A Risk-Prediction Model for Hepatorenal Syndrome in Patients with Liver Failure: A Retrospective Analysis

Objectives To identify the risk factors and develop a risk-prediction model for hepatorenal syndrome (HRS) in patients with liver failure (LF). Methods A retrospective case-control study involving 372 inpatients with LF admitted to The First Affiliated Hospital of Guangxi University of Chinese Medicine between July 2012 and July 2022 was performed. Univariate and multifactorial logistic stepwise regression analyses were employed to identify risk factors for HRS. A risk-prediction model was constructed, and its predictive value was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. Results Combined ascites, combined spontaneous bacterial peritonitis, and high serum levels of gamma-glutamyl transpeptidase, uric acid, and cystatin C were independent risk factors for HRS. The areas under the ROC curve for the training and validation sets were 0.877 and 0.828, respectively. The logistic model demonstrated a good fit. In the decision curve analysis, the curves for both the training and validation sets were well-positioned away from the two extreme treatment strategies (all patients treated or untreated). Conclusions The risk-prediction model developed in this study for HRS in LF patients exhibits robust predictive capability, offering a valuable tool for timely clinical intervention and effective treatment of HRS.