Thromboprophylaxis patterns and determinants in critically ill patients: a multicenter audit
Bibliographic record
Abstract
INTRODUCTION: Heparin is safe and prevents venous thromboembolism in critical illness. We aimed to determine the guideline concordance for thromboprophylaxis in critically ill patients and its predictors, and to analyze factors associated with the use of low molecular weight heparin (LMWH), as it may be associated with a lower risk of pulmonary embolism and heparin-induced thrombocytopenia without increasing the bleeding risk. METHODS: We performed a retrospective audit in 28 North American intensive care units (ICUs), including all consecutive medical-surgical patients admitted in November 2011. We documented ICU thromboprophylaxis and reasons for omission. Guideline concordance was determined by adding days in which patients without contraindications received thromboprophylaxis to days in which patients with contraindications did not receive it, divided by the total number of patient-days. We used multilevel logistic regression including time-varying, center and patient-level covariates to determine the predictors of guideline concordance and use of LMWH. RESULTS: We enrolled 1,935 patients (62.3 ± 16.7 years, Acute Physiology and Chronic Health Evaluation [APACHE] II score 19.1 ± 8.3). Patients received thromboprophylaxis with unfractionated heparin (UFH) (54.0%) or LMWH (27.6%). Guideline concordance occurred for 95.5% patient-days and was more likely in patients who were sicker (odds ratio (OR) 1.49, 95% confidence interval (CI) 1.17, 1.75 per 10-point increase in APACHE II), heavier (OR 1.32, 95% CI 1.05, 1.65 per 10-m/kg2 increase in body mass index), had cancer (OR 3.22, 95% CI 1.81, 5.72), previous venous thromboembolism (OR 3.94, 95% CI 1.46,10.66), and received mechanical ventilation (OR 1.83, 95% CI 1.32,2.52). Reasons for not receiving thromboprophylaxis were high risk of bleeding (44.5%), current bleeding (16.3%), no reason (12.9%), recent or upcoming invasive procedure (10.2%), nighttime admission or discharge (9.7%), and life-support limitation (6.9%). LMWH was less often administered to sicker patients (OR 0.65, 95% CI 0.48, 0.89 per 10-point increase in APACHE II), surgical patients (OR 0.41, 95% CI 0.24, 0.72), those receiving vasoactive drugs (OR 0.47, 95% CI 0.35, 0.64) or renal replacement therapy (OR 0.10, 95% CI 0.05, 0.23). CONCLUSIONS: Guideline concordance for thromboprophylaxis was high, but LMWH was less commonly used, especially in patients who were sicker, had surgery, or received vasopressors or renal replacement therapy, representing a potential quality improvement target.
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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 | 0.000 | 0.004 |
| 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".