Outcomes Following Perioperative Stroke in Cardiac Surgery Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To identify independent predictors of 30-day mortality, adverse discharge, and length of hospital stay following a perioperative stroke among cardiac surgical patients, and to measure trends in outcomes over time. DESIGN: A retrospective cohort study. SETTING: American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database from 2005 to 2020. PARTICIPANTS: Cardiac surgery patients with perioperative stroke (n = 906). INTERVENTIONS: None. Observational analysis. MEASUREMENTS AND MAIN RESULTS: Patient demographics, comorbid conditions, timing of stroke, procedure characteristics, and type of anesthesia information were extracted. The least absolute shrinkage and selection operator technique were employed to identify variables associated with 30-day mortality (the primary outcome), adverse discharge (death or a nonhome facility), and length of hospital stay. Perioperative stroke occurred a median (interquartile range) of 4 days (2-8 days) after surgery, 15% (134/906) of patients died, and 52% (351/906) were discharged to a facility that was not home. Factors significantly associated with 30-day mortality included increasing age, postoperative complications, fewer days from operation to stroke, and increased operative time (C-statistic = 0.794). Significant temporal changes in mortality or adverse discharge outcomes were not identified over the 15-year study period. CONCLUSION: Cardiac perioperative strokes are associated with high rates of nonhome discharge and mortality, and those occurring sooner after cardiac surgery were associated with higher mortality, in addition to other factors. Outcomes did not change significantly over the 15-year study period. Further research should focus on effective interventions to improve outcomes after stroke following cardiac surgery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".