Development and Validation of a Risk Prediction Model for Hepatorenal Syndrome in Hepatic Failure Patients Based on Glucose-6-Phosphate Dehydrogenase and Hepatic and Renal Function Biochemical Parameters
Abstract
Objective: This study aimed to develop and validate a novel risk prediction model for hepatorenal syndrome (HRS) in hepatic failure (HF) patients by integrating glucose-6-phosphate dehydrogenase (G6PD) activity with conventional hepatic and renal function biochemical parameters, thereby enhancing early HRS detection beyond the limitations of traditional indicators.
Methods: We performed a retrospective analysis of 267 HF patients (82 with HRS, 185 without HRS) hospitalized between July 2020 and July 2022. G6PD levels and standard hepatic/renal function biochemical parameters (ALT, AST, TBil, GGT, BUN, Scr, UA, and CysC) were assessed. Key predictors were identified via Lasso regression, and a multivariate logistic regression model was developed. Model performance was evaluated using receiver operating characteristic (ROC) analysis, with internal validation conducted through a 70:30 training-validation split.
Results: HRS patients exhibited significantly lower G6PD activity than non-HRS HF controls (P < 0.05). While G6PD alone showed moderate predictive value (AUC = 0.742; sensitivity 59.76%, specificity 79.12%), the composite model integrating G6PD, GGT, UA, and CysC demonstrated markedly improved discrimination, achieving AUCs of 0.942 (95%CI:0.905-0.979) in the training cohort and 0.998(95%CI:0.0.993-1.000) in the validation cohort with both sensitivity and specificity outperforming individual indicators. The derived risk equation was Combined testingYouden = -17.038 + -0.116 × G6PD + 0.102 × GGT + 0.016 × UA + 0.040 × Scr + 3.760 × CysC.
Conclusion: The integration of G6PD with hepatic (GGT) and renal (UA, CysC) function biochemical parameters significantly enhances HRS risk stratification in HF patients. This validated tool offers superior sensitivity and specificity for the early identification of HRS.
Copyright (c) 2025 Hao Liu, Dewen Mao, Kan Zhang, Tingshuai Wang, Yanmei Lan, Minggang Wang
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