Frequency of Venous Thromboembolism in 6513 Patients with COVID-19: A Retrospective Study
Bibliographic record
Abstract
Background: Patients infected with coronavirus 2 (SARS-CoV-2) appear to be at increased risk for venous thromboembolism (VTE), especially if they become critically ill with coronavirus disease 2019 (COVID-19). Some centers have reported very high rates of thrombosis despite anticoagulant prophylaxis. Methods: The electronic health record (EHR) of a New Orleans-based health system was searched for all patients with PCR-confirmed SARS-CoV-2 infection who were either admitted to hospital or treated and discharged from an emergency department between March 1 and May 1, 2020. From this cohort, patients with confirmed VTE (either during or after their hospital encounter) were identified by administrative query of the EHR. Results: Between March 1, 2020 and May 1, 2020, 6,153 patients with COVID-19 were identified; 2,748 of these patients were admitted, while 3,405 received care exclusively through the emergency department. Data on patient outcomes were determined up until and including May 21, 2020. In total, 637 patients required mechanical ventilation and 206 required renal replacement therapy. Within the hospitalized cohort, the overall mortality rate was 24.5% and VTE occurred in 86 patients (3.1%). In the 637 patients who required mechanical ventilation at some point during their hospital stay, 45 developed VTE (7.2%). After a median follow-up of 14.6 days, VTE had been diagnosed in 3 of the 2,075 admitted who were discharged alive (0.14%). Conclusions: Among 6,153 patients with COVID-19 who were hospitalized or treated in emergency departments, we did not find evidence of unusually high VTE risk. Pending further evidence from prospective, controlled trials, our findings support a traditional approach to primary VTE prevention in patients with COVID-19. Disclosures Crowther: Precision Biologicals: Membership on an entity's Board of Directors or advisory committees; Hemostais Reference Laboratories: Honoraria; Pfizer: Speakers Bureau; CSL Behring: Speakers Bureau; Alnylam: Divested equity in a private or publicly-traded company in the past 24 months; Servier Canada: Membership on an entity's Board of Directors or advisory committees; Diagnostica Stago: Speakers Bureau; Asahi Kasei: Membership on an entity's Board of Directors or advisory committees.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".