Influence of Perceived Risk Factors for Bleeding on Venous Thromboembolism Prophylaxis Practices in Acutely Ill Medical Patients: Findings from IMPROVE.
Bibliographic record
Abstract
Abstract Background Acutely ill medical patients at risk for venous thromboembolism (VTE) should receive VTE prophylaxis. However, factors perceived by physicians to increase patients’ risk of bleeding may influence VTE prophylaxis practices. In this analysis from The International Medical Prevention Registry on Venous Thromboembolism (IMPROVE), we examined whether perceived risk factors for bleeding had a significant influence on physicians’ prescribing of in-hospital prophylaxis in acutely ill medical patients. Methods Patients aged ≥18 years and hospitalized ≥3 days with an acute medical illness have been enrolled consecutively since July 2002. Risk factors considered were: severe renal failure, known bleeding disorder, hemorrhagic stroke, thrombocytopenia, bacterial endocarditis, active gastroduodenal ulcer, NSAID use, hepatic failure, age, immobility and alcoholism. Factors associated with different prescription rates of prophylaxis compared with rates in patients without these factors were identified and included in a multiple logistic regression model (significance at p<0.05). Results Up to 31 March 2005, 6946 patients were enrolled in 49 hospitals in 12 countries. Pharmacologic prophylaxis was received by 42%, 25%, 16%, and 14% of patients with a platelet count at admission >100, 50–100, 20–50 and <20x109/L, respectively (p<0.0001), and 43%, 39%, 30% and 32% of patients with none, 1, 2 and 3 risk factors for bleeding (p<0.0001). Factors independently associated with a lower/higher prescription rate of heparin-based prophylaxis compared with the risk in patients without these factors are shown in the Table. Conclusions The likelihood that hospitalized acutely ill medical patients receive in-hospital pharmacologic prophylaxis decreases as their platelet count at admission decreases, or their cumulative number of perceived risk factors for bleeding increases. Further studies are needed to determine whether the changes in prophylaxis practices observed in this study are justified. Table. Factors Independently Associated with a Higher/lower Rate of Heparin-based VTE Prophylaxis Factor Odds Ratio 95% Confidence Interval Age (per 10-year increase) 1.19 1.66–1.22 Immobility (per 10-day increase) 1.03 1.02–1.05 Alcoholism 0.62 0.46–0.83 Thrombocytopenia 0.60 0.48–0.74 Active duodenal ulcer 0.36 0.26–0.52 Hepatic failure 0.34 0.21–0.54
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".