A Novel Nomogram for Prediction of Post-Hepatectomy Liver Failure in Patients with Resectable Hepatocellular Carcinoma: A Multicenter Study
Jitao Wang, Zhanguo Zhang, Dong Shang, Yong Liao, Peng Yu, Jinling Li, Shubo Chen, Dengxiang Liu, Hongrui Miao, Shuang Li, Biao Zhang, Anliang Huang, Hao Liu, Yewei Zhang, Xiaolong Qi
Abstract
Objective: To develop a nomogram for predicting post-hepatectomy liver failure (PHLF) in patients with resectable hepatocellular carcinoma (HCC) based on portal hypertension, the extent of resection, ALT, total bilirubin, and platelet count. Methods: Patients with HCC hospitalized from January 2015 to December 2020 were included in a retrospective cohort study. 595 HCC patients were divided into a training cohort (n=416) and a validation cohort (n=179) by random sampling. Univariate and multivariable analyses were performed to identify the independent variables to predict PHLF. The nomogram models for predicting the overall risk of PHLF and the risk of PHLF B+C were constructed based on the independent variables. Comparisons were made by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) with traditional models, such as FIB-4 score, APRI score, CP class (Child-Pugh), MELD score (model of end-stage liver disease), and ALBI score (albumin-bilirubin) to analyze the accuracy and superiority of the nomogram. Results: We discovered that portal hypertension (yes vs no) (OR=1.677,95% CI:1.817-4.083, p=0.002), the extent of liver resection (OR=1.872,95% CI:3.937-47.096, p=0.001), ALT (OR=1.003,95% CI:1.003-1.016, P=0.003), total bilirubin (OR=1.036,95% CI:1.031-1.184, p=0.005), and platelet count (OR= 1.004, 95% CI:0.982-0.998, p=0.020) were independent risk factors for PHLF using multifactorial analysis. The nomogram models were constructed using well-fit calibration curves for each of these five covariates. When compared to the FIB4, ALBI, MELD, and CP score, our nomogram models have a better predictive value for predicting the overall risk of PHLF or the risk of PHLF B+C. The validation cohort's results were consistent. DCA also confirmed the conclusion. Conclusion: Our models, in the form of static nomogram or web application, were developed to predict PHLF overall risk and PHLF B+C risk in patients with HCC, with a high prediction sensitivity and specificity performance than other commonly used scoring systems.