Factors associated with adverse outcomes following perioperative stroke after noncardiac surgery
Bibliographic record
Abstract
BACKGROUND: Perioperative stroke is associated with high rates of adverse outcomes. Our objective was to identify factors associated with 30-day mortality, adverse discharge, and length of hospital stay following perioperative stroke among noncardiac surgical patients, and to analyze trends in these outcomes from 2005 to 2020. STUDY DESIGN: A retrospective cohort study of noncardiac perioperative stroke patients was conducted using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (2005-2020). Data included patient demographics, comorbidities, stroke, and surgical variables. Elastic net penalization identified variables associated with 30-day mortality (primary outcome), adverse discharge (death or non-home facility) and length of hospital stay in multivariable models. RESULTS: We identified 14,386 patients with perioperative stroke. Strokes occurred a median [interquartile range] of 5 days [2 days to 12 days] after surgery, 24.6 % (N = 3,540) of patients died, and 39.8 % (N = 4,773) were discharged to a non-home facility. Factors significantly associated with 30-day mortality included age, body mass index, postoperative complications, stroke closer to surgery and perioperative blood transfusion (c-statistic = 0.749, 95 % CI 0.739 to 0.758). We did not identify significant changes in mortality and adverse discharge over the study period. CONCLUSION: Several factors were significantly associated with increased risk of poor outcome following perioperative stroke, including potentially modifiable factors such as perioperative anemia, and transfusion. Further research is warranted to identify mechanisms and possible interventions to improve outcome in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".