Developing and validating a center-specific preoperative prediction calculator for risk of outcomes following major hepatectomy procedures. Issue 8 (August 2018)

• Record Type: Journal Article
• Authors: Fruscione, Mike; Kirks, Russell; Cochran, Allyson; Murphy, Keith; Baker, Erin H.; Martinie, John B.; Iannitti, David A.; Vrochides, Dionisios
• Is Part Of: HPB. Volume 20:Issue 8(2018)
• Page Start: 721
• Page End: 728
• Publication Date: 2018-08
• Subjects: Liver -- Diseases -- Periodicals; Biliary tract -- Diseases -- Periodicals; Pancreas -- Diseases -- Periodicals; 616.362005
• DOI: 10.1016/j.hpb.2018.02.634
• Languages: English
• ISSNs: 1365-182X

Abstract

Abstract: Background: The American College of Surgeons NSQIP ® Surgical Risk Calculator (SRC) was developed to estimate postoperative outcomes. Our goal was to develop and validate an institution-specific risk calculator for patients undergoing major hepatectomy at Carolinas Medical Center (CMC). Methods: Outcomes generated by the SRC were recorded for 139 major hepatectomies performed at CMC (2008–2016). Novel predictive models for seven postoperative outcomes were constructed and probabilities calculated. Brier score and area under the curve (AUC) were employed to assess accuracy. Internal validation was performed using bootstrap logistic regression. Logistic regression models were constructed using bivariate and multivariate analyses. Results: Brier scores showed no significant difference in the predictive ability of the SRC and CMC model. Significant differences in the discriminative ability of the models were identified at the individual level. Both models closely predicted 30-day mortality (SRC AUC: 0.867; CMC AUC: 0.815). The CMC model was a stronger predictor of individual postoperative risk for six of seven outcomes (SRC AUC: 0.531–0.867; CMC AUC: 0.753–0.970). Conclusion: Institution-specific models provide superior outcome predictions of perioperative risk for patients undergoing major hepatectomy. If properly developed and validated, institution-specific models can be used to deliver more accurate, patient-specific care.