Rationale
Cancer-associated venous thrombosis is relatively common: from 20% to 30% of all primary venous thromboembolic events are cancer-associated. Cancer patients have an increased risk of developing venous thromboembolism (VTE) compared to individuals without underlying malignancies, and it is also recognised as one of the major causes of death in cancer patients. The reported incidence varies across different populations and cancer types and can also be attributed to variations in patient characteristics, clinical management options, and the cancer stage at diagnosis. The incidence of VTE was found to be higher in cases of renal cell, ovarian, pancreatic, stomach, and lung cancers, as well as acute myelogenous leukaemia and non-Hodgkin lymphoma during the four months immediately preceding the cancer diagnosis. When investigating a potential safety signal associated with oncological treatments, the availability of recent and reliable information on the background risk of VTE in cancer patients is crucial.
Research Objectives
This study aims to estimate incidence rates of venous thromboembolic events (deep vein thrombosis (DVT), pulmonary embolisms (PE), venous thromboembolism (VTE, composite of DVT and PE), pelvic venous thrombosis (PVT), 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) in adult patients (aged 18 and above) newly diagnosed with selected cancers ((bone, brain, breast, colorectal, corpus uteri, kidney, leukaemia and lymphoma, liver, lung, melanoma, oesophageal, ovary, pancreas, prostate, stomach) in 2016-2022 and to describe their characteristics at the time of cancer diagnosis.
The specific objectives of the study are:
1. To estimate the incidence rates of thromboembolic events in patients newly diagnosed with each type of selected cancers stratified by country/database, age group, sex, study period (2016-2019 and 2020-2022), and cancer stage one and two years after cancer diagnosis.
1. To characterise cancer patients in terms of demographics, comorbidities and concomitant medication before and at the time of diagnosis, as well as medications and procedures received in the first 90 days after cancer diagnosis.
MODEL DE SOL·LICITUD
2 IMP-126-CT Versió 07
Research Methods
Study design
Population-based cohort study.
Population
The study population will include all individuals aged 18 years and above with a primary diagnosis of one of the selected cancers (bone, brain, breast, colorectal, corpus uteri, kidney, leukaemia and lymphoma, liver, lung, melanoma, oesophageal, ovary, pancreas, prostate, stomach). The study period will be from 01/01/2016 to 31/12/2022, which is at least one year prior to the last date of data availability in all databases to ensure sufficient time to capture potential outcomes of interest. Only patients with the first and one cancer diagnosis (except non-melanoma skin cancer) will be included. Cancer cases and thromboembolic events will be identified based on appropriate computable phenotyping algorithms. Conditions in the OMOP CDM use the Systematised 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.
Variables
Outcomes will include thromboembolic events, in particular, DVT, PE, VTE (composite of DVT and PE), PVT, SVT, RVT, DIC. The outcomes will be calculated in patients with selected cancer types and stratified by the American Joint Committee on Cancer and the International Union Against AJCC/UICC stage categories, age (18-34, 35-44, 45-54, 55-64, 65-74, 75-84 and 85 and above), sex and study year.
Data sources
1. Clinical Practice Research Datalink GOLD (CPRD GOLD), United Kingdom
2. Danish Data Health Registries (DK-DHR), Denmark
3. Estonian Biobank (EBB), Estonia
4. Finnish Care Register for Health Care (FinOMOP-HILMO) Finland
5. Hospital District of Helsinki and Uusimaa (FinOMOP-HUS), Finland
6. IQVIA Disease Analyzer Germany (IQVIA DA Germany), Germany
7. IQVIA Longitudinal Patient Database Belgium (IQVIA LPD Belgium), Belgium
8. Integrated Primary Care Information (IPCI), Netherlands
9. The Information System for Research in Primary Care (SIDIAP), Spain.
10. UK Biobank (UKBB), United Kingdom
Sample size
No sample size will be calculated as this is a descriptive study.
Data analyses
Analyses will be conducted separately for each database and carried out in a federated manner, allowing analyses to be run locally without sharing patient-level data.
The incidence of thromboembolic events (Objective 1) will be estimated over one and two years after the selected cancer diagnosis. Each cancer type and outcome will be assessed separately. Large-scale patient-level characterisation (Objective 2) will be conducted at the index date. Age and sex will be described at the time of diagnosis. The medical history and medication will be assessed at the index date.
For all analyses, absolute and relative frequencies will be reported. A minimum cell count of 5 will be used when reporting results, with any smaller counts reported as “<5” and zero counts as “0”. Overall analyses will be done separately for each database. Stratification by age category, sex, study year, cancer stage will be conducted when possible (minimum cell count reached and data available).