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Performance of risk prediction models for post-operative mortality in patients undergoing liver resection
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Performance of risk prediction models for post-operative mortality in patients undergoing liver resection
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Performance of risk prediction models for post-operative mortality in patients undergoing liver resection
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Journal Article
Performance of risk prediction models for post-operative mortality in patients undergoing liver resection
2023
Overview
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.
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.
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.
The VOCAL-Penn score demonstrated superior predictive performance for 30-day post-operative mortality after liver resection as compared to existing clinical standards.
•Preoperative risk stratification for liver resection remains a clinical challenge.•In a dataset of patients undergoing liver resection, prediction scores were compared for 30-day postoperative mortality.•The VOCAL-Penn score had superior discrimination and adequate calibration versus MELD, MELD-Na, ALBI, and Mayo risk scores.
