Performance of risk prediction models for post-operative mortality in patients undergoing liver resection
Nadim Mahmud1, Sarjukumar Panchal2, Florence E Turrentine3
1Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA; Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Leonard David Institute of Health Economics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Background:
Liver resection is commonly performed for hepatic tumors, however preoperative risk stratification remains challenging. We evaluated the performance of contemporary prediction models for short-term mortality after liver resection in patients with and without cirrhosis.
Methods:
This retrospective cohort study examined National Surgical Quality Improvement Program data. We included patients who underwent liver resections from 2014 to 2019. VOCAL-Penn, MELD, MELD-Na, ALBI, and Mayo risk scores were evaluated in terms of model discrimination and calibration for 30-day post-operative mortality.
Results:
A total 15,198 patients underwent liver resection, of whom 249 (1.6%) experienced 30-day post-operative mortality. The VOCAL-Penn score had the highest discrimination (area under the ROC curve [AUC] 0.74) compared to all other models. The VOCAL-Penn score similarly outperformed other models in patients with (AUC 0.70) and without (AUC 0.74) cirrhosis.
Conclusion:
The VOCAL-Penn score demonstrated superior predictive performance for 30-day post-operative mortality after liver resection as compared to existing clinical standards.
