Background. Variceal rebleeding is a significant and potentially life-threatening complication of cirrhosis. Unfortunately, currently, there is no reliable method for stratifying high-risk patients. Liver stiffness measurements (LSM) have been shown to have a predictive value in identifying complications associated with portal hypertension, including first-time bleeding. However, there is a lack of evidence to confirm that LSM is reliable in predicting variceal rebleeding. The objective of our study was to evaluate the ability of generating a extreme gradient boosting (XGBoost) algorithm model to improve the prediction of variceal rebleeding. Methods. This retrospective analysis examined a cohort of 284 patients with hepatitis B-related cirrhosis. XGBoost models were developed using laboratory data, LSM, and imaging data to predict the risk of rebleeding in the patients. In addition, we compared the XGBoost models with traditional logistic regression (LR) models. We evaluated and compared the two models using the area under the receiver operating characteristic curve (AUROC) and other model performance parameters. Lastly, we validated the models using nomograms and decision curve analysis (DCA). Results. During a median follow-up of 66.6 weeks, 72 patients experienced rebleeding, including 21 (7.39%) and 61 (21.48%) patients who rebleed within 6 weeks and 1 year, respectively. In brief, the AUC of the LR models in predicting rebleeding at 6 weeks and 1 year was 0.828 (0.759–0.897) and 0.799 (0.738–0.860), respectively. In contrast, the accuracy of the XGBoost model in predicting rebleeding at 6 weeks and 1 year was 0.985 (0.907–0.731) and 0.931 (0.806–0.935), respectively. LSM and high-density lipoprotein (HDL) levels differed significantly between the rebleeding and nonrebleeding groups, with LSM being a reliable predictor in those models. The XGBoost models outperformed the LR models in predicting rebleeding within 6 weeks and 1 year, as demonstrated by the ROC and DCA curves. Conclusion. The XGBoost algorithm model can achieve higher accuracy than the LR model in predicting rebleeding, making it a clinically beneficial tool. This implies that the XGBoost model is better suited for predicting the risk of esophageal variceal rebleeding in patients.
Background. Cirrhosis esophageal variceal rebleeding is a major complication of chronic cirrhosis. The hepatic venous pressure gradient (HVPG) can predict the risk of rebleeding in patients with cirrhosis and has a good correlation with liver stiffness measurement (LSM). However, there are currently few studies based on liver stiffness to predict the risk of rebleeding in patients with liver cirrhosis. This study is aimed at exploring whether liver stiffness can predict rebleeding in patients with hepatitis B virus-related cirrhosis and developing an easy-to-use nomogram for predicting the risk of rebleeding in patients with liver cirrhosis undergoing secondary prevention. Methods. A prospective analysis of 289 cirrhosis patients was performed. Univariate and multivariate analyses were used to identify independent prognostic factors to create a nomogram. The performance of the nomogram was evaluated by using a bootstrapped-concordance index and calibration plots. Results. Use of a nonselective beta-blocker (NSBB) drug, LSM, hemoglobin, and platelet count were identified as factors that could predict rebleeding. We created a nomogram for rebleeding in cirrhosis by using these risk factors. The predictive ability of the nomogram was assessed by the C -index (0.772, 95% CI 0.732–0.822). The results of the calibration plots showed that the actual observation and prediction values obtained by the nomogram had good consistency. Conclusions. LSM can predict the risk of rebleeding in patients with cirrhosis, while the nomogram is a conventional tool for doctors to facilitate a personalized prognostic evaluation.
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