Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology.

BACKGROUND

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.

AIM: To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.

METHODS

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.

RESULTS

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.

CONCLUSION

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.