Risk Prediction Models for Post-Operative Mortality in Patients With Cirrhosis

Background and Aims Patients with cirrhosis are at increased risk of postoperative mortality. Currently available tools to predict postoperative risk are suboptimally calibrated and do not account for surgery type. Our objective was to use population-level data to derive and internally validate cirrhosis surgical risk models. Approach and Results We conducted a retrospective cohort study using data from the Veterans Outcomes and Costs Associated with Liver Disease (VOCAL) cohort, which contains granular data on patients with cirrhosis from 128 U.S. medical centers, merged with the Veterans Affairs Surgical Quality Improvement Program (VASQIP) to identify surgical procedures. We categorized surgeries as abdominal wall, vascular, abdominal, cardiac, chest, or orthopedic and used multivariable logistic regression to model 30-, 90-, and 180-day postoperative mortality (VOCAL-Penn models). We compared model discrimination and calibration of VOCAL-Penn to the Mayo Risk Score (MRS), Model for End-Stage Liver Disease (MELD), Model for End-Stage Liver Disease-Sodium MELD-Na, and Child-Turcotte-Pugh (CTP) scores. We identified 4,712 surgical procedures in 3,785 patients with cirrhosis. The VOCAL-Penn models were derived and internally validated with excellent discrimination (30-day postoperative mortality C-statistic = 0.859; 95% confidence interval [CI], 0.809-0.909). Predictors included age, preoperative albumin, platelet count, bilirubin, surgery category, emergency indication, fatty liver disease, American Society of Anesthesiologists classification, and obesity. Model performance was superior to MELD, MELD-Na, CTP, and MRS at all time points (e.g., 30-day postoperative mortality C-statistic for MRS = 0.766; 95% CI, 0.676-0.855) in terms of discrimination and calibration. Conclusions The VOCAL-Penn models substantially improve postoperative mortality predictions in patients with cirrhosis. These models may be applied in practice to improve preoperative risk stratification and optimize patient selection for surgical procedures ().