Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients
Bibliographic record
Abstract
BACKGROUND: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for high-risk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient's primary procedure (American Medical Association), American Society of Anesthesiologists Physical Status, and age (for mortality) or hospitalization (inpatient vs. outpatient, for morbidity). METHODS: Data from 635,265 noncardiac surgical patients participating in the American College of Surgeons National Surgical Quality Improvement Program between 2005 and 2008 were analyzed. The authors developed a novel algorithm to aggregate sparsely represented Current Procedural Terminology codes into logical groups and estimated univariable Procedural Severity Scores-one for mortality and morbidity, respectively-for each aggregated group. These scores were then used as predictors in developing respective risk quantification models. Models were validated with c-statistics, and calibration was assessed using observed-to-expected ratios of event frequencies for clinically relevant strata of risk. RESULTS: The risk quantification models demonstrated excellent predictive accuracy for 30-day postoperative mortality (c-statistic [95% CI] 0.915 [0.906-0.924]) and morbidity (0.867 [0.858-0.876]). Even in high-risk patients, observed rates calibrated well with estimated probabilities for mortality (observed-to-expected ratio: 0.93 [0.81-1.06]) and morbidity (0.99 [0.93-1.05]). CONCLUSION: The authors developed simple risk-adjustment models, each based on three easily obtained variables, that allow for objective quality-of-care monitoring among hospitals.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".