Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Jiangxi Clinical Research Center for Gastroenterology, Nanchang, Jiangxi, China.
Int J Clin Pract. 2023 Aug 3;2023:9701841. doi: 10.1155/2023/9701841. eCollection 2023.
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.
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).
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.
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.
静脉曲张再出血是肝硬化的一种严重且潜在危及生命的并发症。不幸的是，目前还没有可靠的方法对高危患者进行分层。肝硬度测量（LSM）已被证明具有预测与门静脉高压相关并发症的价值，包括首次出血。然而，目前尚无证据证实 LSM 能够可靠地预测静脉曲张再出血。本研究的目的是评估生成极端梯度增强（XGBoost）算法模型来提高静脉曲张再出血预测能力的效果。
本回顾性分析研究了 284 例乙型肝炎相关肝硬化患者。使用实验室数据、LSM 和影像学数据为患者建立 XGBoost 模型，以预测再出血风险。此外，我们将 XGBoost 模型与传统逻辑回归（LR）模型进行了比较。我们使用接收者操作特征曲线（AUROC）和其他模型性能参数评估并比较了两种模型。最后，我们使用列线图和决策曲线分析（DCA）验证了模型。
在中位随访 66.6 周期间，72 例患者发生再出血，其中 21 例（7.39%）和 61 例（21.48%）患者分别在 6 周和 1 年内再次出血。简而言之，LR 模型在预测 6 周和 1 年内再出血的 AUC 分别为 0.828（0.759-0.897）和 0.799（0.738-0.860）。相比之下，XGBoost 模型在预测 6 周和 1 年内再出血的准确率分别为 0.985（0.907-0.731）和 0.931（0.806-0.935）。LSM 和高密度脂蛋白（HDL）水平在再出血组和非再出血组之间存在显著差异，LSM 是这些模型中的可靠预测因子。XGBoost 模型在预测 6 周和 1 年内再出血方面优于 LR 模型，ROC 和 DCA 曲线也证实了这一点。
XGBoost 算法模型在预测再出血方面的准确率高于 LR 模型，是一种具有临床价值的工具。这意味着 XGBoost 模型更适合预测食管静脉曲张再出血的风险。
