Adverse Pregnancy Outcomes in Women With Immune Abnormalities: Machine Learning Model Development and Validation Using First-Trimester Sonographic Features.

Background: The maintenance and progression of pregnancy rely on immune homeostasis at the maternal-fetal interface. However, pregnancy complicated by autoimmune abnormalities can disrupt this balance and significantly increase the risk of adverse pregnancy outcomes (APOs). Objective: This study aimed to (1) develop an interpretable predictive tool for APOs in patients with immune abnormalities and (2) interpret the models using Shapley additive explanations (SHAP) values. Methods: This study retrospectively analyzed clinical data from 288 patients with autoimmune abnormalities at Yichang Central People's Hospital between 2019 and 2024. Feature selection was performed using both the Boruta algorithm and Least Absolute Shrinkage and Selection Operator regression to identify optimal predictive factors associated with APOs. Nine machine learning models were developed and subsequently underwent a comprehensive comparative evaluation of their predictive performance, leading to the identification of the optimal predictive model. SHAP values were generated to provide interpretable insights into model predictions. Results: A total of 288 patients were included in the study, 124 (43.06%) of whom had APOs. The extreme gradient boosting algorithm was shown to be the optimal model after a comparison of 9 different models utilizing various metrics. The SHAP analysis showed that crown-rump length at 6+0 to 8+6 weeks, the number of other drugs, the number of complications during pregnancy, gestational sac volume at 6+0 to 8+6 weeks, and yolk sac diameter change at 6+0 to 8+6 weeks were the key predictive factors affecting APOs. Conclusions: The study developed an interpretable predictive tool for APOs in patients with immune abnormalities, which may assist clinicians in making early intervention decisions.