Deep venous thrombosis in medical-surgical critically ill patients: Prevalence, incidence, and risk factors
Bibliographic record
Abstract
OBJECTIVE: Critically ill patients may be at high risk of venous thromboembolism. The objective was to determine the prevalence, incidence, and risk factors for proximal lower extremity deep venous thrombosis among critically ill medical-surgical patients. DESIGN: Prospective cohort. SETTING: Closed university-affiliated intensive care unit. PATIENTS: We enrolled consecutive patients > or =18 yrs of age expected to be in intensive care unit for > or =72 hrs. Exclusion criteria were an admitting diagnosis of trauma, orthopedic surgery, pregnancy, and life support withdrawal. INTERVENTIONS: Interventions included bilateral lower extremity compression ultrasound within 48 hrs of intensive care unit admission, twice weekly, and if venous thromboembolism was clinically suspected. Thromboprophylaxis was protocol directed and universal. We recorded deep venous thrombosis risk factors at baseline and daily, using multivariate regression analysis to determine independent predictors. Patients were followed to hospital discharge. RESULTS: Among 261 patients with a mean Acute Physiology and Chronic Health Evaluation II score of 25.5 (+/-8.4), the prevalence of deep venous thrombosis was 2.7% (95% confidence interval 1.1-5.5) on intensive care unit admission, and the incidence was 9.6% (95% confidence interval 6.3-13.8) over the intensive care unit stay. We identified four independent risk factors for intensive care unit-acquired deep venous thrombosis: personal or family history of venous thromboembolism (hazard ratio 4.0, 95% confidence interval 1.5-10.3), end-stage renal failure (hazard ratio 3.7, 95% confidence interval 1.2-11.1), platelet transfusion (hazard ratio 3.2, 95% confidence interval 1.2-8.4), and vasopressor use (hazard ratio 2.8, 95% confidence interval 1.1-7.2). Patients with deep venous thrombosis had a longer duration of mechanical ventilation (p = .03), intensive care unit stay (p = .005), and hospitalization (p < .001) than patients without deep venous thrombosis. CONCLUSIONS: Despite universal thromboprophylaxis, medical-surgical critically ill patients remain at risk for lower extremity deep venous thrombosis. Further research is needed to evaluate the risks and benefits of more intense venous thromboembolism prophylaxis.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".