Trends in Perioperative Venous Thromboembolism Associated with Major Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a common vascular complication of non-cardiac surgery. METHODS: We evaluated national trends in perioperative in-hospital VTE incidence, management, and outcomes using a large database of hospital admissions from the United States. Patients aged ≥ 45 years undergoing major non-cardiac surgery from 2005 to 2013 were identified from the National Inpatient Sample. In-hospital perioperative VTE was defined as lower extremity deep vein thrombosis (DVT) or pulmonary embolism (PE), and the incidence was evaluated over time. Multivariable regression models with demographics and comorbidities as covariates were generated to estimate adjusted odds ratios (aOR). RESULTS: Major non-cardiac surgery was performed in 9,431,442 hospitalizations that met inclusion criteria, and perioperative VTE occurred in 99,776 patients (1,057 per 100,000), corresponding to an annual incidence of ≈53,000 after applying sample weights. Over time, perioperative VTE per 100,000 surgeries increased by 135 (95% CI 107 - 163), from 925 in 2005 to 1,060 in 2013 (p for trend <0.001; aOR [for 2013 versus 2005] 1.22, 95% CI 1.19 - 1.26), due to increases in non-fatal VTE rates (from 840 [per 100,000 surgeries] in 2005 to 987 in 2013; p for trend <0.001). Perioperative VTE occurred most frequently in patients undergoing thoracic (2.0%) and vascular surgery (1.8%). Mortality was higher in patients with VTE than those without VTE (aOR 3.12, 95% CI 3.05 - 3.20). CONCLUSIONS: Perioperative VTE occurs in approximately 1% of patients ≥45 years undergoing major non-cardiac surgery, with increasing incidence of non-fatal VTE over time.
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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 | 0.001 | 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".