h 14 8.6.1. Exposure PAGEREF _Toc211005581 \h 14 8.6.2. Outcome PAGEREF _Toc211005582 \h 14 8.6.3. Intercurrent events (only for causal studies) PAGEREF _Toc211005583 \h 14 8.6.4. Covariates, including confounders, effect modifiers, and other variables PAGEREF _Toc211005584 \h 14 8.7. Study size PAGEREF _Toc211005585 \h 15 8.8. Analysis PAGEREF _Toc211005586 \h 15 8.8.1. Federated network analyses PAGEREF _Toc211005587 \h 15 8.8.2. Data privacy protection PAGEREF _Toc211005588 \h 15 8.8.3. Statistical model specification and assumptions of the analytical approach considered PAGEREF _Toc211005589 \h 15 8.8.4. Output PAGEREF _Toc211005590 \h 16 Table 1. Attrition of study participants. PAGEREF _Toc211005591 \h 17 Figure 1. Cumulative probability of not having thromboembolic event after the first cancer diagnosis accounting for a competing risk of death. PAGEREF _Toc211005592 \h 19 Table 2. Median time in days (95% CI) to thromboembolic event after first cancer diagnosis. PAGEREF _Toc211005593 \h 20 9. STRENGTHS AND LIMITATIONS PAGEREF _Toc211005594 \h 20 10. REFERENCES PAGEREF _Toc211005595 \h 21 11. ANNEXES PAGEREF _Toc211005596 \h 22 ANNEX I. Description of data sources PAGEREF _Toc211005597 \h 22 ANNEX II. Fitness for use assessment PAGEREF _Toc211005598 \h 33 ANNEX III. Operational and reporting considerations PAGEREF _Toc211005599 \h 37 ANNEX IV. List of stand-alone documents PAGEREF _Toc211005600 \h 39 Table S1. List of concepts used to define deep vein thrombosis (DVT). PAGEREF _Toc211005601 \h 39 Table S2. List of concepts used to define pulmonary embolism (PE). PAGEREF _Toc211005602 \h 43 Table S3. List of concepts used to define venous thromboembolism (VTE). PAGEREF _Toc211005603 \h 43 Table S4. List of concepts used to define pelvic vein thrombosis (PVT) (concept sets included all descendants of listed concepts). PAGEREF _Toc211005604 \h 48 Table S5. List of concepts used to splanchnic vein thrombosis (SVT). PAGEREF _Toc211005605 \h 49 Table S6. List of concepts used to define retinal vein thrombosis (RVT). PAGEREF _Toc211005606 \h 49 Table S7. List of concepts used to define disseminated intravascular coagulation (DIC). PAGEREF _Toc211005607 \h 50 ANNEX V. ENCePP checklist for study protocols PAGEREF _Toc211005608 \h 52 ANNEX VI. Glossary PAGEREF _Toc211005609 \h 58 Study title DARWIN EU® - Time to onset of thromboembolic events in adult s with selected types of cancer Protocol version V 1.0 Date 1 0 /10/ 2025 EUPAS number Study not registered yet Active substance None Medicinal product None Research question and objectives The aim of this study is to estimate time to onset of venous thromboembolic events in adults with each type of selected cancer. The specific objectives of the study are: To estimate the probability of not having thromboembolic events at 6-month intervals within 5 years in adult s with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . To estimate median time to onset of venous thromboembolic events in a cohort of adult s with thromboembolic events with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . Countries of study Belgium, Denmark, Estonia, Finland, Germany, The Netherlands, Spain, United Kingdom Authors Melissa Leung ( HYPERLINK "mailto:[email protected]" [email protected] ) Cesar Barboza ( [email protected] ) Ionna Nika ( HYPERLINK "mailto:[email protected]" [email protected] ) Anton Barchuk ( [email protected] ) Talita Duarte-Salles ( [email protected] ) This is a routinely repeated study from P3-C3-005 with EUPAS1000000440 ( https://catalogues.ema.europa.eu/node/4341 ). LIST OF ABBREVIATIONS Acronyms/terms Description ADHD Attention deficit hyperactivity disorder AJCC/UICC American Joint Committee on Cancer and the International Union Against Cancer ATC Anatomical Therapeutic Chemical CDM Common Data Model CI Confidence interval 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 EHR Electronic Health Record EMA European Medicines Agency EBB Estonian Biobank EGCUT Estonian Genome Center at the University of Tartu ENCePP European Network of Centres for Pharmacoepidemiology and Pharmacovigilance EU European Union EUPAS EU Post-Authorisation Studies Register GDPR General Data Protection Regulation GP General Practitioner HIV Human Immunodeficiency Virus 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 IP Inpatient IPCI Integrated Primary Care Information Project IR Incidence rate IRB Institutional Review Board LPD Longitudinal Patient Database OHDSI Observational Health Data Sciences and Informatics OMOP Observational Medical Outcomes Partnership OP Outpatient PE Pulmonary Embolism PVT Pelvic Venous Thrombosis PY Person-years RVT Retinal vein thrombosis SNOMED Systematized Nomenclature of Medicine SVT Splanchnic Vein Thrombosis UKBB UK Biobank VTE Venous Thromboembolism TITLE DARWIN EU® - Time to onset of thromboembolic events in adult s with selected types of cancer DESCRIPTION OF THE STUDY TEAM Study team role Names Organisation Principal Investigator Melissa Leung Anton Barchuk Talita Duarte-Salles Erasmus MC Data Scientist Cesar Barboza Ioanna Nika Erasmus MC Clinical Domain Expert Anton Barchuk Erasmus MC Study Manager Natasha Yefimenko Erasmus MC Data source Names Data Partner Organisation * IQVIA Longitudinal Patient Database Belgium ( IQVIA LPD Belgium ) IQVIA Disease Analyzer Germany ( IQVIA DA Germany ) Gargi Jadhav Isabella Kacmarczyl Akram Mendez Hanne van Ballegooijen Dina Vojinovic IQVIA Danish Data Health Registries ( DK-DHR ) Elvira Bräuner Susanne Bruun Danish Medicines Agency Estonian Biobank ( EBB ) Marek Oja Raivo Kolde Ami Sild Estonian Biobank, Estonia Finnish Care Register for Health Care ( FinOMOP -THL ) Anna Hammais Gustav Klingstedt Finnish Care Register for Health Care, Finland Integrated Primary Care Information ( IPCI ) Katia Verhamme Integrated Primary Care Information, Netherlands The Information System for the Development of Research in Primary Care ( SIDIAP ) Anna Palomar-Cros Irene López-Sánchez Agustina Giuliodori IDIAPJGol Clinical Practice Research Datalink GOLD ( CPRD GOLD ) and UK BioBank ( UKBB ) Antonella Delmestri University of Oxford * Data partners do not have an investigator role. Data partners execute code at their data source, review, and approve their results. ABSTRACT Title DARWIN EU® - Time to onset of thromboembolic events in adult s with selected types of cancer Rationale and background Thromboembolic events are a common complication for individuals with cancer, with risk varying according to the cancer site, suggesting cancer-specific mechanisms playing a role in the occurrence of these events. Haematological malignancies and lung, pancreas, stomach, bowel , and brain cancers are generally associated with a high risk of clot formation, whilst prostate and breast cancers are associated with low risk of thrombosis. When a safety signal of a thromboembolic event appears in cancer populations, it can be challenging to assess a potential association with the oncologic treatment without reliable information on the background risk. This study is intended to address this knowledge gap by generating evidence on the time to onset of different venous thromboembolic events among adult s with selected cancer types. Research question and objectives Research question What was th e t ime to onset of venous thromboembolic events in adults newly diagnosed with each type of selected cancer during the period 2016–2022? Objectives The aim of this study is to estimate time to onset of venous thromboembolic events in adult s with each type of selected cancer. The specific objectives of the study are: To estimate the probability of not having thromboembolic events at 6-month intervals within 5 years in adults with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . To estimate median time to onset of venous thromboembolic events in a cohort of adult s with thromboembolic events with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . Methods Study design Population-based cohort study. The index date, i.e., date of cohort entry, will be the date of the first cancer diagnosis. I ndividuals are followed up until the earliest of occurrence of the outcome , loss to follow-up, end of data availability, end of the study period, or death . Population The study population will be the population that was included in the study EUPAS1000000440 , of which this is a routinely repeated study. This study population will include all individuals aged 18 years and above with a primary diagnosis of one of the selected cancers (bone, brain, breast, colorectal, corpus uteri, kidney, leukaemia and lymphoma, liver, lung, melanoma, oesophageal, ovary, pancreas, prostate, stomach) during the inclusion period (from 01/01/2016 to 31/12/2022). Only individual s with an incident cancer diagnosis (excluding non-melanoma skin cancer) , defined as a first cancer diagnosis after ≥ 365 days cancer-free history, will be included. Cancer cases and thromboembolic events w ill be identified based on appropriate computable phenotyping algorithms. Conditions in the OMOP CDM use the Systematised 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. Other eligibility criteria will include at least 365 days of database history prior to index date and at least 365 days between index date and end of data availability in the data source. Variables Exposure: Not applicable. Outcome: The outcome s will include thromboembolic event s, specifically: deep vein thrombosis ( DVT ) , pulmonary embolism ( PE ) , venous thromboembolism ( VTE, composite of DVT and PE ) , pelvic venous thrombosis ( PVT ) , splanchnic vein thrombosis ( SVT , including hepatic and extra-hepatic vein thrombosis ) , retinal vein thrombosis ( RVT, including retinal central vein thrombosis ) , and disseminated intravascular coagulation ( DIC ). Relevant covariates: The following covariates will be assessed at index date: age group in years (18–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥ 85), sex, and study subperiod (2016–2019 and 2020–2022) . These variables will be used to stratify the results. Data source s Belgium: IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium) Denmark: Danish Data Health Registries (DK-DHR) Estonia: Estonian Biobank (EBB) Finland: Finnish Care Register for Health Care ( FinOMOP -THL) Germany: IQVIA Disease Analyzer Germany (IQVIA DA Germany) Netherlands: Integrated Primary Care Information (IPCI) Spain: The Information System for Research on Primary Care (SIDIAP) United Kingdom: Clinical Practice Research Datalink GOLD (CPRD GOLD) United Kingdom: UK BioBank (UKBB) Study size No sample size will be calculated, as this is an exploratory study which will not test a specific hypothesis. Based on the results of the study EUPAS1000000440 , the expected number of person counts will be the lowest for DIC ( during 1-year follow-up: 5 in FinOMOP -THL – 171 in SIDIAP , with 0 counts in CPRD GOLD, EBB, IPCI, IQVIA DA Germany , and I QVIA LPD Belgium) and highest for VTE ( during 1-year follow-up: 27 in IQVIA LPD Belgium – 4,597 in FinOMOP -THL ). Statistical analysis Analyses will be conducted separately for each data source and carried out in a federated manner, allowing analyses to be run locally without sharing individual -level data. Objective 1 The probabilit ies of not having thromboembolic events at 6-month intervals within 5 years in adult s with each type of selected cancer will be assessed using the R package CohortSurvival , accounting for a competing risk of death. Objective 2 The median time to onset of venous thromboembolic events in a cohort of adult s with thromboembolic events with each type of selected cancer will be assessed using the R package CohortSurvival . The R package CohortSurvival is designed to work with data in the OMOP CDM format to extract and summarise survival data applying the Kaplan-Meier method. T he analyses will be conducted for the overall cohorts as well as by strata of age group, sex, and study subperiod . Absence of diagnosis codes will be interpreted as a lack of the conditions themselves. A minimum cell count of 5 will be used when reporting results, with any smaller count reported as “&lt;5” and zero counts as “0” . AMENDMENTS AND UPDATES None MILESTONES Study milestones and deliverables Planned dates Final Study Protocol To be confirmed by EMA Creation of Analytical code 31 October 2025 Execution of Analytical Code on the data 14 November 2025 Draft Study Report Depending on IRB approvals Final Study Report Depending on IRB approvals *Planned dates are dependent on obtaining approvals from the internal review boards of the data sources. RATIONALE AND BACKGROUND This study is a routine repeated study of a previous DARWIN EU® study ( EUPAS1000000440 ) focused on estimating the incidence rates of venous thromboembolic events in adults newly diagnosed with any of the selected cancers ( bone, brain, breast, colorectal, corpus uteri, kidney, leukaemia and lymphoma, liver, lung, melanoma, oesophageal, ovary, pancreas, prostate, stomach) during the period 2016–2022 and describing the individuals’ characteristics at the time of cancer diagnosis . This study is now being repeated with the same study population and outcome, but a different objective , i.e., to estimate the time to onset of thromboembolic events in adults with selected types of cancer . RESEARCH QUESTION AND OBJECTIVES Research question The aim of this study is to estimate time to onset of venous thromboembolic events in adult s with each type of selected cancer. Research objectives The specific objectives of the study are: To estimate the probability of not having thromboembolic events at 6-month intervals within 5 years in adult s with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . To estimate median time to onset of venous thromboembolic events in a cohort of adult s with thromboembolic events with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . RESEARCH METHODS 8.1. Study design A cohort study will be conducted. The study will comprise: a characteri s ation study to address objective 1, assessing the probability of not having thromboembolic events at 6-month intervals within 5 years in adults with each type of selected cancer, overall and stratified by age group, sex, and study subperiod a characteri s ation study to address objective 2, assessing median time to onset of venous thromboembolic events in a cohort of adults with thromboembolic events with each type of selected cancer, overall and stratified by age group, sex, and study subperiod . The study design to address objective 1 , including assessment windows , is visualised in Figure 1 . For objective 2 , we will subset the cohort of adults with cancer with thromboembolic events . Figure 1. Graphical depiction of the study design. The censor date will be the earliest of occurrence of the outcome, loss to follow-up, end of data availability, or death. 8.2. Follow-up For both objectives, f ollow-up in the survival analysis will start on the date of cancer diagnosis (index date) and end on the earliest of occurrence of the outcome (thromboembolic event) , loss to follow-up, end of data availability, or death . 8.3 . Study population with inclusion and exclusion criteria Objective 1 Inclusion criteria First diagnosis of a selected cancer (index date) between 01/01/2016 and 31/12/2022 Age ≥18 years at cancer diagnosis Minimum 365 days of available history before the cancer diagnosis date Cancer diagnosis date ≥365 days prior to end of data availability of the data source Exclusion criteria History of any cancer diagnosis ever before the selected cancer diagnosis date Outcome during the year before the cancer diagnosis date Objective 2 Inclusion criteria Included in the study population of objective 1 Occurrence of the outcome (thromboembolic event) during follow-up . 8. 4 . Study setting and data sources This study will be conducted using routinely collected data from different health care settings from 9 data sources in the DARWIN EU ® network of data partners from 8 countries across Europe , of which 7 EU member states ( Table 1 ) . All data were a priori mapped to the OMOP CDM. Table 1. Data sources. Country Name of Data source Health Care setting Type of Data Number of active individuals Calendar period covered by each data source Contributing to BE IQVIA LPD Belgium Primary care EHRs 189k 2015– 2025 All objectives DK DK-DHR All settings EHRs, registries, claims 5.98M 1995– 2025 All objectives EE EBB Primary care, hospital care (IP and OP) EHRs, claims, registries, biobank 212k 2004– 2025 All objectives FI FinOMOP -THL Hospital care (IP and OP) EHRs, registries 5.7M 2011– 2025 All objectives DE IQVIA DA Germany Primary care EHRs 4.48M 1992– 2025 All objectives NL IPCI Primary care EHRs 1.33M 2006– 2025 All objectives ES SIDIAP Primary care EHRs 5.95M 2006– 2025 All objectives GB CPRD GOLD Primary care, hospital care (OP) EHRs 2.83M 1987– 2025 All objectives GB UKBB Primary care (up to 2017) , hospital care (IP and OP , up to November 2022 ) EHRs, registries, biobank 500k 1940– 2025 All objectives Countries: BE=Belgium, DE=Germany, DK=Denmark, EE=Estonia, ES=Spain, FI=Finland, GB=United Kingdom of Great Britain and Northern Ireland, NL=The Netherlands Data sources: IQVIA LPD=IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium); DK-DHR=Danish Data Health Registries; EBB=Estonian Biobank; FinOMOP -THL=Finnish Care Register for Health Care; IQVIA DA=IQVIA Disease Analyzer Germany (IQVIA DA Germany); IPCI=Integrated Primary Care Information; SIDIAP=The Information System for Research in Primary Care; CPRD GOLD=Clinical Practice Research Datalink GOLD; UKBB=UK BioBank Types of data: EHR=electronic health record, IP=inpatient, OP=outpatient Number of active subjects: k=thousands, M=millions Data sources s election These data sources fulfil the criteria required in terms of data quality, completeness, timeliness, and representativeness for the cohort study while covering different regions of Europe ( Annex I I ) . 8. 5 . Study period The study period is from 01/01/2016 to the most recent data available for each contributing data source. 8.6. Variables 8.6.1. Exposure None. 8.6.2. Outcome All o bjectives The thromboembolic event outcomes in this study are identical to those in EUPAS1000000440 , of which this is a routinely repeated study : Deep vein thrombosis (DVT) Pulmonary embolism (PE) Venous thromboembolism (VTE, composite of DVT and PE) Pelvic venous thrombosis (PVT) Splanchnic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis Retinal vein thrombosis (RVT), including retinal central vein thrombosis Disseminated intravascular coagulation (DIC ) Each of the specific thromboembolic events will be a primary, binary outcome. The outcome will be assessed at any diagnosis position in the electronic health record and from any of the care settings in each data source. The list of concepts , from EUPAS1000000440 and based on SNOMED codes and aligned with previous studies that used OMOP CDM and VTE as an outcome (Burn et al., 2022) , is provided in Annex I V . 8.6.3. Intercurrent events (only for causal studies) Not applicable. 8.6.4. Covariates , including confounders, effect modifiers, and other variables All Objectives Sex Female/male Age groups (years) at cancer diagnosis date : 18–34 35–44 45–54 55–64 65–74 75–84 ≥85 Age at cancer diagnosis date will be calculated using January 1st of the year of birth as proxy for the actual birthday. Date/month is either not present or cannot be made available for governance reasons. If available, date is often set to first of the month for personal privacy. Study subperiod : 2016–2019 2020 – 2022 The status of each covariate will be assessed at the cancer diagnosis date. Each covariate will be used to stratify the results. 8.7. S tudy size No sample size will be calculated, as this is a characteri s ation study which will not test a specific hypothesis . In addition, we will use data from the study population that was included in EUPAS1000000440 , of which this is a routinely repeated study . Thus, the sample size is driven by the availability of data for adults with both cancer and a thromboembolic event . Based on the results of the study EUPAS1000000440 , the expected number of person counts is lowest for DIC ( during 1-year follow-up: 5 in FinOMOP -THL – 171 in SIDIAP, with 0 counts in CPRD GOLD, EBB, IPCI, IQVIA DA Germany, and IQVIA LPD Belgium) and highest for VTE ( during 1-year follow-up: 27 in IQVIA LPD Belgium – 4,597 in FinOMOP -THL ) . 8.8. A nalysis 8.8.1. Federated network analyses All analyses will be conducted separately for each data source, and will be carried out in a federated manner, allowing analyses to be run locally without sharing individuals’ data. Before sharing the study package, test runs of the analytics will be performed on a subset of the data sources and quality control checks will be performed. After all the tests are passed (see Annex I I I. Operational and reporting considerations ), the final package will be released in a version-controlled study repository for execution against all the participating data sources. 8.8.2. Data privacy protection The data partners will locally execute the analytics against the OMOP CDM in R Studio and review and approve the default aggregated results. They will then be made available to the Principal Investigators and study team in secure online repository of DTZ (Data Transfer Zone). All results will be locked and timestamped for reproducibility and transparency. The study results of all data sources will be checked , after which they are made available to the team , and the Study Dissemination Phase can start. All 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 individual -level data. Cell counts &lt;5 will be suppressed when reporting results to comply with the data source’s privacy protection regulations. 8.8.3. Statistical model specification and assumptions of the analytical approach considered Objective 1 The probability of not having thromboembolic events at 6-month intervals within 5 years ( 6, 12, 18, 24, 30, 36, 42, 48 , 54 , and 60 months ) in adult s with each type of selected cancer will be calculated based on OMOP CDM mapped data using the R package CohortSurvival , developed by DARWIN EU® . The key measure of not having events at 6-month intervals within 5 years in adult s with each type of selected cancer will be the probability , i.e. , the proportion of individuals in the cohort who have not yet experienced a thromboembolic event after each interval , accounting for the competing risk of death. Individuals will be censored at the date of loss to follow-up, the end of data availability , or the end of the study period . The estimateCompetingRiskSurvival () function will be used in the analysis. Objective 2 The median time to onset of venous thromboembolic events in a cohort of adult s with thromboembolic events with each type of selected cancer will be calculated based on OMOP CDM mapped data using the R package CohortSurvival , developed by DARWIN EU®. The key measure of median time to onset of venous thromboembolic events will be median survival time obtained using the estimateSingleEventSurvival () function. All objectives The R package CohortSurvival is designed to work with data in the OMOP CDM format to extract and summarise survival data applying the Kaplan-Meier method. F or objective 1 , death will be accounted for as a competing risk . T he analyses will be conducted for the overall cohorts as well as by strata of age group, sex, and study subperiod . The a bsence of diagnosis codes will be interpreted as the absence of the conditions themselves. A minimum cell count of 5 will be used when reporting results, with any smaller count reported as “&lt;5” and zero counts as “0” . Sensitivity analysis Not applicable . 8.8.4. Output Output will include a PDF report including an executive summary, and tables and figures. Mock versions of the intended tables and figures are listed below. Table 1. Attrition of study participants (objectives 1 and 2 ) . Figure 1. P robability of not having thromboembolic event after the first cancer diagnosis (objective 1 ) . This figure will be plotted for each combination of cancer type and outcome, faceted by data source. The main report will include 15 such figures: one for each type of cancer . In the meta-analysis results of EUPAS1000000440 , of which this is a routinely repeated study, the most common outcome was VTE across all cancer types, except for liver cancer. In liver cancer , the most common outcome was SVT. Therefore, Figure 1 in the main report will be plotted for SVT in liver cancer and for VTE in all other cancer types. The corresponding figures for all other outcomes across all cancer types will be available in Shiny. Table 2. Median time to thromboembolic event after first cancer diagnosis in those with occurrence of thromboembolic event (objective 2 ) . The main report will include 15 such tables: one for each cancer type. An interactive dashboard (Shiny) will be generated by incorporating all the results (tables and figures) included in the PDF report mentioned above. Specificall y, the Shiny will contain: Overall results : Objective 1: 15 cancer types * 7 outcomes = 105 figures as presented in Figure 1 . Objective 2: 15 tables as presented in Table 2 . Stratified results: By age group : Objective 1: 7 age groups * 105 figures = 735 figures Objective 2: 7 age groups * 15 tables = 105 tables By sex: Objective 1: 2 sexes * 105 figures = 2 10 figures Objective 2: 2 sexes * 15 tables = 30 tables By study subperiod: Objective 1: 2 subperiods * 105 figures = 2 10 figures Objective 2: 2 subperiods * 15 tables = 30 tables . Table 1. Attrition of study participants. IQVIA LPD DK-DHR EBB FinOMOP -THL IQVIA DA IPCI SIDIAP CPRD GOLD UKBB Bone cancer Qualifying initial records N o prior history of cancer Outcome during follow-up Brain cancer Qualifying initial records No prior history of cancer Outcome during follow-up Breast cancer Qualifying initial records No prior history of cancer Outcome during follow-up Colorectal cancer Qualifying initial records No prior history of cancer Outcome during follow-up Corpus uteri cancer Qualifying initial records No prior history of cancer Outcome during follow-up Oesophageal cancer Qualifying initial records No prior history of cancer Outcome during follow-up Kidney cancer Qualifying initial records No prior history of cancer Outcome during follow-up Liver cancer Qualifying initial records No prior history of cancer Outcome during follow-up Lung cancer Qualifying initial records No prior history of cancer Outcome during follow-up Lymphoma and Leukaemia Qualifying initial records No prior history of cancer Outcome during follow-up Melanoma Qualifying initial records No prior history of cancer Outcome during follow-up Ovarian cancer Qualifying initial records No prior history of cancer Outcome during follow-up Pancreatic cancer Qualifying initial records No prior history of cancer Outcome during follow-up Prostate cancer Qualifying initial records No prior history of cancer Outcome during follow-up Stomach cancer Qualifying initial records No prior history of cancer Outcome during follow-up N=the number of individuals meeting each criterion. IQVIA LPD=IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium); DK-DHR=Danish Data Health Registries; EBB=Estonian Biobank; FinOMOP -THL=Finnish Care Register for Health Care; IQVIA DA=IQVIA Disease Analyzer Germany (IQVIA DA Germany); IPCI=Integrated Primary Care Information; SIDIAP=The Information System for Research in Primary Care; CPRD GOLD=Clinical Practice Research Datalink GOLD; UKBB=UK BioBank . 3528060 2123440 Data source 0 0 Data source 1223010 2123440 Data source 0 0 Data source 3442335 37465 Data source 0 0 Data source 1146810 37465 Data source 0 0 Data source 5575935 2209165 Outcome 0 0 Outcome Figure 1. Cumulative probability of not having thromboembolic event after the first cancer diagnosis accounting for a competing risk of death . Table 2. Median time in days (95% CI) to thromboembolic event after first cancer diagnosis . IQVIA LPD DK-DHR EBB IQVIA DA IPCI SIDIAP CPRD GOLD UKBB FinOMOP -THL Deep vein thrombosis (DVT) PE and DVT combined (VTE) Pelvic vein thrombosis (PVT) Pulmonary embolism (PE) Retinal vein thrombosis (RVT) Splanchnic extrahepatic vein thrombosis (SVT) IQVIA LPD=IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium); DK-DHR=Danish Data Health Registries; EBB=Estonian Biobank; FinOMOP -THL=Finnish Care Register for Health Care; IQVIA DA=IQVIA Disease Analyzer Germany (IQVIA DA Germany); IPCI=Integrated Primary Care Information; SIDIAP=The Information System for Research in Primary Care; CPRD GOLD=Clinical Practice Research Datalink GOLD; UKBB=UK BioBank ; NC=Not calculated (fewer than 5 events). STRENGTHS AND LIMITATIONS The study will be informed by routinely collected healthcare data, and therefore, data quality issues must be considered. In particular, the identification of individuals with cancer and thromboembolic events may vary across data sources. While relatively few false positives are expected, false negatives may be more likely, especially for primary care data sources that lack patient-level linkage to secondary care data. We expect misclassification to be minimal in registry data sources and in the primary care data sources where cancer diagnoses ha ve been previously validated. Underestimation of thromboembolic events is also possible, particularly for rare events with complex diagnoses, such as RVT and SVT. Given the large number and diverse nature of participating data sources, it is important to note that differences in patient representations might have resulted 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 ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"oD2PIVlM","properties":{"formattedCitation":"[1]","plainCitation":"[1]","noteIndex":0},"citationItems":[{"id":1,"uris":["http://zotero.org/users/local/mtOZugFB/items/NZTENE78"],"itemData":{"id":1,"type":"article-journal","abstract":"Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.","container-title":"AMIA Annual Symposium Proceedings","ISSN":"1942-597X","journalAbbreviation":"AMIA Annu Symp Proc","note":"PMID: 33936474\nPMCID: PMC8075504","page":"983-992","source":"PubMed Central","title":"Characterizing database granularity using SNOMED-CT hierarchy","volume":"2020","author":[{"family":"Ostropolets","given":"Anna"},{"family":"Reich","given":"Christian"},{"family":"Ryan","given":"Patrick"},{"family":"Weng","given":"Chunhua"},{"family":"Molinaro","given":"Anthony"},{"family":"DeFalco","given":"Frank"},{"family":"Jonnagaddala","given":"Jitendra"},{"family":"Liaw","given":"Siaw-Teng"},{"family":"Jeon","given":"Hokyun"},{"family":"Park","given":"Rae Woong"},{"family":"Spotnitz","given":"Matthew E."},{"family":"Natarajan","given":"Karthik"},{"family":"Argyriou","given":"George"},{"family":"Kostka","given":"Kristin"},{"family":"Miller","given":"Robert"},{"family":"Williams","given":"Andrew"},{"family":"Minty","given":"Evan"},{"family":"Posada","given":"Jose"},{"family":"Hripcsak","given":"George"}],"issued":{"date-parts":[["2021",1,25]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [1] . The preliminary code lists created to identify individuals with cancer include codes from standard vocabularies used in cancer registries, such as ICD-O-3 codes. However, most data sources capture information on cancer diagnoses using SNOMED codes, which may not be granular enough to cover all the topology and morphology of cancer ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"s4kVsvv8","properties":{"formattedCitation":"[2]","plainCitation":"[2]","noteIndex":0},"citationItems":[{"id":3,"uris":["http://zotero.org/users/local/mtOZugFB/items/PT76GYNM"],"itemData":{"id":3,"type":"article-journal","abstract":"Objective This research investigated the use of SNOMED CT to represent diagnostic tissue morphologies and notable tissue architectures typically found within a pathologist's microscopic examination report to identify gaps in expressivity of SNOMED CT for use in anatomic pathology.Methods 24 breast biopsy cases were reviewed by two board certified surgical pathologists who independently described the diagnostically important tissue architectures and diagnostic morphologies observed by microscopic examination. In addition, diagnostic comments and details were extracted from the original diagnostic pathology report. 95 unique clinical statements were extracted from 13 malignant and 11 benign breast needle biopsy cases.Results 75% of the inventoried diagnostic terms and statements could be represented by valid SNOMED CT expressions. The expressions included one pre-coordinated expression and 73 post-coordinated expressions. No valid SNOMED CT expressions could be identified or developed to unambiguously assert the meaning of 21 statements (ie, 25% of inventoried clinical statements). Evaluation of the findings indicated that SNOMED CT lacked sufficient definitional expressions or the SNOMED CT concept model prohibited use of certain defined concepts needed to describe the numerous, diagnostically important tissue architectures and morphologic changes found within a surgical pathology microscopic examination.Conclusions Because information gathered during microscopic histopathology examination provides the basis of pathology diagnoses, additional concept definitions for tissue morphometries and modifications to the SNOMED CT concept model are needed and suggested to represent detailed histopathologic findings in computable fashion for purposes of patient information exchange and research.Trial registration number UNMC Institutional Review Board ID# 342-11-EP.","container-title":"Journal of the American Medical Informatics Association","DOI":"10.1136/amiajnl-2013-002456","ISSN":"1067-5027","issue":"5","journalAbbreviation":"J Am Med Inform Assoc","page":"885-892","source":"Silverchair","title":"Semantic analysis of SNOMED CT for a post-coordinated database of histopathology findings","volume":"21","author":[{"family":"Campbell","given":"Walter S"},{"family":"Campbell","given":"James R"},{"family":"West","given":"William W"},{"family":"McClay","given":"James C"},{"family":"Hinrichs","given":"Steven H"}],"issued":{"date-parts":[["2014",9,1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} [2] . ICD-O-3 codes are only available at DK-DHR, EBB, and UKBB. REFERENCES ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY 1. Ostropolets A, Reich C, Ryan P, Weng C, Molinaro A, DeFalco F, et al. Characterizing database granularity using SNOMED-CT hierarchy. AMIA Annu Symp Proc. 2021 Jan 25;2020:983–92. 2. Campbell WS, Campbell JR, West WW, McClay JC, Hinrichs SH. Semantic analysis of SNOMED CT for a post-coordinated database of histopathology findings. J Am Med Inform Assoc. 2014 Sept 1;21(5):885–92. ANNEXES ANNEX I. Description of data sources DATA SOURCES DESCRIPTION IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium) # Section Description 1 Database Identification and country IQVIA LPD Belgium (IQVIA Longitudinal Patient Database Belgium) Belgium 2 Data partner information section IQVIA IQVIA Europe 3 Coverage and timespan Data collection since: 2005 Extent: Nation-wide. Panel of 300 GPs in Belgium. The panel is maintained as a representative sample of the primary care physician population in Belgium, according to three criteria known to influence prescribing: age, sex, and geographical distribution. 4 Healthcare setting / type of data Primary care – gps . Ambulatory visits, with diagnosis, prescriptions, procedures, and laboratory tests. 5 Data collection process Outpatient electronic health records. Records are entered by GPs at the healthcare encounter. 6 General representativeness The panel of contributing physicians (a stable 300 GPs) is maintained as a representative sample of the primary care physician population in Belgium, according to three criteria known to influence prescribing: age, sex, and geographical distribution. 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 provider of the data has more than 2,250 GPs under contract so in case of a drop out a replacement is easily found. 7 Data content /source coding No information on source coding. 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. The patient ID is per practice. So a patient can have different IDs in the DB, one per practice. In Belgium, patients are typically registered at only one GP practice, so duplication should be minimal. 9 Quality control (database specific) No QC. Integrity constraints only. 10 Linkage No linkage. 11 Vital status Death information is derived from healthcare events. 12 Limitations No database-specific limitations documented. General limitations for the data type applicable. 13 Main references No main reference provided. 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/1111116 Website : https://iqvia.com Danish Data Health Registries (DK-DHR) # Section Description 1 Database Identification and country DK-DHR (Danish Data Health Registries) Denmark 2 Data partner information section Danish Medicines Agency (DKMA), Data Analytics Centre (DAC) 3 Coverage and timespan Data collection since: 1995, Extent: Nationwide. The data is representative of the entire Danish population. 4 Healthcare setting / type of data C ommunity pharmacists, and secondary care – specialists (ambulatory or hospital outpatient care), and hospital inpatient care. The following data elements are collected: diagnosis (including rare diseases and pregnancy data), hospital admissions, discharge and ICU data, Cause of death, Drug prescription retrievals , vaccination and contraception, Procedures, and Sociodemographic information (sex and age but no information on income, education, occupation) . 5 Data collection process Outpatient electronic health records, and Inpatient hospital electronic health records, and Registries, and Other. All causes of deaths, all retrieved drug prescriptions, all records of vaccinations, all hospital inpatient and outpatients contacts including disease diagnoses and hospital surgical and non-surgical procedures, histologically confirmed incident cancers, laboratory test results for the entire Danish population from 1/1/1995 onwards. 6 General representativeness The data is representative of the entire Danish population. Healthcare is free in Denmark, so we do not expect any bias in data collection based on socio-economic status. 7 Data content /source coding Diagnoses and causes of death are collected using the ICD-10 vocabulary. ATC and RxNorm are used for Drugs. SNOMED codes are used for Procedures. 8 Data Harmonization T he data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC , ICDO3, cancer Modifier ). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. . 9 Quality control (database specific) The data we have received relating to nationwide Danish Health Data registries offer an opportunity for large-scale, population-based studies with several advantages 1) Their large size improves the precision of estimates and enables the study of rare exposures and outcomes with long-term latency, 2) Inclusion of nearly all individuals in the target population ensures that the data reflect routine clinical care and all clinical segments of the source population, 3) Data are collected independently of each research study, thus minimising certain types of bias, e.g., non-response, and the influence from attention to the research question on the diagnostic process. Before the source data is sent to us, the Danish Health Data Authority does running and comprehensive checks of the registry table data validity of the variables, breaks in data, changes in variable coding, missingness, etc. We perform checks of missingness/completeness in relation to requested variables. In essence, we are receiving a dump of a mirror of the data that is controlled by the SDS. The documentation performed by SDS is available online, in Danish primarily https://www.esundhed.dk/Dokumentation (all variables), but also in English https://sundhedsdatastyrelsen.dk/da/english/health_data_and_registers/national_health_registers 10 Linkage There is no linkage in this data source. 11 Vital status The Cause of Death registry (DAR) is used, the cause of death is collected using ICD-10 codes. 12 Limitations There are no clinical measurements in the data. DK-DHR has the following limitations, which may be relevant confounders for certain complex Darwin EU studies: We lack information on key socio-eco nomic status (SES) factors, such as occupation, education, and income. These variables may be important for analysis in some studies. We only have complete data on lifestyle factors (such as smoking status and weight) for pregnant women. We have no information on patient contacts in primary care (visits to the GP). Consequently, the incidence of chronic diseases like Type 2 Diab 13 Main references Schmidt M, Schmidt SAJ,Adelborg K,Sundbøll J,Laugesen K,Ehrenstein V,Sørensen HT "The Danish health care system and epidemiological research: from health care contacts to database records." Clinical epidemiology (2019): 31372058 14 Link to HMA-EMA catalogue and database webpage Website: https://sundhedsdatastyrelsen.dk/da/english/health_data_and_registers/healthdatadenmark HMA-EMA Catalogue entry: https://catalogues.ema.europa.eu/data-source/1111217 Estonian Biobank (EBB) # Section Description 1 Database Identification and country EBB (Estonian Biobank) Estonia 2 Data partner information section University of Tartu Institute of Computer Science 3 Coverage and timespan Data collection since: 2004 Extent: Nation-wide. EBB is a nation-wide database containing records from 2004 onwards. Estonian population-based cohort size of 211,800 participants (01/01/2024) aged 18 years and older recruited at GP offices, private practices, and hospitals or in the recruitment offices of the Estonian Genome Center. 4 Healthcare setting / type of data Primary care – GPs , and community pharmacists, and primary care specialists (e.g. paediatricians), and secondary care – specialists (ambulatory or hospital outpatient care), and hospital inpatient care. Registry which collects electronic records from the biobank and cohort study. 5 Data collection process Data is retrieved by Estonian Biobank once a year from national registries. The insurance claims are requested from Estonian Health Insurance Fund . The inpatient and outpatient electronic health records are requested from National Health Information System . The cancer registry and cause of death registry information is requested from The National Institute for Health Development. The data is sent to the national registry by the healthcare providers. 6 General representativeness The age, sex, and geographical distribution closely reflect those of the Estonian adult population and encompass close to 5 approximately 20 % of adult population. Female participants are over-represented in EBB. Overall, 3.4% of Estonian men and 5.5% of Estonian women are represented in EBB. Older people tend to participate less frequently, however, all age groups are well represented. 7 Data content /source coding All participants have undergone a standardized health assessment, including provision of 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 and local package codes. Procedures and services are coded with NOMESCO classifier and local service codes. 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. T here is one national identifier that allows linking together all encounters across databases. 9 Quality control (database specific) The quality control procedures in the Estonian Biobank aim to remove the most obvious mistakes in the data, misspellings, impossible dates, duplicates. Before performing the ETL, several problems are fixed on the source data. Since the ETL procedures are used for a number of different datasets (from the same national sources), we have a growing number of pre-processing steps that correspond to the issues we have discovered previously in the data, such as checking for the presence of critical values, harmonizing date and unit of measurement formats, checking the validity of certain entries against classifiers, etc. 10 Linkage Follow‐up data are available via linkage with national health‐related registries and via re‐examination of participants. Furthermore, electronic health records are updated for phenotypic outcome information every year. The EBB database is regularly linked with national registries, hospital databases, and the databases of the Estonian Health Insurance Fund (EHIF) and the National Health Information System (NHIS) 11 Vital status Vital status (death date and causes of death) are obtained from the Causes of Death Registry. 12 Limitations Participation in EBB cohort is voluntary, therefore the biobank does not represent a random sample and could be subject to recruitment bias. Although recruitment was open to everyone, there is a disproportion between ethnic Estonians and ethnic Russians in the biobank, with Estonians being overrepresented. 13 Main references Milani, L., Alver, M., Laur, S. et al. The Estonian Biobank’s journey from biobanking to personalized medicine. Nat Commun 16 , 3270 (2025). https://doi.org/10.1038/s41467-025-58465-3 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/1111114 Website : https://genomics.ut.ee/en/content/estonian-biobank Finnish Care Register for Health Care ( FinOMOP -THL) # Section Description 1 Database Identification and country FinOMOP -THL (Finnish Care Register for Health Care) Finland 2 Data partner information section Finnish Institute for Health and Welfare (THL) Department of Knowledge Brokers 3 Coverage and timespan Data collection since: 1998 Extent: Nation-wide. The current CDM population comprises all persons having been alive and residing in Finland since the beginning of 2011. 4 Healthcare setting / type of data Primary care – gps , and primary care specialists (e.g. paediatricians), and secondary care – specialists (ambulatory or hospital outpatient care), and hospital inpatient care. The THL database covers both public and private, primary, and specialised inpatient and outpatient health care encounters in Finland, starting from 2011. The entire public sector and private inpatient encounters have been included since 2011, while private outpatient encounters, including occupational care, are included since 2020. Since 1998, the register has covered both public outpatient and inpatient specialized care and private inpatient care ( TerveysHilmo ). Since 2009, the Finnish National Vaccination Register is covered (complete since 2020). The vaccination register covers all vaccinations from the public sector and from a large part of private vaccination providers, with the data coverage from both sections being very good from 2020 onwards. Since 2011, the register has covered public primary care ( AvoHilmo ). Since 2020, the register has covered private outpatient care and occupational care. In addition, the CDM also contains positive COVID-19 test results from the Finnish National Infectious Diseases Register, which is maintained by THL. 5 Data collection process Outpatient electronic health records, and Inpatient hospital electronic health records, and Registries. Data is entered by clinicians upon healthcare contact and processed by THL. 6 General representativeness The THL data has national coverage and is therefore well representative of the Finnish population. Using the complete population as a basis for the person table also serves to facilitate calculations on a population level, e.g. incidence rates. 7 Data content /source coding The following coding systems have been OMOP-mapped, typically to a good level of completeness: ICD10fi Finnish Extension, ATC, Toimenpideluokitus (procedure classification adapted from the Nordic Classification of Surgical Procedures (NCSP)), Terveydenhuollon erikoisalat (Hilmo specific provider speciality), Rokotustapa (AR/YDIN National classification for vaccine administration), Tupakointistatus (AR/YDIN National classification for smoking status). Vaccinations are identified on product level based on batch number, trade name, vaccine title, and ATC-code. This is mapped on brand and type in the OMOP CDM. 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. Each patient in THL has a unique identifier. 9 Quality control (database specific) The source data collection undergoes a structural and semantic validation before entry into the source database. Additionally, some coded variables undergo quality assessment against the respective code systems post entry into the database. The source registers are also assessed for completeness and coverage, with the aim of improving future collection in the areas where data is lacking. 10 Linkage THL is already a linkage of multiple Finnish registries (see above). 11 Vital status The National Population registry data forms the basis for forming the patient population. This ensures an up-to-date location (municipality of residence) of patients, as well as complete death occurrences (although not the cause of death). 12 Limitations No database-specific limitations documented. General limitations for the data type applicable. 13 Main references Häkkinen, Pirjo; Mölläri , Kaisa; Saukkonen, Sanna-Mari; Väyrynen, Riikka; Mielikäinen , Lasse; Järvelin , Jutta "Hilmo - Sosiaali - ja terveydenhuollon hoitoilmoitus 2020 : Määrittelyt ja ohjeistus : Voimassa 1.1.2020 alkaen " Terveyden ja hyvinvoinnin laitos (2019): 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/1111187 Website : https://thl.fi/fi/tilastot-ja-data/ohjeet-tietojen-toimittamiseen/hoitoilmoitusjarjestelma-hilmo IQVIA Disease Analyzer Germany (IQVIA DA Germany) # Section Description 1 Database Identification and country IQVIA DA Germany (IQVIA Disease Analyzer Germany) Germany 2 Data partner information section IQVIA 3 Coverage and timespan Data collection since: 1989 Extent: Nation-wide. GP and specialists in Germany using specific patient management software. 4 Healthcare setting / type of data Primary care – gps , and primary care specialists (e.g. paediatricians). Diagnoses, medication, and procedures from an ambulatory setting. Medications are recorded as prescriptions of marketed products. 5 Data collection process Outpatient electronic health records. By clinicians at healthcare contact. 6 General representativeness No specific details on general representativeness given. 7 Data content /source coding Prescription is on product code level (German PZN), ICD10, NFC, Local lab coding. 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. There can be patients registered under different ID numbers, because there is no linkage between different GPs. 9 Quality control (database specific) Data is quality checked on plausibility. 10 Linkage No. 11 Vital status Death information is derived from medical events. 12 Limitations No database-specific limitations documented. General limitations for the data type applicable. 13 Main references No main reference provided. 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/104282 Website : https://www.iqvia.com/ Integrated Primary Care Information (IPCI) # Section Description 1 Database Identification and country IPCI (Integrated Primary Care Information) Netherlands 2 Data partner information section Erasmus University Medical Center Department of Medical Informatics 3 Coverage and timespan Data collection since: 2006 Extent: Nation-wide. IPCI is a Dutch database that contains patient records from 2006 onwards. However, it mainly covers the central part of the country, including the most densely populated area (the ‘Randstad’) and non-urban areas. IPCI contains information on all patients registered with GPs responsible for non-emergency care and referrals. A patient is registered at birth or at first encounter with the GP. 4 Healthcare setting / type of data Primary care – gps . Data is collected from primary care EHR. This includes demographic information, complaints and symptoms, diagnoses, laboratory test results, lifestyle factors (in limited amount), and correspondence with secondary care, such as referral and discharge letters. 5 Data collection process Outpatient electronic health records. Data is entered into the EHR system by the GPs, during or after the visit. Data is aggregated by Erasmus MC data managers and combined in one harmonized database. Several checks are done on this database to ensure correct data processing. Persons are mostly uniquely identified, with the exception of when persons change GP practice (when the same individual can receive several different identifiers). 6 General representativeness 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. 7 Data content /source coding Dutch GPs use mainly Dutch standard codes, like ICPC-1 and Diagnostische Bepalingen maintained by NHG. And for therapy the G-Standard is used, maintained by ZIndex . 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. Patients can be registered under different IDs. However, in the Netherlands, patients typically have one GP and changing practice is uncommon. 9 Quality control (database specific) Prior to each data release, extensive quality control steps are performed, e.g., comparison of patient characteristics between practices, and checks to identify abnormal temporal data patterns in practices. For each practice, around 200 quality indicators are obtained. Of these indicators, a quarter refer to population characteristics, e.g. number of birth and mortalities relative to practice size, temporal consistency. The other indicators are based on medical data, e.g. distribution of measurement values, frequencies of diagnoses and procedures relative to age, completeness of data. The indicators are combined in a couple of quality scores for each practice. For these scores, cut-off values for acceptable quality have been defined. Practices with a score below a cut-off are excluded for research. This approach has shown to be very important, for example to check if data from practices that just joined the database are at an acceptable level of quality. The details of the approach, like the cut-off values for acceptance, are based on years of experience. In addition, trends are compared with the previous database release. 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 and completeness of laboratory results). Records of low quality are excluded from the database. 10 Linkage Linkage requires additional approval steps and needs to be assessed on a case-by-case basis. IPCI is not routinely linked with other databases. 11 Vital status Vital status (death date and cause) is collected based on GP records. 12 Limitations The main limitation comes with the fact that IPCI is limited to GP records, and although it contains information on referrals and discharge letters, it may not fully capture specific hospital information. IPCI does not include coded/detailed data about medications/procedures/test results from the hospital or other care-providers. 13 Main references de Ridder MAJ, de Wilde M,de Ben C,Leyba AR,Mosseveld BMT,Verhamme KMC,van der Lei J,Rijnbeek PR "Data Resource Profile: The Integrated Primary Care Information (IPCI) database, The Netherlands." International journal of epidemiology (2022): 35182143 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/42618 Website : http://www.ipci.nl The Information System for the Development of Research on Primary Care (SIDIAP) # Section Description 1 Database Identification and country SIDIAP (The Information System for the Development of Research in Primary Care) Catalunya, Spain 2 Data partner information section IDIAPJGol 3 Coverage and timespan Data collection since: 2006 Extent: Regional. The SIDIAP database contains records of around 6 million people residing in Catalonia, estimated to be representing around 76% of the Catalan population. 4 Healthcare setting / type of data Primary care – gps , and hospital inpatient care. SIDIAP captured data includes routine visits, socio demographic information , diagnoses, laboratory tests, drugs (prescribed and dispensed), referrals, and lifestyle information. 5 Data collection process Outpatient electronic health records, and Inpatient hospital electronic health records, and Other. Data is entered by primary care physicians upon healthcare contact, supplemented with hospital discharge records. The Institut Catala de la Salut is the owner of the data and acts as the data controller. 6 General representativeness It was previously shown that the captured SIDIAP population is highly representative of the entire Catalan region in terms of geographic, age, and sex distributions. 7 Data content /source coding SIDIAP data covers all services that occur at the Primary Care Centres, as well as support services, such as sexual and reproductive health or home end-of-life care. Drugs are coded in ATC-WHO terminology in the source data. Health outcomes are captured in ICD-10CM codes. The SIDIAP contains all laboratory tests and results performed in primary health centres. Demographics, geographical, as well as socio-economic factors are recorded for each patient. 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. No. 9 Quality control (database specific) Internal and external validation processes are carried out to determine the data quality of the SIDIAP information at each data update. These include stratifying the data by geographical regions and year in order to identify differences in data collection that need to be harmonized (e.g. recording of specific information under different codes). The measurement units of variables measuring one characteristic are also homogenized (e.g. transformation of the data from every laboratory that measures haemoglobin to grams per decilitre). Visual inspection of all data included in the database by week is also conducted, allowing one to see temporal patterns in the registry of a certain variable. With this information, the SIDIAP team can issue recommendations to researchers about the most common variable(s) where certain information is recorded (e.g., there are several variables with information concerning the women’s menopausal status and with these visual inspection tools the SIDIAP team can inform the researchers about which related variables have the largest number of records and could be more helpful to capture menopause). Data availability (longitudinally and reliability), plausibility (range checks and unusual values), and consistency are inspected through visualisation tools. In addition, before accessing the data for a requested project, research teams have access to a quality-control report. This document contains counts, years, percentiles, maximums and minimums, incidences, and prevalence of the data requested for the project, allowing detection of inconsistencies in the data extraction prior to data delivery. External validation processes of the SIDIAP database mainly include assessing the data recorded in SIDIAP through linkage to external gold standard data sources, by analysing free text, or by sending questionnaires to health professionals. 10 Linkage SIDIAP is linked to a hospital discharge database, pharmacy dispensation, and primary care laboratories. It can also be linked to other registries in Catalonia on a project-by-project basis. 11 Vital status Mortality is fully captured in SIDIAP. The cause of death is not available but can be linked to the Spanish death registry on a project-by-project basis. 12 Limitations The SIDIAP data is not representative of individuals not using public primary care, and conditions that are usually followed by specialist care might not be properly captured. In addition, there is limited information on lifestyle variables (not always requested in primary care visit and, therefore, the information is missing in many cases) . Patients are followed until Death or when transferring to another primary health care centre that does not contribute to SIDIAP. 13 Main references Recalde M, Rodríguez C,Burn E,Far M,García D,Carrere -Molina J,Benítez M,Moleras A,Pistillo A,Bolíbar B,Aragón M,Duarte -Salles T "Data Resource Profile: The Information System for Research in Primary Care (SIDIAP)." International journal of epidemiology (2022): 35415748 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/50190 Website : https://www.sidiap.org/index.php/en Clinical Practice Research Datalink GOLD (Oxford) (CPRD GOLD) # Section Description 1 Database Identification and country CPRD GOLD (Clinical Practice Research Datalink GOLD ) United Kingdom 2 Data partner information section University of Oxford NDORMS 3 Coverage and timespan Data collection since: 1987 Extent: Nation-wide. CPRD GOLD consists of patients in contributing practices using Vision software. Historically this covered the whole of the UK, but the number of contributing practices in the England is dropping. In January 2025 only 3 practices from England were a part of CPRD GOLD, while historical patient data were from the whole of the UK, and will continue to be so. In the future, no practices from England will be present, only practices from Scotland, Wales, and Northern Ireland. 4 Healthcare setting / type of data Primary care – gps , and primary care specialists (e.g. paediatricians), and secondary care – specialists (ambulatory or hospital outpatient care), and hospital inpatient care. CPRD GOLD data include patient demographics, biological measurements, clinical symptoms and diagnoses, referrals to specialist/hospital and their outcome, laboratory tests/results, and prescribed medications. 5 Data collection process Outpatient electronic health records. Data is entered by clinicians into the EHR. Data is processed by CPRD and provides data releases for research. 6 General representativeness CPRD GOLD has been assessed and found to be broadly representative of the UK general population in terms of age, gender, and ethnicity. In CPRD GOLD in January 2025 there were 2,730,707 current acceptable patients (i.e. registered at currently contributing practices that use Vision software, excluding transferred out, deceased patients, and those flagged by CPRD as not acceptable for clinical research for data quality issues). This equals to 4.07%, based on the UK population estimates of 67,026,300 from the Office of National Statistics (mid-2023). Current patients are only from Scotland, Wales, and Northern Ireland. Historically, GOLD does contain data from England as well. 7 Data content /source coding Gemscript , Read, dm+d 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. In GOLD, a patient can be registered under different ID numbers upon changing practice or re-registration. Researchers are not able to identify these patients, as the data are anonymised. However, GOLD covers less than 5% of the current UK GP practices and it is unlikely that an individual who does change GP practice ends up in another GP practice which uses the Vision software and accepts the CPRD data collection agreement. The very small number of duplicated IDs will have different observation periods and should not have an impact on the data analyses. 9 Quality control (database specific) CPRD GOLD only includes practices whose data quality is assessed to be up-to-standard ( uts ). Each practice is associated to an uts date set when the data quality standards become satisfactory, and CPRD recommend using only longitudinal data starting from this uts date. Every time CPRD collect the EHR from a practice, checks are run for the data quality standards and if they are not adequate, the EHR is not accepted. When the data quality becomes acceptable again, CPRD updates the practice uts date. CPRD also check data quality standards at the patient level and associate each patient to a flag, reporting if its data is acceptable for clinical research. Only patients with acceptable data quality are included in the population to be mapped to CDM. 10 Linkage CPRD GOLD can be linked to several sources, however our Oxford OMOP CDM is only linked to the CPRD GOLD Ethnicity Record and to the CPRD Townsend Deprivation Index at Practice Level 11 Vital status Vital status is retrieved from the GP records. Population registry (ONS) data can be requested on a study-by-study basis and linked. This data only covers England and is planned to be mapped to OMOP in the future. The cause of death is not captured. 12 Limitations The main limitation is due to the fact that CPRD GOLD is limited to GP records, and although it contains information on referrals and discharge letters, it may not fully capture specific hospital information. Events from hospital and specialist care are not covered. 13 Main references Sanchez-Santos MT, Axson EL,Dedman D,Delmestri A "Data Resource Profile Update: CPRD GOLD." International journal of epidemiology (2025): 40499193 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/1111113 Website : https://cprd.com UK BioBank (UKBB) # Section Description 1 Database Identification and country UKBB (UK BioBank ) United Kingdom 2 Data partner information section Oxford University NDORMS 3 Coverage and timespan Data collection since: 2006 Extent: Nation-wide. People recruited from whole of the UK. 4 Healthcare setting / type of data Primary care – gps , and primary care specialists (e.g. paediatricians), and secondary care – specialists (ambulatory or hospital outpatient care), and hospital inpatient care, and other (specify). UK Biobank is made by a rich variety of data sources, which include genetic data, primary care data, hospital inpatient data, death data, and cancer registry. 5 Data collection process Inpatient hospital electronic health records, and Registries, and Biobank. The baseline assessment is consisting of both patient reported data (questionnaire) and physical measurements. GP and hospital data, as well as death and cancer registry records, are linked afterwards and are subject to data validation: https://biobank.ctsu.ox.ac.uk/~bbdatan/Data_cleaning_overall_doc_showcase_v1.pdf 6 General representativeness The database population consists of volunteers aged 40-69years. We can expect that volunteers might have been more willing to participate if living closer to one of the 22 recruitment centres or if more interested in health issues compared to the general population. These aspects might have introduced an unavoidable bias in the cohort. 7 Data content /source coding READ2, READ3, DM+D, ICD9, ICD10, OPCS3, OPCS4, ICD-O-3 are used. 8 Data Harmonization The data has been mapped to the OMOP CDM v5.4 and the OMOP standard vocabularies (SNOMED, RxNorm, LOINC). The format, structural and semantic conformance has been verified upon onboarding into the DARWIN EU® data network. No. 9 Quality control (database specific) All UK BioBank data are provided already curated and each of the many datasets have specific curation algorithms and procedures. As always, primary care and hospital data, which come from real-world setting, need special attention regarding data quality. Please refer to the link below for specific details https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/primary_care_data.pdf https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/HospitalEpisodeStatistics.pdf 10 Linkage The database contains liked data from death and cancer registry, GP and hospital for the participants. 11 Vital status Linked to the national death registry. 12 Limitations The UKBB source data will not be updated anymore and have no new records after December 2022. There is no day's supply information captured in the source. GP prescription data are available for 45% of the cohort. There is no information on dispensed medicines. GP laboratory tests and results are not available. 13 Main references Hewitt J, Walters M,Padmanabhan S,Dawson J "Cohort profile of the UK Biobank: diagnosis and characteristics of cerebrovascular disease." BMJ open (2016): 27006341 14 Link to HMA-EMA catalogue and database webpage HMA-EMA Catalogue entry : https://catalogues.ema.europa.eu/data-source/1111233 Website : https://www.ukbiobank.ac.uk/ ANNEX II. Fitness for use assessment Data source justification for inclusion and key characteristics The selected data source s met the criteria required to capture outcomes of interest and relevant data, enabling a patient-level characterisation of newly diagnosed individuals with cancer across different European settings and regions. The main criterion was a meaningful number of person counts for the population of interest ( individuals with cancer) and outcomes (thromboembolic events) assessed at the feasibility stage for all data sources included in the study. Data source s were also selected based on European representativeness. Not all data source s ha d records of all outcomes of interest. Additional criteria reflect other data quality domains assessed at the DARWIN EU® data partners onboarding stage. With every new release of the data partners OMOP CDM, the DARWIN EU® coordination centre also receives new results of the CdmOnboarding , DashboardExport , and DataQualityDashboard packages and assesses the quality of the data. No open quality issues related to the study population and outcomes were present for any of the data sources selected for the study. Relevance was also assessed based on the previous research related to the study population of the outcome. Previously, IPCI, EBB, SIDIAP, CPRD, IQVIA DA Germany , UKBB, and EBB were used in studies with thromboembolic events as an outcome. (Ali et al., 2020, Mercadé-Besora et al., 2024, Li et al., 2022, Voss et al, 2023). CPRD, FinOMOP-THL, UKBB, SIDIAP, IPCI, and UKBB were used in studies repeated on individuals with cancer (Hagberg et al., 2023; Corby et al., 2024, Leinonen et al., 2017, Smith et al., 2024; Recalde et al, 2019; van Soest 2008; Chen et al., 2024). In addition to that, DK-DHR is a nationwide , fully representative data source that includes information from t he National Patient Registry and t he National Cancer Register . Design elements Operational definition Data elements for valid capture Criticality of the quality of the element, including justification where relevant Study population Objective 1 Inclusion criteria First diagnosis of a selected cancer (index date) between 01/01/2016 and 31/12/2022 Age ≥18 years at index date Minimum 365 days of available history before index date Index date ≥365 days prior to end of data availability of the data source Exclusion criteria Diagnoses of multiple primary tumours at index date History of cancer diagnosis ever before index date Outcome during the year prior to index date Objective 2 Inclusion criteria Included in the study population of objective 1 Occurrence of the outcome during follow-up First diagnosis of a selected cancer Diagnosis of: Deep vein thrombosis (DVT) Pulmonary embolisms (PE) Venous thromboembolism (VTE, composite of DVT and PE) Pelvic venous thrombosis (PVT) Splanchnic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis Retinal vein thrombosis (RVT), including retinal central vein thrombosis Disseminated intravascular coagulation (DIC ) Low/Medium/ High Treatment/ exposure Not applicable. Low/Medium/High Comparator group (if relevant) Not applicable. Low/Medium/High Outcomes (if relevant) All objectives The thromboembolic event outcomes in this study are identical to those in EUPAS1000000440 , of which this is a routinely repeated study : Deep vein thrombosis (DVT) Pulmonary embolisms (PE) Venous thromboembolism (VTE, composite of DVT and PE) Pelvic venous thrombosis (PVT) Splanchnic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis Retinal vein thrombosis (RVT), including retinal central vein thrombosis Disseminated intravascular coagulation (DIC ) Each of the specific thromboembolic events was a primary, binary outcome. The outcome was assessed at any diagnosis position in the electronic health record and from any of the care settings in each data source. The list of concepts, from EUPAS1000000440 and based on SNOMED codes and aligned with previous studies that used OMOP CDM and VTE as an outcome (Burn et al., 2022), is provided in Annex I V . Diagnosis of: Deep vein thrombosis (DVT) Pulmonary embolisms (PE) Venous thromboembolism (VTE, composite of DVT and PE) Pelvic venous thrombosis (PVT) Splanchnic vein thrombosis (SVT), including hepatic and extra-hepatic vein thrombosis Retinal vein thrombosis (RVT), including retinal central vein thrombosis Disseminated intravascular coagulation (DIC ) Low/Medium/ High Covariates (including confounders if relevant) All Objectives Sex Female/male Age groups (years) at index date: 18–34 35–44 45–54 55–64 65–74 75–84 ≥85 Age at index date will be calculated using January 1st of the year of birth as proxy for the actual birthday. Date/month is either not present or cannot be made available for governance reasons. If available, date is often set to first of the month for personal privacy. Study subperiod : 2016–2019 2020-2022 The status of each covariate will be assessed at index date. Each covariate will be used to stratify the results. Date of first diagnosis of selected cancer (index date) Sex Age at index date Low/Medium/ High Follow-up time (if relevant) For both objectives, follow-up will start on the date of cancer diagnosis (index date) and end on the earliest of occurrence of the outcome, loss to follow-up, end of data availability, death, or end of the study period (31/12/2024) . Date of first diagnosis of selected cancer (index date) Date of thromboembolic event Data of end of data availability Death date Low/Medium/ High EMA Data Quality Framework for EU medicines regulation: application to Real-World Data for more information ( https://www.ema.europa.eu/system/files/documents/other/data-quality-framework-eu-medicines-regulation-application-real-world-data_en.pdf ) ANNEX I II. Operational and reporting considerations DATA MANAGEMENT Data management All data sources have previously mapped their data to the OMOP common data model. This enables the use of standardised analytics and using DARWIN EU ® tools across the network , since the structure of the data and the terminology system is harmonised. The OMOP CDM was 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 . The analytic code for this study will be written in R and will use standardized analytics wherever possible. Each data partner will execute the study code against their data source containing patient-level data and then return the results (csv files) , 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. Data storage and protection For this study, participants from various EU member states will process personal data from individuals that is collected in national/regional electronic health record data sources. 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 data sources 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 EU ® Remote Research Environment (RRE) . These output files do not contain any data that allow identification of subjects included in the study. The RRE 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. QUALITY CONTROL D ata source quality control When defining drug cohorts, non-systemic products will be excluded from the list of included codes summarised on the ingredient level. When defining cohorts for indications, a systematic search of possible codes for inclusion will be identified using the CodelistGenerator R package ( https://github.com/darwin-eu/CodelistGenerator ). This package allows the user to define a search strategy and will use this to query the vocabulary tables of the OMOP common data model so as to find potentially relevant codes. In addition, the CohortDiagnostics ( https://github.com/OHDSI/CohortDiagnostics ) and DrugExposureDiagnostics ( https://cran.r-project.org/web/packages/DrugExposureDiagnostics/index.html ) R packages will be run , if needed , to assess the use of different codes across the data sources contributing to the study and identify any codes potentially omitted in error. The DrugExposureDiagnostics package evaluates ingredient-specific attributes and patterns in drug exposure records. The study code will be based on DARWIN EU® R packages: IncidencePrevalence to estimate Incidence and Prevalence, DrugUtilisation to characterise the drug use, and CohortCharacteristics to characterise the cohort by indication. These packages will include numerous automated unit tests to ensure the validity of the codes, alongside software peer review and user testing. The R package will be made publicly available via GitHub. 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. The full set of underlying aggregated data used in the dashboard will also be made available , if requested. ANNEX I V. List of stand-alone documents Concepts to define individuals with cancer (populations of interest) are available in a stand-alone document (DARWIN_EU_P3_C3_005 _Cancer_phenotypes.xlsx). Tables S1 to S7 include concepts used to define outcomes (thromboembolic events). Table S1. List of concepts used to define deep vein thrombosis (DVT) . Concept ID Concept Name Domain Vocabulary 762047 Acute bilateral thrombosis of subclavian veins Condition SNOMED 762148 Acute deep vein thrombosis of bilateral iliac veins Condition SNOMED 37169261 Acute deep vein thrombosis of bilateral lower limbs following procedure Condition SNOMED 37169249 Acute deep vein thrombosis of bilateral upper limbs following procedure Condition SNOMED 35616028 Acute deep vein thrombosis of left iliac vein Condition SNOMED 35615035 Acute deep vein thrombosis of left lower limb following procedure Condition SNOMED 35615031 Acute deep vein thrombosis of left upper limb following procedure Condition SNOMED 43531681 Acute deep vein thrombosis of lower limb Condition SNOMED 35616027 Acute deep vein thrombosis of right iliac vein Condition SNOMED 35615034 Acute deep vein thrombosis of right lower limb following procedure Condition SNOMED 35615030 Acute deep vein thrombosis of right upper limb following procedure Condition SNOMED 44782746 Acute deep venous thrombosis Condition SNOMED 44782751 Acute deep venous thrombosis of axillary vein Condition SNOMED 762008 Acute deep venous thrombosis of bilateral axillary veins Condition SNOMED 760875 Acute deep venous thrombosis of bilateral calves Condition SNOMED 765155 Acute deep venous thrombosis of bilateral ileofemoral veins Condition SNOMED 762017 Acute deep venous thrombosis of bilateral internal jugular veins Condition SNOMED 762417 Acute deep venous thrombosis of bilateral legs Condition SNOMED 761461 Acute deep venous thrombosis of bilateral pelvic veins Condition SNOMED 762020 Acute deep venous thrombosis of bilateral popliteal veins Condition SNOMED 765546 Acute deep venous thrombosis of bilateral tibial veins Condition SNOMED 762004 Acute deep venous thrombosis of both upper extremities Condition SNOMED 44782742 Acute deep venous thrombosis of calf Condition SNOMED 44782747 Acute deep venous thrombosis of femoral vein Condition SNOMED 762015 Acute deep venous thrombosis of ileofemoral vein of left leg Condition SNOMED 765541 Acute deep venous thrombosis of ileofemoral vein of right lower extremity Condition SNOMED 44782748 Acute deep venous thrombosis of iliofemoral vein Condition SNOMED 44782752 Acute deep venous thrombosis of internal jugular vein Condition SNOMED 762009 Acute deep venous thrombosis of left axillary vein Condition SNOMED 760876 Acute deep venous thrombosis of left calf Condition SNOMED 765540 Acute deep venous thrombosis of left femoral vein Condition SNOMED 765922 Acute deep venous thrombosis of left internal jugular vein Condition SNOMED 762418 Acute deep venous thrombosis of left lower extremity Condition SNOMED 761462 Acute deep venous thrombosis of left pelvic vein Condition SNOMED 618482 Acute deep venous thrombosis of left peroneal vein Condition SNOMED 765537 Acute deep venous thrombosis of left upper extremity Condition SNOMED 44782767 Acute deep venous thrombosis of lower extremity as complication of procedure Condition SNOMED 44782761 Acute deep venous thrombosis of pelvic vein Condition SNOMED 762022 Acute deep venous thrombosis of politeal vein of right leg Condition SNOMED 44782743 Acute deep venous thrombosis of popliteal vein Condition SNOMED 762021 Acute deep venous thrombosis of popliteal vein of left leg Condition SNOMED 762010 Acute deep venous thrombosis of right axillary vein Condition SNOMED 760877 Acute deep venous thrombosis of right calf Condition SNOMED 762013 Acute deep venous thrombosis of right femoral vein Condition SNOMED 762018 Acute deep venous thrombosis of right internal jugular vein Condition SNOMED 762419 Acute deep venous thrombosis of right lower extremity Condition SNOMED 765229 Acute deep venous thrombosis of right pelvic vein Condition SNOMED 618681 Acute deep venous thrombosis of right peroneal vein Condition SNOMED 762005 Acute deep venous thrombosis of right upper extremity Condition SNOMED 44782745 Acute deep venous thrombosis of thigh Condition SNOMED 44782744 Acute deep venous thrombosis of tibial vein Condition SNOMED 762026 Acute deep venous thrombosis of tibial vein of left leg Condition SNOMED 765156 Acute deep venous thrombosis of tibial vein of right leg Condition SNOMED 44782421 Acute deep venous thrombosis of upper extremity Condition SNOMED 44782766 Acute deep venous thrombosis of upper extremity as complication of procedure Condition SNOMED 37171353 Acute ischemia of colon due to thrombosis of mesenteric vein Condition SNOMED 37170675 Acute ischemia of small intestine due to thrombosis of mesenteric vein Condition SNOMED 762048 Acute thrombosis of left subclavian vein Condition SNOMED 45757410 Acute thrombosis of mesenteric vein Condition SNOMED 762049 Acute thrombosis of right subclavian vein Condition SNOMED 36712892 Acute thrombosis of splenic vein Condition SNOMED 44782762 Acute thrombosis of subclavian vein Condition SNOMED 4179911 Axillary vein thrombosis Condition SNOMED 37109253 Bilateral acute deep vein thrombosis of femoral veins Condition SNOMED 618678 Bilateral acute deep venous thrombosis of peroneal veins Condition SNOMED 609003 Bilateral deep femoral vein thrombophlebitis Condition SNOMED 3179900 Bilateral deep vein thromboses Condition Nebraska Lexicon 40478951 Bilateral deep vein thrombosis of lower extremities Condition SNOMED 609002 Bilateral femoral vein thrombophlebitis Condition SNOMED 608965 Bilateral iliac vein thrombophlebitis Condition SNOMED 1245776 Bilateral popliteal vein thrombophlebitis Condition SNOMED 609006 Bilateral tibial vein thrombophlebitis Condition SNOMED 4042396 Deep thrombophlebitis Condition SNOMED 4046884 Deep vein thrombosis of leg related to air travel Condition SNOMED 3655221 Deep vein thrombosis of lower extremity due to intravenous drug use Condition SNOMED 4133004 Deep venous thrombosis Condition SNOMED 761013 Deep venous thrombosis of bilateral pelvic veins Condition SNOMED 37163011 Deep venous thrombosis of calf Condition SNOMED 45773536 Deep venous thrombosis of femoropopliteal vein Condition SNOMED 763942 Deep venous thrombosis of left lower extremity Condition SNOMED 1075379 Deep venous thrombosis of left posterior tibial vein Condition SNOMED 761980 Deep venous thrombosis of left upper extremity Condition SNOMED 443537 Deep venous thrombosis of lower extremity Condition SNOMED 4133975 Deep venous thrombosis of pelvic vein Condition SNOMED 40480555 Deep venous thrombosis of peroneal vein Condition SNOMED 1075377 Deep venous thrombosis of posterior tibial vein Condition SNOMED 4322565 Deep venous thrombosis of profunda femoris vein Condition SNOMED 763941 Deep venous thrombosis of right lower extremity Condition SNOMED 1075378 Deep venous thrombosis of right posterior tibial vein Condition SNOMED 761928 Deep venous thrombosis of right upper extremity Condition SNOMED 4207899 Deep venous thrombosis of tibial vein Condition SNOMED 4028057 Deep venous thrombosis of upper extremity Condition SNOMED 193512 Embolism and thrombosis of the renal vein Condition SNOMED 435565 Embolism and thrombosis of the vena cava Condition SNOMED 4258295 Embolism from thrombosis of vein of distal lower extremity Condition SNOMED 40481089 Embolism from thrombosis of vein of lower extremity Condition SNOMED 40479840 Embolism from thrombosis of vein of thigh Condition SNOMED 4119760 Iliofemoral deep vein thrombosis Condition SNOMED 4124856 Inferior mesenteric vein thrombosis Condition SNOMED 608964 Left iliac vein thrombophlebitis Condition SNOMED 602592 Left peroneal vein thrombophlebitis Condition SNOMED 600938 Left subclavian vein thrombophlebitis Condition SNOMED 37164448 Lemierre syndrome Condition SNOMED 4281689 Phlegmasia alba dolens Condition SNOMED 4284538 Phlegmasia cerulea dolens Condition SNOMED 3185768 Popliteal vein thrombosis Condition Nebraska Lexicon 4309333 Postoperative deep vein thrombosis Condition SNOMED 1245858 Postpartum acute deep vein thrombosis Condition SNOMED 46285905 Provoked deep vein thrombosis Condition SNOMED 608963 Right iliac vein thrombophlebitis Condition SNOMED 602583 Right peroneal vein thrombophlebitis Condition SNOMED 600939 Right subclavian vein thrombophlebitis Condition SNOMED 4033521 Splenic vein thrombosis Condition SNOMED 4055089 Superior mesenteric vein thrombosis Condition SNOMED 4230403 Thrombophlebitis of axillary vein Condition SNOMED 4069561 Thrombophlebitis of deep femoral vein Condition SNOMED 761831 Thrombophlebitis of deep vein of bilateral lower limbs Condition SNOMED 761830 Thrombophlebitis of deep vein of left lower limb Condition SNOMED 761808 Thrombophlebitis of deep vein of left upper limb Condition SNOMED 761832 Thrombophlebitis of deep vein of right lower limb Condition SNOMED 761809 Thrombophlebitis of deep vein of right upper limb Condition SNOMED 4221821 Thrombophlebitis of deep veins of lower extremity Condition SNOMED 440750 Thrombophlebitis of deep veins of upper extremities Condition SNOMED 4203618 Thrombophlebitis of femoropopliteal vein Condition SNOMED 4176614 Thrombophlebitis of iliac vein Condition SNOMED 764715 Thrombophlebitis of internal jugular vein Condition SNOMED 608904 Thrombophlebitis of left axillary vein Condition SNOMED 761821 Thrombophlebitis of left deep femoral vein Condition SNOMED 761819 Thrombophlebitis of left femoral vein Condition SNOMED 609000 Thrombophlebitis of left popliteal vein Condition SNOMED 609005 Thrombophlebitis of left tibial vein Condition SNOMED 4318407 Thrombophlebitis of mesenteric vein Condition SNOMED 608903 Thrombophlebitis of right axillary vein Condition SNOMED 761820 Thrombophlebitis of right deep femoral vein Condition SNOMED 761818 Thrombophlebitis of right femoral vein Condition SNOMED 609001 Thrombophlebitis of right popliteal vein Condition SNOMED 609004 Thrombophlebitis of right tibial vein Condition SNOMED 4205652 Thrombophlebitis of subclavian vein Condition SNOMED 4110339 Thrombophlebitis of the anterior tibial vein Condition SNOMED 4111868 Thrombophlebitis of the common iliac vein Condition SNOMED 4110343 Thrombophlebitis of the external iliac vein Condition SNOMED 439314 Thrombophlebitis of the femoral vein Condition SNOMED 4109877 Thrombophlebitis of the internal iliac vein Condition SNOMED 4112171 Thrombophlebitis of the popliteal vein Condition SNOMED 4112172 Thrombophlebitis of the posterior tibial vein Condition SNOMED 4250765 Thrombophlebitis of tibial vein Condition SNOMED 42538533 Thrombosis of iliac vein Condition SNOMED 44811347 Thrombosis of internal jugular vein Condition SNOMED 765049 Thrombosis of left peroneal vein Condition SNOMED 4317289 Thrombosis of mesenteric vein Condition SNOMED 4203836 Thrombosis of subclavian vein Condition SNOMED 4175649 Thrombosis of the popliteal vein Condition SNOMED 4153353 Traumatic thrombosis of axillary vein Condition SNOMED 46285904 Unprovoked deep vein thrombosis Condition SNOMED 37163265 Venous thromboembolism due to thrombosis of vein of lower limb Condition SNOMED Table S2. List of concepts used to define pulmonary embolism (PE). Concept ID Concept Name Domain Vocabulary 608954 Acute cor pulmonale due to septic pulmonary embolism Condition SNOMED 4120091 Acute massive pulmonary embolism Condition SNOMED 45768439 Acute pulmonary embolism Condition SNOMED 45768888 Acute pulmonary thromboembolism Condition SNOMED 762808 Infarction of lung due to embolus Condition SNOMED 40480461 Infarction of lung due to iatrogenic pulmonary embolism Condition SNOMED 4108681 Postoperative pulmonary embolus Condition SNOMED 37160752 Postoperative pulmonary thromboembolism Condition SNOMED 1244882 Pulmonary artery embolism due to foreign body Condition SNOMED 440417 Pulmonary embolism Condition SNOMED 37109911 Pulmonary embolism due to and following acute myocardial infarction Condition SNOMED 37016922 Pulmonary embolism on long-term anticoagulation therapy Condition SNOMED 43530605 Pulmonary embolism with pulmonary infarction Condition SNOMED 4253796 Pulmonary microemboli Condition SNOMED 4121618 Pulmonary thromboembolism Condition SNOMED 36713113 Saddle embolus of pulmonary artery Condition SNOMED 35615055 Saddle embolus of pulmonary artery with acute cor pulmonale Condition SNOMED 40479606 Septic pulmonary embolism Condition SNOMED 4119607 Subacute massive pulmonary embolism Condition SNOMED Table S 3 . List of concepts used to define venous thromboembolism (VTE ). Concept ID Concept Name Domain Vocabulary 762047 Acute bilateral thrombosis of subclavian veins Condition SNOMED 608954 Acute cor pulmonale due to septic pulmonary embolism Condition SNOMED 762148 Acute deep vein thrombosis of bilateral iliac veins Condition SNOMED 37169261 Acute deep vein thrombosis of bilateral lower limbs following procedure Condition SNOMED 37169249 Acute deep vein thrombosis of bilateral upper limbs following procedure Condition SNOMED 35616028 Acute deep vein thrombosis of left iliac vein Condition SNOMED 35615035 Acute deep vein thrombosis of left lower limb following procedure Condition SNOMED 35615031 Acute deep vein thrombosis of left upper limb following procedure Condition SNOMED 43531681 Acute deep vein thrombosis of lower limb Condition SNOMED 35616027 Acute deep vein thrombosis of right iliac vein Condition SNOMED 35615034 Acute deep vein thrombosis of right lower limb following procedure Condition SNOMED 35615030 Acute deep vein thrombosis of right upper limb following procedure Condition SNOMED 44782746 Acute deep venous thrombosis Condition SNOMED 44782751 Acute deep venous thrombosis of axillary vein Condition SNOMED 762008 Acute deep venous thrombosis of bilateral axillary veins Condition SNOMED 760875 Acute deep venous thrombosis of bilateral calves Condition SNOMED 765155 Acute deep venous thrombosis of bilateral ileofemoral veins Condition SNOMED 762017 Acute deep venous thrombosis of bilateral internal jugular veins Condition SNOMED 762417 Acute deep venous thrombosis of bilateral legs Condition SNOMED 761461 Acute deep venous thrombosis of bilateral pelvic veins Condition SNOMED 762020 Acute deep venous thrombosis of bilateral popliteal veins Condition SNOMED 765546 Acute deep venous thrombosis of bilateral tibial veins Condition SNOMED 762004 Acute deep venous thrombosis of both upper extremities Condition SNOMED 44782742 Acute deep venous thrombosis of calf Condition SNOMED 44782747 Acute deep venous thrombosis of femoral vein Condition SNOMED 762015 Acute deep venous thrombosis of ileofemoral vein of left leg Condition SNOMED 765541 Acute deep venous thrombosis of ileofemoral vein of right lower extremity Condition SNOMED 44782748 Acute deep venous thrombosis of iliofemoral vein Condition SNOMED 44782752 Acute deep venous thrombosis of internal jugular vein Condition SNOMED 762009 Acute deep venous thrombosis of left axillary vein Condition SNOMED 760876 Acute deep venous thrombosis of left calf Condition SNOMED 765540 Acute deep venous thrombosis of left femoral vein Condition SNOMED 765922 Acute deep venous thrombosis of left internal jugular vein Condition SNOMED 762418 Acute deep venous thrombosis of left lower extremity Condition SNOMED 761462 Acute deep venous thrombosis of left pelvic vein Condition SNOMED 618482 Acute deep venous thrombosis of left peroneal vein Condition SNOMED 765537 Acute deep venous thrombosis of left upper extremity Condition SNOMED 44782767 Acute deep venous thrombosis of lower extremity as complication of procedure Condition SNOMED 44782761 Acute deep venous thrombosis of pelvic vein Condition SNOMED 762022 Acute deep venous thrombosis of politeal vein of right leg Condition SNOMED 44782743 Acute deep venous thrombosis of popliteal vein Condition SNOMED 762021 Acute deep venous thrombosis of popliteal vein of left leg Condition SNOMED 762010 Acute deep venous thrombosis of right axillary vein Condition SNOMED 760877 Acute deep venous thrombosis of right calf Condition SNOMED 762013 Acute deep venous thrombosis of right femoral vein Condition SNOMED 762018 Acute deep venous thrombosis of right internal jugular vein Condition SNOMED 762419 Acute deep venous thrombosis of right lower extremity Condition SNOMED 765229 Acute deep venous thrombosis of right pelvic vein Condition SNOMED 618681 Acute deep venous thrombosis of right peroneal vein Condition SNOMED 762005 Acute deep venous thrombosis of right upper extremity Condition SNOMED 44782745 Acute deep venous thrombosis of thigh Condition SNOMED 44782744 Acute deep venous thrombosis of tibial vein Condition SNOMED 762026 Acute deep venous thrombosis of tibial vein of left leg Condition SNOMED 765156 Acute deep venous thrombosis of tibial vein of right leg Condition SNOMED 44782421 Acute deep venous thrombosis of upper extremity Condition SNOMED 44782766 Acute deep venous thrombosis of upper extremity as complication of procedure Condition SNOMED 37171353 Acute ischemia of colon due to thrombosis of mesenteric vein Condition SNOMED 37170675 Acute ischemia of small intestine due to thrombosis of mesenteric vein Condition SNOMED 4120091 Acute massive pulmonary embolism Condition SNOMED 45768439 Acute pulmonary embolism Condition SNOMED 45768888 Acute pulmonary thromboembolism Condition SNOMED 762048 Acute thrombosis of left subclavian vein Condition SNOMED 45757410 Acute thrombosis of mesenteric vein Condition SNOMED 762049 Acute thrombosis of right subclavian vein Condition SNOMED 36712892 Acute thrombosis of splenic vein Condition SNOMED 44782762 Acute thrombosis of subclavian vein Condition SNOMED 4179911 Axillary vein thrombosis Condition SNOMED 37109253 Bilateral acute deep vein thrombosis of femoral veins Condition SNOMED 618678 Bilateral acute deep venous thrombosis of peroneal veins Condition SNOMED 609003 Bilateral deep femoral vein thrombophlebitis Condition SNOMED 3179900 Bilateral deep vein thromboses Condition Nebraska Lexicon 40478951 Bilateral deep vein thrombosis of lower extremities Condition SNOMED 609002 Bilateral femoral vein thrombophlebitis Condition SNOMED 608965 Bilateral iliac vein thrombophlebitis Condition SNOMED 1245776 Bilateral popliteal vein thrombophlebitis Condition SNOMED 609006 Bilateral tibial vein thrombophlebitis Condition SNOMED 44782732 Chronic pulmonary embolism Condition SNOMED 45768887 Chronic pulmonary thromboembolism Condition SNOMED 45771016 Chronic pulmonary thromboembolism without pulmonary hypertension Condition SNOMED 4042396 Deep thrombophlebitis Condition SNOMED 4046884 Deep vein thrombosis of leg related to air travel Condition SNOMED 3655221 Deep vein thrombosis of lower extremity due to intravenous drug use Condition SNOMED 4133004 Deep venous thrombosis Condition SNOMED 761013 Deep venous thrombosis of bilateral pelvic veins Condition SNOMED 37163011 Deep venous thrombosis of calf Condition SNOMED 45773536 Deep venous thrombosis of femoropopliteal vein Condition SNOMED 763942 Deep venous thrombosis of left lower extremity Condition SNOMED 1075379 Deep venous thrombosis of left posterior tibial vein Condition SNOMED 761980 Deep venous thrombosis of left upper extremity Condition SNOMED 443537 Deep venous thrombosis of lower extremity Condition SNOMED 4133975 Deep venous thrombosis of pelvic vein Condition SNOMED 40480555 Deep venous thrombosis of peroneal vein Condition SNOMED 1075377 Deep venous thrombosis of posterior tibial vein Condition SNOMED 4322565 Deep venous thrombosis of profunda femoris vein Condition SNOMED 763941 Deep venous thrombosis of right lower extremity Condition SNOMED 1075378 Deep venous thrombosis of right posterior tibial vein Condition SNOMED 761928 Deep venous thrombosis of right upper extremity Condition SNOMED 4207899 Deep venous thrombosis of tibial vein Condition SNOMED 4028057 Deep venous thrombosis of upper extremity Condition SNOMED 193512 Embolism and thrombosis of the renal vein Condition SNOMED 435565 Embolism and thrombosis of the vena cava Condition SNOMED 4258295 Embolism from thrombosis of vein of distal lower extremity Condition SNOMED 40481089 Embolism from thrombosis of vein of lower extremity Condition SNOMED 40479840 Embolism from thrombosis of vein of thigh Condition SNOMED 4119760 Iliofemoral deep vein thrombosis Condition SNOMED 43530934 Induced termination of pregnancy complicated by pulmonary embolism Condition SNOMED 762808 Infarction of lung due to embolus Condition SNOMED 40480461 Infarction of lung due to iatrogenic pulmonary embolism Condition SNOMED 4124856 Inferior mesenteric vein thrombosis Condition SNOMED 608964 Left iliac vein thrombophlebitis Condition SNOMED 602592 Left peroneal vein thrombophlebitis Condition SNOMED 600938 Left subclavian vein thrombophlebitis Condition SNOMED 37164448 Lemierre syndrome Condition SNOMED 4281689 Phlegmasia alba dolens Condition SNOMED 4284538 Phlegmasia cerulea dolens Condition SNOMED 3185768 Popliteal vein thrombosis Condition Nebraska Lexicon 4309333 Postoperative deep vein thrombosis Condition SNOMED 4108681 Postoperative pulmonary embolus Condition SNOMED 37160752 Postoperative pulmonary thromboembolism Condition SNOMED 1245858 Postpartum acute deep vein thrombosis Condition SNOMED 46285905 Provoked deep vein thrombosis Condition SNOMED 1244882 Pulmonary artery embolism due to foreign body Condition SNOMED 440417 Pulmonary embolism Condition SNOMED 37109911 Pulmonary embolism due to and following acute myocardial infarction Condition SNOMED 3655209 Pulmonary embolism due to and following ectopic pregnancy Condition SNOMED 3655210 Pulmonary embolism due to and following molar pregnancy Condition SNOMED 37016922 Pulmonary embolism on long-term anticoagulation therapy Condition SNOMED 43530605 Pulmonary embolism with pulmonary infarction Condition SNOMED 4253796 Pulmonary microemboli Condition SNOMED 4121618 Pulmonary thromboembolism Condition SNOMED 4236271 Recurrent pulmonary embolism Condition SNOMED 608963 Right iliac vein thrombophlebitis Condition SNOMED 602583 Right peroneal vein thrombophlebitis Condition SNOMED 600939 Right subclavian vein thrombophlebitis Condition SNOMED 36713113 Saddle embolus of pulmonary artery Condition SNOMED 35615055 Saddle embolus of pulmonary artery with acute cor pulmonale Condition SNOMED 40479606 Septic pulmonary embolism Condition SNOMED 4033521 Splenic vein thrombosis Condition SNOMED 4119607 Subacute massive pulmonary embolism Condition SNOMED 4055089 Superior mesenteric vein thrombosis Condition SNOMED 4230403 Thrombophlebitis of axillary vein Condition SNOMED 4069561 Thrombophlebitis of deep femoral vein Condition SNOMED 761831 Thrombophlebitis of deep vein of bilateral lower limbs Condition SNOMED 761830 Thrombophlebitis of deep vein of left lower limb Condition SNOMED 761808 Thrombophlebitis of deep vein of left upper limb Condition SNOMED 761832 Thrombophlebitis of deep vein of right lower limb Condition SNOMED 761809 Thrombophlebitis of deep vein of right upper limb Condition SNOMED 4221821 Thrombophlebitis of deep veins of lower extremity Condition SNOMED 440750 Thrombophlebitis of deep veins of upper extremities Condition SNOMED 4203618 Thrombophlebitis of femoropopliteal vein Condition SNOMED 4176614 Thrombophlebitis of iliac vein Condition SNOMED 764715 Thrombophlebitis of internal jugular vein Condition SNOMED 608904 Thrombophlebitis of left axillary vein Condition SNOMED 761821 Thrombophlebitis of left deep femoral vein Condition SNOMED 761819 Thrombophlebitis of left femoral vein Condition SNOMED 609000 Thrombophlebitis of left popliteal vein Condition SNOMED 609005 Thrombophlebitis of left tibial vein Condition SNOMED 4318407 Thrombophlebitis of mesenteric vein Condition SNOMED 608903 Thrombophlebitis of right axillary vein Condition SNOMED 761820 Thrombophlebitis of right deep femoral vein Condition SNOMED 761818 Thrombophlebitis of right femoral vein Condition SNOMED 609001 Thrombophlebitis of right popliteal vein Condition SNOMED 609004 Thrombophlebitis of right tibial vein Condition SNOMED 4205652 Thrombophlebitis of subclavian vein Condition SNOMED 4110339 Thrombophlebitis of the anterior tibial vein Condition SNOMED 4111868 Thrombophlebitis of the common iliac vein Condition SNOMED 4110343 Thrombophlebitis of the external iliac vein Condition SNOMED 439314 Thrombophlebitis of the femoral vein Condition SNOMED 4109877 Thrombophlebitis of the internal iliac vein Condition SNOMED 4112171 Thrombophlebitis of the popliteal vein Condition SNOMED 4112172 Thrombophlebitis of the posterior tibial vein Condition SNOMED 4250765 Thrombophlebitis of tibial vein Condition SNOMED 42538533 Thrombosis of iliac vein Condition SNOMED 44811347 Thrombosis of internal jugular vein Condition SNOMED 765049 Thrombosis of left peroneal vein Condition SNOMED 4317289 Thrombosis of mesenteric vein Condition SNOMED 4203836 Thrombosis of subclavian vein Condition SNOMED 4175649 Thrombosis of the popliteal vein Condition SNOMED 4153353 Traumatic thrombosis of axillary vein Condition SNOMED 46285904 Unprovoked deep vein thrombosis Condition SNOMED 37163265 Venous thromboembolism due to thrombosis of vein of lower limb Condition SNOMED Table S 4 . List of concepts used to define pelvic vein thrombosis (PVT) (concept sets included all descendants of listed concepts). Concept ID Concept Name Domain Vocabulary 762148 Acute deep vein thrombosis of bilateral iliac veins Condition SNOMED 35616028 Acute deep vein thrombosis of left iliac vein Condition SNOMED 35616027 Acute deep vein thrombosis of right iliac vein Condition SNOMED 765155 Acute deep venous thrombosis of bilateral ileofemoral veins Condition SNOMED 761461 Acute deep venous thrombosis of bilateral pelvic veins Condition SNOMED 762015 Acute deep venous thrombosis of ileofemoral vein of left leg Condition SNOMED 765541 Acute deep venous thrombosis of ileofemoral vein of right lower extremity Condition SNOMED 761462 Acute deep venous thrombosis of left pelvic vein Condition SNOMED 44782761 Acute deep venous thrombosis of pelvic vein Condition SNOMED 765229 Acute deep venous thrombosis of right pelvic vein Condition SNOMED 608965 Bilateral iliac vein thrombophlebitis Condition SNOMED 765152 Chronic deep vein thrombosis of bilateral iliac veins Condition SNOMED 35616026 Chronic deep vein thrombosis of left iliac vein Condition SNOMED 761439 Chronic deep vein thrombosis of left pelvic vein Condition SNOMED 46271548 Chronic deep vein thrombosis of pelvic vein Condition SNOMED 35616025 Chronic deep vein thrombosis of right iliac vein Condition SNOMED 761441 Chronic deep vein thrombosis of right pelvic vein Condition SNOMED 765542 Chronic deep venous thrombosis of bilateral ileofemoral veins Condition SNOMED 761440 Chronic deep venous thrombosis of bilateral pelvic veins Condition SNOMED 765543 Chronic deep venous thrombosis of left ileofemoral vein Condition SNOMED 762016 Chronic deep venous thrombosis of right ileofemoral vein Condition SNOMED 761013 Deep venous thrombosis of bilateral pelvic veins Condition SNOMED 4133975 Deep venous thrombosis of pelvic vein Condition SNOMED 608964 Left iliac vein thrombophlebitis Condition SNOMED 4285751 Pelvic thrombophlebitis in puerperium Condition SNOMED 608963 Right iliac vein thrombophlebitis Condition SNOMED 4176614 Thrombophlebitis of iliac vein Condition SNOMED 4317290 Thrombophlebitis of pelvic vein Condition SNOMED 4111868 Thrombophlebitis of the common iliac vein Condition SNOMED 4110343 Thrombophlebitis of the external iliac vein Condition SNOMED 4109877 Thrombophlebitis of the internal iliac vein Condition SNOMED 42538533 Thrombosis of iliac vein Condition SNOMED 4319327 Thrombosis of pelvic vein Condition SNOMED Table S 5 . List of concepts used to splanchnic vein thrombosis (SVT). Concept ID Concept Name Domain Vocabulary 37171353 Acute ischemia of colon due to thrombosis of mesenteric vein Condition SNOMED 37170675 Acute ischemia of small intestine due to thrombosis of mesenteric vein Condition SNOMED 45757410 Acute thrombosis of mesenteric vein Condition SNOMED 36712892 Acute thrombosis of splenic vein Condition SNOMED 196715 Budd-Chiari syndrome Condition SNOMED 4301208 Hepatic vein thrombosis Condition SNOMED 4124856 Inferior mesenteric vein thrombosis Condition SNOMED 4092406 Portal thrombophlebitis Condition SNOMED 199837 Portal vein thrombosis Condition SNOMED 4033521 Splenic vein thrombosis Condition SNOMED 4055089 Superior mesenteric vein thrombosis Condition SNOMED 4318407 Thrombophlebitis of mesenteric vein Condition SNOMED 4317289 Thrombosis of mesenteric vein Condition SNOMED Table S 6 . List of concepts used to define retinal vein thrombosis (RVT ). Concept ID Concept Name Domain Vocabulary 437544 Arterial retinal branch occlusion Condition SNOMED 3657106 Bilateral occlusion of branch retinal arteries Condition SNOMED 37310623 Bilateral occlusion of central retinal arteries Condition SNOMED 37169454 Bilateral vascular occlusion of retina of eyes Condition SNOMED 4336004 Branch macular artery occlusion Condition SNOMED 4339013 Branch retinal vein occlusion with macular edema Condition SNOMED 4334248 Branch retinal vein occlusion with neovascularization Condition SNOMED 4199035 Branch retinal vein occlusion with no neovascularization Condition SNOMED 437540 Central retinal artery occlusion Condition SNOMED 313761 Central retinal vein occlusion Condition SNOMED 4208221 Central retinal vein occlusion - ischemic Condition SNOMED 4208222 Central retinal vein occlusion - non-ischemic Condition SNOMED 4339010 Central retinal vein occlusion with macular edema Condition SNOMED 4334246 Central retinal vein occlusion with neovascularization Condition SNOMED 4338905 Cilioretinal artery occlusion Condition SNOMED 42535735 Combined occlusion by thrombus of retinal artery and retinal vein Condition SNOMED 4102317 Incipient occlusion of retinal vein Condition SNOMED 4083482 Macular branch retinal vein occlusion Condition SNOMED 37206377 Occlusion of branch of retinal vein of left eye Condition SNOMED 37206378 Occlusion of branch of retinal vein of right eye Condition SNOMED 37206381 Occlusion of central retinal vein of left eye Condition SNOMED 37206380 Occlusion of central retinal vein of right eye Condition SNOMED 36713329 Occlusion of left branch retinal artery Condition SNOMED 37207955 Occlusion of left central retinal artery Condition SNOMED 3657873 Occlusion of left cilioretinal artery Condition SNOMED 36713330 Occlusion of right branch retinal artery Condition SNOMED 37207895 Occlusion of right central retinal artery Condition SNOMED 3657872 Occlusion of right cilioretinal artery Condition SNOMED 4334245 Retinal artery occlusion Condition SNOMED 4324290 Retinal phlebitis Condition SNOMED 440392 Retinal vascular occlusion Condition SNOMED 3183076 Right branch retinal artery occlusion Condition Nebraska Lexicon 4216561 Thrombophlebitis of retinal vein Condition SNOMED 4187790 Thrombosis of retinal vein Condition SNOMED 3657847 Vascular occlusion of retina of left eye Condition SNOMED 3657848 Vascular occlusion of retina of right eye Condition SNOMED 312622 Venous retinal branch occlusion Condition SNOMED Table S 7 . List of concepts used to define disseminated intravascular coagulation (DIC ). Concept ID Concept Name Domain Vocabulary 37117819 Acquired purpura fulminans Condition SNOMED 436093 Disseminated intravascular coagulation Condition SNOMED 4028488 Purpura fulminans Condition SNOMED ANNEX V . ENCePP checklist for study protocols ENCePP Checklist for Study Protocols (Revision 4) Doc.Ref. EMA/540136/2009 Adopted by the ENCePP Steering Group on 15/10/2018 Study title: DARWIN EU® - Time to onset of thromboembolic events in adults with selected types of cancer EU PAS Register ® number: Study not registered yet Study reference number (if applicable): P4-C2-017 Section 1: Milestones Yes No N/A Section Number 1.1 Does the protocol specify timelines for 1.1.1 Start of data collection X FORMCHECKBOX FORMCHECKBOX 8.5 1.1.2 End of data collection X FORMCHECKBOX FORMCHECKBOX 8.5 1.1.3 Progress report(s) X FORMCHECKBOX FORMCHECKBOX 5 1.1.4 Interim report(s) X FORMCHECKBOX FORMCHECKBOX 5 1.1.5 Registration in the EU PAS Register ® X FORMCHECKBOX FORMCHECKBOX 5 1.1.6 Final report of study results. X FORMCHECKBOX FORMCHECKBOX 5 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 risk management plan, an emerging safety issue) X FORMCHECKBOX FORMCHECKBOX 6 2.1.2 The objective(s) of the study? X FORMCHECKBOX FORMCHECKBOX 7 2.1.3 The target population? (i.e. population or subgroup to whom the study results are intended to be generalised) X FORMCHECKBOX FORMCHECKBOX 8.3 2.1.4 Which hypothesis(-es) is (are) to be tested? FORMCHECKBOX FORMCHECKBOX X 2.1.5 If applicable, that there is no a priori hypothesis? FORMCHECKBOX FORMCHECKBOX X Comments: Section 3: Study design Yes No N/A Section Number 3.1 Is the study design described? (e.g. cohort, case-control, cross-sectional, other design) X FORMCHECKBOX FORMCHECKBOX 8.1 3.2 Does the protocol specify whether the study is based on primary, secondary or combined data collection? X FORMCHECKBOX FORMCHECKBOX 8.4 3.3 Does the protocol specify measures of occurrence? (e.g., rate, risk, prevalence) X FORMCHECKBOX FORMCHECKBOX 8.8.3 3.4 Does the protocol specify measure(s) of association? (e.g. risk, odds ratio, excess risk, rate ratio, hazard ratio, risk/rate difference, number needed to harm (NNH)) FORMCHECKBOX FORMCHECKBOX X 3.5 Does the protocol describe the approach for the collection and reporting of adverse events/adverse reactions? (e.g. adverse events that will not be collected in case of primary data collection) FORMCHECKBOX FORMCHECKBOX X Comments: Section 4: Source and study populations Yes No N/A Section Number 4.1 Is the source population described? X FORMCHECKBOX FORMCHECKBOX Annex I 4.2 Is the planned study population defined in terms of: 4.2.1 Study time period X FORMCHECKBOX FORMCHECKBOX 8.5 4.2.2 Age and sex X FORMCHECKBOX FORMCHECKBOX 8.3 4.2.3 Country of origin X FORMCHECKBOX FORMCHECKBOX 8.4 4.2.4 Disease/indication X FORMCHECKBOX FORMCHECKBOX 8.3 4.2.5 Duration of follow-up X FORMCHECKBOX FORMCHECKBOX 8.2 4.3 Does the protocol define how the study population will be sampled from the source population? (e.g. event or inclusion/exclusion criteria) X FORMCHECKBOX FORMCHECKBOX 8.3 Comments: Section 5: Exposure definition and measurement Yes No N/A Section Number 5.1 Does the protocol describe how the study exposure is defined and measured? (e.g. operational details for defining and categorising exposure, measurement of dose and duration of drug exposure) FORMCHECKBOX FORMCHECKBOX X 5.2 Does the protocol address the validity of the exposure measurement? (e.g. precision, accuracy, use of validation sub-study) FORMCHECKBOX FORMCHECKBOX X 5.3 Is exposure categorised according to time windows? FORMCHECKBOX FORMCHECKBOX X 5.4 Is intensity of exposure addressed? (e.g. dose, duration) FORMCHECKBOX FORMCHECKBOX X 5.5 Is exposure categorised based on biological mechanism of action and taking into account the pharmacokinetics and pharmacodynamics of the drug? FORMCHECKBOX FORMCHECKBOX X 5.6 Is (are) (an) appropriate comparator(s) identified? FORMCHECKBOX FORMCHECKBOX X Comments: Section 6: Outcome definition and measurement Yes No N/A Section Number 6.1 Does the protocol specify the primary and secondary (if applicable) outcome(s) to be investigated? X FORMCHECKBOX FORMCHECKBOX 8.6.2 6.2 Does the protocol describe how the outcomes are defined and measured? X FORMCHECKBOX FORMCHECKBOX 8.6.2 6.3 Does the protocol address the validity of outcome measurement? (e.g. precision, accuracy, sensitivity, specificity, positive predictive value, use of validation sub-study) X FORMCHECKBOX FORMCHECKBOX 9 6.4 Does the protocol describe specific outcomes relevant for Health Technology Assessment? (e.g. HRQoL, QALYs, DALYS, health care services utilisation, burden of disease or treatment, compliance, disease management) FORMCHECKBOX FORMCHECKBOX X Comments: Section 7: Bias Yes No N/A Section Number 7.1 Does the protocol address ways to measure confounding? (e.g. confounding by indication) FORMCHECKBOX FORMCHECKBOX X 7.2 Does the protocol address selection bias? (e.g. healthy user/adherer bias) X FORMCHECKBOX FORMCHECKBOX 9 7.3 Does the protocol address information bias? (e.g. misclassification of exposure and outcomes, time-related bias) X FORMCHECKBOX FORMCHECKBOX 9 Comments: Section 8: Effect measure modification Yes No N/A Section Number 8.1 Does the protocol address effect modifiers? (e.g. collection of data on known effect modifiers, sub-group analyses, anticipated direction of effect) FORMCHECKBOX FORMCHECKBOX X Comments: Section 9: Data sources Yes No N/A Section Number 9.1 Does the protocol describe the data source(s) used in the study for the ascertainment of: 9.1.1 Exposure? (e.g. pharmacy dispensing, general practice prescribing, claims data, self-report, face-to-face interview) FORMCHECKBOX FORMCHECKBOX X 9.1.2 Outcomes? (e.g. clinical records, laboratory markers or values, claims data, self-report, patient interview including scales and questionnaires, vital statistics) X FORMCHECKBOX FORMCHECKBOX Annex I 9.1.3 Covariates and other characteristics? X FORMCHECKBOX FORMCHECKBOX Annex I 9.2 Does the protocol describe the information available from the data source(s) on: 9.2.1 Exposure? (e.g. date of dispensing, drug quantity, dose, number of days of supply prescription, daily dosage, prescriber) FORMCHECKBOX FORMCHECKBOX X 9.2.2 Outcomes? (e.g. date of occurrence, multiple event, severity measures related to event ) X FORMCHECKBOX FORMCHECKBOX Annex I 9.2.3 Covariates and other characteristics? (e.g. age, sex, clinical and drug use history, co-morbidity, co-medications, lifestyle) X FORMCHECKBOX FORMCHECKBOX Annex I 9.3 Is a coding system described for: 9.3.1 Exposure? (e.g. WHO Drug Dictionary, Anatomical Therapeutic Chemical (ATC) Classification System) FORMCHECKBOX FORMCHECKBOX X 9.3.2 Outcomes? (e.g. International Classification of Diseases (ICD), Medical Dictionary for Regulatory Activities (MedDRA)) X FORMCHECKBOX FORMCHECKBOX Annex I 9.3.3 Covariates and other characteristics? X FORMCHECKBOX FORMCHECKBOX Annex I 9.4 Is a linkage method between data sources described? (e.g. based on a unique identifier or other) FORMCHECKBOX FORMCHECKBOX X Comments: Section 10: Analysis plan Yes No N/A Section Number 10.1 Are the statistical methods and the reason for their choice described? X FORMCHECKBOX FORMCHECKBOX 8.8.3 10.2 Is study size and/or statistical precision estimated? FORMCHECKBOX FORMCHECKBOX X 10.3 Are descriptive analyses included? X FORMCHECKBOX FORMCHECKBOX 8.8.3 10.4 Are stratified analyses included? X FORMCHECKBOX FORMCHECKBOX 8.8.3 10.5 Does the plan describe methods for analytic control of confounding? FORMCHECKBOX FORMCHECKBOX X 10.6 Does the plan describe methods for analytic control of outcome misclassification? FORMCHECKBOX FORMCHECKBOX X 10.7 Does the plan describe methods for handling missing data? X FORMCHECKBOX FORMCHECKBOX 8.8.3 10.8 Are relevant sensitivity analyses described? FORMCHECKBOX FORMCHECKBOX X Comments: Section 11: Data management and quality control Yes No N/A Section Number 11.1 Does the protocol provide information on data storage? (e.g. software and IT environment, database maintenance and anti-fraud protection, archiving) X FORMCHECKBOX FORMCHECKBOX Annex I II 11.2 Are methods of quality assurance described? X FORMCHECKBOX FORMCHECKBOX Annex I II 11.3 Is there a system in place for independent review of study results? X FORMCHECKBOX FORMCHECKBOX Annex I I I 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? X FORMCHECKBOX FORMCHECKBOX 9 12.1.2 Information bias? X FORMCHECKBOX FORMCHECKBOX 9 12.1.3 Residual/unmeasured confounding? (e.g. anticipated direction and magnitude of such biases, validation sub-study, use of validation and external data, analytical methods). X FORMCHECKBOX FORMCHECKBOX 9 12.2 Does the protocol discuss study feasibility? (e.g. study size, anticipated exposure uptake, duration of follow-up in a cohort study, patient recruitment, precision of the estimates) X FORMCHECKBOX FORMCHECKBOX 8.7 Comments: Section 13: Ethical/data protection issues Yes No N/A Section Number 13.1 Have requirements of Ethics Committee/ Institutional Review Board been described? X FORMCHECKBOX FORMCHECKBOX Annex III 13.2 Has any outcome of an ethical review procedure been addressed? FORMCHECKBOX FORMCHECKBOX X 13.3 Have data protection requirements been described? X FORMCHECKBOX FORMCHECKBOX Annex II I Comments: Section 14: Amendments and deviations Yes No N/A Section Number 14.1 Does the protocol include a section to document amendments and deviations? X FORMCHECKBOX FORMCHECKBOX 4 Comments: Section 15: Plans for communication of study results Yes No N/A Section Number 15.1 Are plans described for communicating study results (e.g. to regulatory authorities) ? X FORMCHECKBOX FORMCHECKBOX Annex II I 15.2 Are plans described for disseminating study results externally, including publication? X FORMCHECKBOX FORMCHECKBOX Annex II I Comments: ANNEX V I. Glossary Additional definitions are available in the EMA Glossary of terms https://www.ema.europa.eu/en/about-us/glossaries . Aggregated Data Data collected and combined from multiple sources to generate summary information, typically anonymi s ed. Benefit-Risk Assessment Evaluation of the positive therapeutic effects of a medicine compared to its risks (e.g., side effects). Common Data Model (CDM) A standardized data structure that enables data from multiple sources to be harmonized, making analysis consistent and reproducible. DARWIN EU ® utili s es the OMOP CDM maintained by the OHDSI community. Complex Studies (C3) Studies requiring the development or customi s ation of specific study designs, protocols, and Statistical Analysis Plans (SAPs), with extensive collection or extraction of data. Examples include etiological studies measuring the strength and determinants of an association between an exposure and the occurrence of a health outcome in a defined population considering sources of bias, potential confounding factors, and effect modifiers. Coordination Centre (CC) The central hub responsible for managing and overseeing the activities within DARWIN EU®. It is based at Erasmus University Medical Centre in Rotterdam, the Netherlands. Data Access The process of obtaining permission to use specific datasets for regulatory or scientific studies. Data Quality Framework A set of standards and procedures to ensure accuracy, completeness, timeliness, and consistency of data used in DARWIN EU ® . Data Source A database or repository of structured health-related data, such as electronic health records (EHRs), insurance claims, or registries. DARWIN EU® The European Medicines Agency's (EMA) federated network of real-world data sources designed to generate evidence to support regulatory decision-making. EMA (European Medicines Agency) The regulatory body responsible for the evaluation and supervision of medicinal products in the EU, overseeing DARWIN EU ® . Evidence Generation The process of analysing real-world data to produce scientific information that can inform healthcare or regulatory decisions. Federated Network A data infrastructure where data remain at their original location but can be analysed in a harmoni s ed way across multiple partners using a common model and tools. GDPR (General Data Protection Regulation) The EU regulation governing the protection of personal data and privacy, crucial to how DARWIN EU ® handles health data. Health Technology Assessment (HTA) A systematic evaluation of properties and impacts of health technology, often using DARWIN EU ® data to support assessments. Metadata Descriptive information about a data source (e.g., its content, quality, and structure), essential for identifying relevant databases in DARWIN EU ® studies. Off-the-Shelf Studies (OTS) Studies for which a standard protocol per study/analysis type and standardi s ed analytics may be developed and applied or adapted, typically relating to a descriptive research question. This includes studies on disease epidemiology, for example, the estimation of the prevalence or incidence of health outcomes in defined time periods and population groups, or drug utili s ation studies at the population or individual level. OHDSI (Observational Health Data Sciences and Informatics) An open-science collaborative community that develops tools and standards (including the OMOP CDM) to enable large-scale analytics of observational health data. OHDSI provides the technical and scientific foundation for DARWIN EU ® ’s analytical ecosystem. Patient -Level Data Data related to individuals , de-identified, used for longitudinal or detailed analyses. OMOP (Observational Medical Outcomes Partnership) A common data model (CDM) that standardi s es the structure and content of observational healthcare data, enabling systematic analysis across disparate datasets. DARWIN EU ® uses the OMOP CDM to ensure interoperability and consistency in real-world evidence generation. Real-World Data (RWD) Data relating to individual health status or healthcare delivery that is collected from routine clinical practice rather than from randomi s ed controlled trials. Real-World Evidence (RWE) Clinical evidence derived from the analysis of RWD, used to inform decisions by regulators, payers, or clinicians. Regulatory Decision-Making The process by which authorities like EMA assess data to authori s e, monitor, or modify the use of medicines in the EU. Routine Repeated Studies (RR) Studies that are either Off-the-Shelf or Complex studies repeated on a regular basis, following the same protocol and study code, but with updated data and/or different data partners. Study Protocol A detailed plan describing how a specific real-world study will be conducted, including objectives, design, data sources, and analyses. Very Complex Studies (C4) Studies which cannot rely only on electronic health care databases, or which would require complex methodological work, for example, due to the occurrence of events that cannot be defined by existing diagnosis codes, including events that do not yet have a diagnosis code, where it may be necessary to combine a diagnosis code with other data such as results of laboratory investigations. These studies might require the collection of data prospectively, or the inclusion of new (not previously onboarded) data sources. Name of the main author of the protocol: Melissa Leung Date: 1 0 / 10 / 2025 Signature: M. Leung