Novel Risk Prediction Models For Post-Operative Mortality In Patients With Cirrhosis

Abstract [from journal]

Background & aims: Patients with cirrhosis are at increased risk of post-operative mortality. Currently available tools to predict post-operative risk are suboptimally calibrated and do not account for surgery type. Our objective was to use population-level data to derive and internally validate novel cirrhosis surgical risk models.

Methods: 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 United States 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 post-operative mortality (VOCAL-Penn models). We compared model discrimination and calibration of VOCAL-Penn to the Mayo risk score (MRS), MELD, MELD-Na, and Child-Turcotte-Pugh (CTP) scores.

Results: We identified 4,712 surgical procedures in 3,785 patients with cirrhosis. The novel VOCAL-Penn models were derived and internally validated with excellent discrimination (30-day post-operative mortality C-statistic=0.859, 95% confidence interval [CI] 0.809-0.909). Predictors included age, pre-operative 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 timepoints (e.g. 30-day post-operative mortality C-statistic for MRS=0.766, 95% CI 0.676-0.855) in terms of discrimination and calibration.

Conclusion: The VOCAL-Penn models substantially improve post-operative mortality predictions in patients with cirrhosis. These models may be applied in practice to improve pre-operative risk stratification and optimize patient selection for surgical procedures (