Anticoagulation for patients discharged from the emergency department with venous thromboembolism
Bibliographic record
Abstract
OBJECTIVE: Direct oral anticoagulants (DOACs) are increasingly being used over low molecular weight heparin (LMWH) and vitamin K antagonists for the treatment of venous thromboembolism (VTE). The objective of this study was to examine predictors of anticoagulant type (DOAC vs. LMWH) prescribed at discharge from the emergency department (ED) among patients diagnosed with VTE in the ED. METHODS: We conducted a retrospective chart review of adult (>17 years) patients discharged from an Ontario, Canada ED in a tertiary care centre with an ED diagnosis of deep vein thrombosis or pulmonary embolism from January 2019 to December 2021. A multivariable logistic regression model was used to examine the predictors of the anticoagulant (DOAC vs. LMWH) prescribed at discharge. Covariables included: age, sex, history of major bleeding, history of cancer, and previous anticoagulation. RESULTS: VTE was confirmed in 390 ED visits by 365 unique patients. Among unique patients, 239 (65.5 %) patients were discharged from the ED and included in analysis. Of the 239 patients included, 12.1 % of patients were over the age of 80, 46.4 % were female and 29.7 % had a history of cancer. The majority of patients discharged from the ED were prescribed DOACs (70.7 %,169/239). Cancer history was associated with anticoagulation with LMWH (vs. DOAC) on discharge (adjusted odds ratio [aOR] =12.81, 95 % CI: 6.60-25.90). CONCLUSIONS: While most patients diagnosed with VTE in the ED setting were discharged with DOACs, most cancer patients included in our study were treated with LMWH over DOACs, despite increasing evidence around the efficacy and safety of DOACs in most cancer patients. Further research is needed to understand longitudinal trends in anticoagulation.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".