Routine versus difficult cholecystectomy: using predictive analytics to assess patient outcomes. Issue 1 (January 2019) - Fruscione, Mike; Kirks, Russell C.; Cochran, Allyson; Murphy, Keith; Baker, Erin H.; Martinie, John B.; Iannitti, David A.; Vrochides, Dionisios
Abstract: Background: The American College of Surgeons National Surgical Quality Improvement Program ® (NSQIP) Surgical Risk. Calculator (SRC) estimates postoperative outcomes. The aim of this study was to develop and validate a specific predictive outcomes model for cholecystectomy procedures. Methods: Patients who underwent cholecystectomy between 2008 and 2016 and were deemed too high risk for acute care general surgery (GS) and had surgery performed by the Division of Hepatopancreatobiliary Surgery (HPB) were identified. Outcomes of the HPB cholecystectomies were matched against cholecystectomies performed by GS. New predictive models for postoperative outcomes were constructed. Area under the curve was used to assess predictive accuracy for both models and internal validation was performed using bootstrap logistic regression. Results: A total of 169/934 (18%) cholecystectomies were identified as too high risk for GS. These 169 patients were matched with 126 patients who had cholecystectomy performed by GS. For GS and HPB cholecystectomies, the proposed model demonstrated better discriminative ability compared to the SRC based on ROC curves (proposed model: 0.589–0.982; SRC: 0.570–0.836) for each of the predicted outcomes. Conclusion: For patients undergoing cholecystectomy, customized models are superior for predicting individual perioperative risk and allow more accurate, patient-specific delivery of care.