Institutional case volume-incorporated mortality risk prediction model for cardiac surgery

초록

Background: Most risk prediction models predicting short-term mortality after cardiac surgery incorporate patient characteristics, laboratory data, and type of surgery, but do not account for surgical experience. Considering the impact of case volume on patient outcome after high-risk procedures, we attempted to develop a risk prediction model for mortality after cardiac surgery that incorporates institutional case volume. Methods: Adult patients who underwent cardiac surgery from 2009 to 2016 were identified. Patients who underwent cardiac surgery (n = 57,804) were randomly divided into the derivation cohort (n = 28,902) or the validation cohorts (n = 28,902). A risk prediction model for in-hospital mortality and 1-year mortality was developed from the derivation cohort and the performance of the model was evaluated in the validation cohort. Results: The model demonstrated fair discrimination (c-statistics, 0.76 for in-hospital mortality in both cohorts; 0.74 for 1-year mortality in both cohorts) and acceptable calibration. Hospitals were classified based on case volume into 50 or less, 50-100, 100-200, or more than 200 average cardiac surgery cases per year and case volume was a significant variable in the prediction model. Conclusions: A new risk prediction model that incorporates institutional case volume and accurately predicts in-hospital and 1-year mortality after cardiac surgery was developed and validated. (c) 2021 Asian Surgical Association and Taiwan Robotic Surgery Association. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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