Improved Re-estimation of Perioperative Cardiac Risk Using the Surgical Apgar Score: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess whether the Surgical Apgar Score (SAS) improves re-estimation of perioperative cardiac risk. BACKGROUND: The SAS is a novel risk index that integrates three relevant and easily measurable intraoperative parameters (blood loss, heart rate, mean arterial pressure) to predict outcomes. The incremental prognostic value of the SAS when used in combination with standard preoperative risk indices is unclear. METHODS: We conducted a retrospective cohort study of adults (18 years and older) who underwent elective noncardiac surgery at a quaternary care hospital in Canada (2009-2014). The primary outcome was postoperative acute myocardial injury. The SAS (range 0-10) was calculated based on intraoperative estimated blood loss, lowest mean arterial pressure, and lowest heart rate documented in electronic medical records. Incremental prognostic value of the SAS when combined with the Revised Cardiac Risk Index was assessed based on discrimination (c-statistic), reclassification (integrated discrimination improvement, net reclassification index), and clinical utility (decision curve analysis). RESULTS: The cohort included 16,835 patients, of whom 607 (3.6%) patients had acute postoperative myocardial injury. Addition of the SAS to the Revised Cardiac Risk Index improved risk estimation based on the integrated discrimination improvement [2.0%; 95% confidence interval (CI): 1.5%-2.4%], continuous net reclassification index (54%; 95% CI: 46%-62%), and c-index, which increased from 0.68 (95% CI: 0.65-0.70) to 0.75 (95% CI: 0.73-0.77). On decision curve analysis, addition of the SAS to the Revised Cardiac Risk Index resulted in a higher net benefit at all decision thresholds. CONCLUSIONS: When combined with a validated preoperative risk index, the SAS improved the accuracy of cardiac risk assessment for noncardiac surgery. Further research is needed to delineate how intraoperative data can better guide postoperative decision-making.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".