Leveraging Decision Curve Analysis to Improve Clinical Application of Surgical Risk Calculators.

ABSTRACT

BACKGROUND: Surgical risk calculators (SRCs) have been developed for estimation of postoperative complications but do not directly inform decision-making. Decision curve analysis (DCA) is a method for evaluating prediction models, measuring their utility in guiding decisions. We aimed to analyze the utility of SRCs to guide both preoperative and postoperative management of patients undergoing hepatopancreaticobiliary surgery by using DCA. METHODS: A single-institution, retrospective review of patients undergoing hepatopancreaticobiliary operations between 2015 and 2017 was performed. Estimation of postoperative complications was conducted using the American College of Surgeons SRC [ACS-SRC] and the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator; risks were compared with observed outcomes. DCA was used to model optimal patient selection for risk prevention strategies and to compare the relative performance of the ACS-SRC and POTTER calculators. RESULTS: A total of 994 patients were included in the analysis. C-statistics for the ACS-SRC prediction of 12 postoperative complications ranged from 0.546 to 0.782. DCA revealed that an ACS-SRC-guided readmission prevention intervention, when compared with an all-or-none approach, yielded a superior net benefit for patients with estimated risk between 5% and 20%. Comparison of SRCs for venous thromboembolism intervention demonstrated superiority of the ACS-SRC for thresholds for intervention between 2% and 4% with the POTTER calculator performing superiorly between 4% and 8% estimated risk. CONCLUSIONS: SRCs can be used not only to predict complication risk but also to guide risk prevention strategies. This methodology should be incorporated into external validations of future risk calculators and can be applied for institution-specific quality improvement initiatives to improve patient outcomes.