Comparing Preoperative Targets to Failure-to-Rescue for Surgical Mortality Improvement
Bibliographic record
Abstract
BACKGROUND: Failure-to-rescue (FTR or death after postoperative complication) is thought to explain surgical mortality excesses across hospitals, and FTR is an emerging performance measure and target for quality improvement. We compared the FTR population to preoperatively identifiable subpopulations for their potential to close the mortality gap between lowest- and highest-mortality hospitals. STUDY DESIGN: Patients undergoing small bowel resection, pancreatectomy, colorectal resection, open abdominal aortic aneurysm repair, lower extremity arterial bypass, and nephrectomy were identified in the 2007 to 2011 Nationwide Inpatient Sample. Lowest- and highest-mortality hospitals were defined using risk- and reliability-adjusted mortality quintiles. Five target subpopulations were established a priori: the FTR population, predicted high-mortality risk (predicted highest-risk quintile), emergency surgery, elderly (>75 years old), and diabetic patients. RESULTS: Across the lowest mortality quintile (n=282 hospitals, 56,893 patients) and highest-mortality quintile (282 hospitals, 45,784 patients), respectively, the size of target subpopulations varied only for the FTR population (20.2% vs 22.4%, p=0.002) but not for other subpopulations. Variation in mortality rates across lowest- and highest-mortality hospitals was greatest for the high-mortality risk (7.5% vs 20.2%, p<0.0001) and FTR subpopulations (7.8% vs 18.9%, p<0.0001). The FTR and high-risk populations had comparable sensitivity (81% and 75%) and positive predictive value (19% and 20%, respectively) for mortality. In Monte Carlo simulations, the mortality gap between the lowest- and highest-mortality hospitals was reduced by nearly 75% when targeting the FTR population or the high-risk population, 78% for the emergency surgery population, but less for elderly (51%) and diabetic (17%) populations. CONCLUSIONS: Preoperatively identifiable patients with high estimated mortality risk may be preferable to the FTR population as a target for surgical mortality reduction.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".