Sex Differences in Venous Thromboembolism after COVID-19 Infection: A Retrospective Population-Based Matched Cohort Study
Bibliographic record
Abstract
Study To the Editor:There is a link between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and increased thrombotic risk.In a population-level study from Denmark, the 30-day risks of venous thromboembolism (VTE) after confirmed infections were 0.2% for nonhospitalized patients and 1.5% for hospitalized patients (1).Others have found that pulmonary embolism (PE) is present in 14.2% of patients at hospital admission for coronavirus disease (COVID-19), increasing to 35% in critically ill patients (2, 3).It is established that male patients have a higher risk of adverse health outcomes, including death, after COVID-19 infection (4).Our aims were: 1) to describe sex differences in short-and long-term populationlevel risk of VTE specifically after COVID-19 infection and 2) to assess sex differences in outcomes among those with COVID-19 and VTE. MethodsThis was a retrospective population-level cohort study performed in Alberta, Canada (2021 population, 4,262,635) using secondary administrative data sources.The study was approved by the University of Calgary Health Research Ethics Board (REB20-0688) and is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology statement for observational studies (5).We included all people in Alberta with a positive polymerase chain reaction (PCR) test for COVID-19 (i.e., exposed) between April 1, 2020, and December 15, 2021.For each case, we identified two unexposed control patients with a negative COVID-19 PCR test result and no subsequent positive results in the observation period (Figure 1A).Unexposed patients were matched for age (62 y), sex, and rural versus urban residence; matching was chosen for the latter because there is less access to certain diagnostic tests for VTE (i.e., computed tomography) in rural hospitals in Alberta.The primary outcome was the first VTE event based on International Classification of Diseases, 10th Revision (Canadian modification) codes associated with healthcare visits on or after the index COVID-19 test date for deep vein thrombosis (DVT; codes I80.1-3, 8, 9; I82.8, 9; O22.3, 9; O87.1) or PE (codes I26.0, 9) plus at least one imaging code within 14 days for leg ultrasonography, computed tomography of the chest, ventilation-perfusion scan, or echocardiography.This approach improves the sensitivity and specificity compared with administrative codes alone (6).Secondary outcomes included emergency department (ED) visits, hospitalization, and all-cause mortality after the index COVID-19 test date.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".