Development and Validation of a Risk Quantification Index for 30-Day Postoperative Mortality and Morbidity in Noncardiac Surgical Patients

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.