Etiology-Specific Nomograms for Predicting First Variceal Bleeding in Decompensated HBV- and HCV-Related Cirrhosis

Meixia Du,1,* Tao Li,1,* Huai Li,1,* Xiaochun Zhang,1 Binqing Xiao,1 Mengni Zhen,1 Yongzhong Li,1 Yi Ouyang1,21Center for Infectious Diseases, Hunan University of Medicine General Hospital, Huaihua, Hunan, 418000, People’s Republic of China; 2Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi Ouyang; Yongzhong Li, Email chuyi_1993@163.com; liyz2008@163.comObjective: Patients with decompensated hepatitis B virus (HBV)- and hepatitis C virus (HCV)-related cirrhosis are at high risk of first variceal bleeding, yet etiology-specific and stage-specific prediction models remain limited. This study aimed to develop and internally validate nomogram models for predicting the risk of first variceal bleeding in this population.Methods: This retrospective cohort study included 451 patients with decompensated HBV- or HCV-related cirrhosis (331 HBV-related and 120 HCV-related), of whom 230 experienced first variceal bleeding. Random Forest and XGBoost algorithms were used for feature selection, followed by multivariable logistic regression to develop etiology-specific nomogram models. Internal validation was performed using a training-testing split. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), bootstrap resampling, and decision curve analysis, respectively. Partial Dependence Plots (PDPs) midpoint risk (≈ 0.5) set clinical thresholds.Results: Seven predictors were retained for HBV (Hemoglobin, Ascites Depth, Total Protein, Prothrombin Activity [PTA], Total Bile Acid [TBA], D-dimer, and White Blood Cell Count [WBC]) and three for HCV (Hemoglobin, PTA, and TBA), with an AUC of 0.9239 (95% CI 0.8941– 0.9537) and 0.9127 (95% CI 0.8646– 0.9609), respectively. The models showed good calibration and clinical utility on decision curve analysis. PDPs indicated 0.5-risk thresholds at 96.12 g/L (Hemoglobin), 60.75 g/L (Total Protein), 30.58 mm (Ascites Depth), 49.2% (PTA), 25.78 μmol/L (TBA), 2.24 mg/L (D-dimer), and 5.33× 109/L (WBC) for HBV, and 46.81% (PTA), 82.4 g/L (Hemoglobin), 47.51 μmol/L (TBA) for HCV.Conclusion: Etiology-specific nomogram models were developed to predict first variceal bleeding in patients with decompensated HBV- and HCV-related cirrhosis. The models showed good discriminative performance in internal validation and may assist in risk stratification. However, further prospective multicenter studies with external validation are needed before clinical implementation.Keywords: esophageal and gastric varices, decompensated liver cirrhosis, hepatitis B, hepatitis C, machine learning, risk assessment, nomograms