Relationship Between Perioperative Outcomes Used for Profiling Hospital Noncardiac Surgical Quality.

BACKGROUND Risk-adjusted morbidity and mortality are commonly used by national surgical quality improvement (QI) programs to measure hospital-level surgical quality. However, the degree of hospital-level correlation between mortality, morbidity, and other perioperative outcomes (like reoperation) collected by contemporary surgical QI programs has not been well-characterized. MATERIALS AND METHODS Veterans Affairs (VA) Surgical Quality Improvement Program (VASQIP) data (2015-2016) were used to evaluate hospital-level correlation in performance between risk-adjusted 30-d mortality, morbidity, major morbidity, reoperation, and 2 composite outcomes (1- mortality, major morbidity, or reoperation; 2- mortality or major morbidity) after noncardiac surgery. Correlation between outcomes rates was evaluated using Pearson's correlation coefficient. Correlation between hospital risk-adjusted performance rankings was evaluated using Spearman's correlation. RESULTS Based on a median of 232 [IQR 95-331] quarterly surgical cases abstracted by VASQIP, statistical power for identifying 30-d mortality outlier hospitals was estimated between 3.3% for an observed-to-expected ratio of 1.1 and 45.7% for 3.0. Among 230,247 Veterans who underwent a noncardiac operation at 137 VA hospitals, there were moderate hospital-level correlations between various risk-adjusted outcome rates (highest r = 0.40, mortality and composite 1; lowest r = 0.32, mortality and morbidity). When hospitals were ranked based on performance, there was low-to-moderate correlation between rankings on the various outcomes (highest ρ = 0.47, mortality and composite 1; lowest ρ = 0.37, mortality and major morbidity). CONCLUSIONS Modest hospital-level correlations between perioperative outcomes suggests it may be difficult to identify high (or low) performing hospitals using a single measure. Additionally, while composites of currently measured outcomes may be an efficient way to improve analytic sample size (relative to evaluations based on any individual outcome), further work is needed to understand whether they provide a more robust and accurate picture of hospital quality or whether evaluating performance across a portfolio of individual measures is most effective for driving QI.