Mortality and major postoperative complications within one year after vascular surgery: a prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Patients undergoing vascular procedures are prone to developing postoperative complications affecting their short‑term mortality. Prospective reports describing the incidence of long‑term complications after vascular surgery are lacking. OBJECTIVES: We aimed to describe the incidence of complications 1 year after vascular surgery and to evaluate an association between myocardial injury after noncardiac surgery (MINS) and 1‑year mortality. PATIENTS AND METHODS: This is a substudy of a large prospective cohort study Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION). Recruitment took place in 28 centers across 14 countries from August 2007 to November 2013. We enrolled patients aged 45 years or older undergoing vascular surgery, receiving general or regional anesthesia, and hospitalized for at least 1 night postoperatively. Plasma cardiac troponin T concentration was measured before the surgery and on the first, second, and third postoperative day. The patients or their relatives were contacted 1 year after the procedure to assess the incidence of major postoperative complications. RESULTS: We enrolled 2641 patients who underwent vascular surgery, 2534 (95.9%) of whom completed 1‑year follow‑up. Their mean (SD) age was 68.2 (9.8) years, and the cohort was predominantly male (77.5%). The most frequent 1‑year complications were myocardial infarction (224/2534, 8.8%), amputation (187/2534, 7.4%), and congestive heart failure (67/2534, 2.6%). The 1‑year mortality rate was 8.8% (223/2534). MINS occurred in 633 patients (24%) and was associated with an increased 1‑year mortality (hazard ratio, 2.82; 95% CI, 2.14-3.72; P <0.001). CONCLUSIONS: The incidence of major postoperative complications after vascular surgery is high. The occurrence of MINS is associated with a nearly 3‑fold increase in 1‑year mortality.
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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".