Venous Thromboembolism and Bleeding Risk in a Population with Obesity Hospitalized for Surgery and Receiving Enoxaparin for Thromboprophylaxis
Bibliographic record
Abstract
INTRODUCTION: Obese patients hospitalized for surgery are at high risk of venous thromboembolism (VTE). The optimal dose and duration of thromboprophylaxis with low molecular weight heparin for these patients are uncertain. AIMS: To assess the time-course, rates and risk factors for VTE and major bleeding (MB) in a population of surgical patients with obesity receiving pharmacological thromboprophylaxis with enoxaparin. METHODS: hospitalized with surgeries between 2010 and 2021 who received thromboprophylaxis with enoxaparin were selected from the US Optum database. Exclusion criteria were VTE, MB, or surgery in previous 90-days, and ongoing anticoagulant treatment or dual antiplatelet therapy. VTE and MB event rates over a 90-day follow-up post enoxaparin initiation were estimated via the Kaplan-Meier (KM) method. Risk factors associated with outcome events were identified via Cox proportional hazard models. RESULTS: A total of 30,492 patients met selection criteria, 12,058 patients received the standard dose, with 18,300 receiving higher doses. KM event rates at 90-days for VTE and MB were 2.5% and 1.2%, respectively. The highest VTE rates were observed in patients hospitalized for thoracic surgery (4.9%). History of VTE was the strongest predictor of post-surgery VTE (HR 5.62, 95% CI 4.71-6.7) while history of MB was the strongest predictor of post-surgery bleeding (HR 2.62, 95% CI 1.29-5.32). CONCLUSIONS: The rates of VTE are non-negligible in surgical patients with obesity receiving thromboprophylaxis with enoxaparin. Individual risk stratification is warranted to identify optimal doses/duration of pharmacologic thromboprophylaxis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".