Feng Si-Yi, Ding Zong-Ren, Cheng Jin, Tu Hai-Bin
Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
World J Gastroenterol. 2025 Apr 7;31(13):104697. doi: 10.3748/wjg.v31.i13.104697.
Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage. Endoscopy is the gold standard for EGV detection but it is invasive, costly and carries risks. Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization. Machine learning (ML) offers a powerful approach to analyze complex clinical data and improve predictive accuracy. This study hypothesized that ML models, utilizing noninvasive ultrasound and serological markers, can accurately predict the risk of EGVs in hepatitis B patients, thereby improving clinical decision-making.
To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.
We retrospectively collected ultrasound and serological data from 310 eligible cases, randomly dividing them into training (80%) and validation (20%) groups. Eleven ML algorithms were used to build predictive models. The performance of the models was evaluated using the area under the curve and decision curve analysis. The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.
Among the 310 patients, 124 were identified as high-risk for EGVs. The extreme gradient boosting model demonstrated the best performance, achieving an area under the curve of 0.96 in the validation set. The model also exhibited high sensitivity (78%), specificity (94%), positive predictive value (84%), negative predictive value (88%), F1 score (83%), and overall accuracy (86%). The top four predictive variables were albumin, prothrombin time, portal vein flow velocity and spleen stiffness. A web-based version of the model was developed for clinical use, providing real-time predictions for high-risk patients.
We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients. The model, presented as a web application, has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy.
严重食管胃静脉曲张（EGV）因有危及生命的出血风险，显著影响乙型肝炎患者的预后。内镜检查是EGV检测的金标准，但它具有侵入性、成本高且有风险。使用超声和血清学标志物的非侵入性预测模型对于识别高危患者和优化内镜检查的使用至关重要。机器学习（ML）提供了一种强大的方法来分析复杂的临床数据并提高预测准确性。本研究假设，利用非侵入性超声和血清学标志物的ML模型能够准确预测乙型肝炎患者发生EGV的风险，从而改善临床决策。
构建并验证一种使用ML的乙型肝炎患者EGV非侵入性预测模型。
我们回顾性收集了310例符合条件病例的超声和血清学数据，将它们随机分为训练组（80%）和验证组（20%）。使用11种ML算法构建预测模型。使用曲线下面积和决策曲线分析评估模型的性能。使用夏普利值加法解释（SHapley Additive exPlanation）对表现最佳的模型进行进一步分析，以解释特征重要性。
在310例患者中，124例被确定为EGV高危患者。极端梯度提升模型表现最佳，在验证集中曲线下面积达到0.96。该模型还具有高灵敏度（78%）、特异性（94%）、阳性预测值（84%）、阴性预测值（88%）、F1分数（83%）和总体准确率（86%）。前四个预测变量是白蛋白、凝血酶原时间、门静脉血流速度和脾脏硬度。开发了该模型的网络版以供临床使用，为高危患者提供实时预测。
我们确定了一种使用极端梯度提升的高效乙型肝炎患者EGV非侵入性预测模型。该模型以网络应用程序的形式呈现，具有筛查EGV高危患者的潜力，可帮助临床医生优化内镜检查的使用。
