A Web-Based Prediction Model for Estimating the Probability of Post-hepatectomy Major Complications in Patients with Hepatocellular Carcinoma: A Multicenter Study from a Hepatitis B Virus-Endemic Area.

ABSTRACT

BACKGROUND: The identification of patients at high risk of developing postoperative complications is important to improve surgical safety. We sought to develop an individualized tool to predict post-hepatectomy major complications in hepatitis B virus (HBV)-infected patients with hepatocellular carcinoma (HCC). METHODS: A multicenter database of patients undergoing hepatectomy for HCC were analyzed; 2/3 and 1/3 of patients were assigned to the training and validation cohorts, respectively. Independent risks of postoperative 30-day major complications (Clavien-Dindo grades III-V) were identified and used to construct a web-based prediction model, which predictive accuracy was assessed using C-index and calibration curves, which was further validated by the validation cohort and compared with conventional scores. RESULTS: Among 2762 patients, 391 (14.2%) developed major complications after hepatectomy. Diabetes mellitus, concurrent hepatitis C virus infection, HCC beyond the Milan criteria, cirrhosis, preoperative HBV-DNA level, albumin-bilirubin (ALBI), and aspartate transaminase to platelet ratio index (APRI) were identified as independent predictors of developing major complications, which were used to construct the online calculator ( http//www.asapcalculate.top/Cal11_en.html ). This model demonstrated good calibration and discrimination, with the C-indexes of 0.752 and 0.743 in the training and validation cohorts, respectively, which were significantly higher than those conventional scores (the training and validation cohorts 0.565 ~ 0.650 and 0.568 ~ 0.614, all P < 0.001). CONCLUSIONS: A web-based prediction model was developed to predict the probability of post-hepatectomy major complications in an individual HBV-infected patient with HCC. It can be used easily in the real-world clinical setting to help management-related decision-making and early warning, especially in areas with endemic HBV infection.