Published online Apr 7, 2025. doi: 10.3748/wjg.v31.i13.104697
Revised: February 20, 2025
Accepted: March 11, 2025
Published online: April 7, 2025
Processing time: 94 Days and 4.4 Hours
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. No
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.
Core Tip: We constructed a noninvasive predictive model using machine learning for esophagogastric varices in hepatitis B patients. An extreme gradient boosting model, based on ultrasound and serological markers, achieved high accuracy (area under the curve = 0.96) in predicting high-risk esophagogastric varices. Key predictive variables included albumin, prothrombin time, portal vein flow velocity and spleen stiffness. A web-based application was developed to facilitate clinical use, offering real-time risk assessment. This model provides a promising tool for targeted screening, potentially reducing the need for costly and risky endoscopic procedures in low-risk individuals.