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Center-level outcome metrics have long been tracked in elective surgery (ELS). Despite recent interest in measuring emergency general surgery (EGS) quality, centers are often compared based on elective or combined outcomes. Therefore, quality of care for emergency surgery specifically is unknown. We extracted data on EGS and ELS patients from the 2016 State Inpatient Databases of Florida, New York, and Kentucky. Centers that performed >100 ELS and EGS operations were included. Risk-adjusted mortality, complication, and failure to rescue (FTR, death after complication) rates were calculated and observed-to-expected ratios were calculated by center for ELS and EGS patients. Centers were determined to be high or low outliers if the 90% CI for the observed: expected ratio excluded 1. We calculated the frequency with which centers demonstrated a different performance status between EGS and ELS. Kendall's tau values were calculated to assess for correlation between EGS and ELS status. A total of 204 centers with 45,500 EGS cases and 49,380 ELS cases met inclusion criteria. Overall mortality, complication, and FTR rates were 1.7%, 8.0%, and 14.5% respectively. There was no significant correlation between mortality performance in EGS and ELS, with 36 centers in a different performance category (high outlier, low outlier, as expected) in EGS than in ELS. The correlation for complication rates was 0.20, with 60 centers in different categories for EGS and ELS. For FTR rates, there was no correlation, with 16 centers changing category. There was minimal correlation between outcomes for ELS and EGS. High performers in one category were rarely high performers in the other. There may be important differences between the processes of care that are important for EGS and ELS outcomes that may yield meaningful opportunities for quality improvement. [ABSTRACT FROM AUTHOR]

Background: Emergency general surgery (EGS) lacks mechanisms to compare performance between institutions. Focusing on higher-risk procedures may efficiently identify outliers., Methods: EGS patients were identified from the 2016 State Inpatient Databases of Florida, New York, and Kentucky. Risk-adjusted mortality was calculated as an O:E ratio, generating expected mortality from a model including demographic and procedural factors. Outliers were centers whose 90% confidence intervals excluded 1. This was repeated in several subsets, to determine if these yielded outliers similar to the overall dataset., Results: We identified 45,430 EGS patients. Overall, 3 high performing centers and 5 low performing centers were identified. Exclusion of appendectomies and cholecystectomies resulted in a remaining data set of 13,569 patients (29.9% of the overall data set), with 2 high performers and 5 low performers. One low performer in the limited data set was not identified in the overall set., Conclusion: Evaluation of 5 procedures, making up less than a third of EGS, identifies most outliers. A streamlined monitoring procedure may facilitate maintenance of an EGS registry., Competing Interests: Declaration of competing interest No authors have conflicts to declare., (Copyright © 2021 Elsevier Inc. All rights reserved.)