Evaluation of Bleeding Risk in Patients Exposed to Therapeutic Unfractionated or Low-Molecular Weight Heparin: A Cohort Study in the Context of a Quality Improvement Initiative
Bibliographic record
Abstract
BACKGROUND: Bleeding associated with the use of unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH) can be a serious complication of health-care management and should be the focus of quality improvement initiatives by institutions. OBJECTIVE: To measure the incidence of bleeding with UFH and LMWH and evaluate associated risk factors. METHODS: An observational cohort study was conducted at a secondary and tertiary care hospital in Canada. All adults receiving therapeutic doses of UFH or LMWH between April 2006 and March 2007, with the exception of cardiac surgery patients, were included. Bleeding episodes were classified per the GUSTO scale. RESULTS: Of 3066 hospitalizations, the incidence of moderate or severe bleeding was 3.5%. Advanced age (OR 1.02, 95% CI 1.01 to 1.04; p < 0.001), female sex (OR 1.80, 95% CI 1.21 to 2.66; p = 0.003), UFH instead of LMWH (OR 4.72, 95% CI 2.17 to 10.30; p < 0.001), creatinine clearance (CrCl) (OR 0.89, 95% CI 0.84 to 0.95; p < 0.001, for a difference of 10 mL/min in CrCl), and supratherapeutic activated partial thromboplastin time (aPTT) (OR 3.88, 95% CI 2.25 to 6.69; p < 0.001 for >180 vs <90 seconds) were associated with a higher risk of bleeding in univariate analysis. In a multivariate model without aPTT, CrCl (OR 0.90, 95% CI 0.85 to 0.96; p < 0.001, for a difference of 10 mL/min in CrCl) and UFH (OR 2.35, 95% CI 1.11 to 4.98; p = 0.005) were significant predictors of bleeding. Among the bleeding episodes, 31% were in a postoperative context and 15% were following a puncture. CONCLUSIONS: Our findings show that CrCl and aPTT values, as well as the type of heparin used, are significant predictors of bleeding in patients receiving UFH or LMWH and that dosages should be adjusted to patient weight. The reason for all supratherapeutic aPTT levels should be sought and corrective measures taken immediately.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".