Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities - Wu, Yue; Yu, Xixuan; Li, Mengting; Zhu, Jing; Yue, Jun; Wang, Yan; Man, Yicun; Zhou, Chao; Tong, Rongsheng; Wu, Xingwei (Frontiers in Pharmacology, 2024)
Patients with immunologically abnormal pregnancies, encompassing autoimmune diseases and autoantibody abnormalities, face a heightened risk of miscarriages. To address this, a risk prediction model was developed to identify high-risk patients and facilitate timely interventions, ultimately aiming to significantly reduce adverse pregnancy outcomes. The study focused on patients with immunologically abnormal pregnancies attending the Sichuan Provincial People's Hospital, utilizing data obtained from the electronic medical record (EMR). Subsequently, the data was divided into a training set and a test set in an 8:2 ratio. Model development and validation involved the use of two sampling methods, two feature screening methods, and seven machine learning algorithms. Internally, the training set was validated using 10-fold cross-validation, while external validation of the test set was conducted using the bootstrap method. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics, leading to the development of a risk prediction platform based on the best models. Additionally, the SHapley additive expansion (SHAP) method was utilized to assess feature contributions. The study encompassed 565 patients, with 90 experiencing miscarriage (15.93%) and 475 live births (84.07%). Following data sampling and feature selection, four datasets were generated, leading to the development of 28 risk warning models for predicting the risk of miscarriage. External validation revealed that the best model achieved an AUC of 0.9209, along with an Accuracy of 0.8469, Precision of 0.8778, Recall of 0.8061, F1 score of 0.8404, and AUPRC of 0.9395. Notably, the results of the study highlighted that the total number of medications used was the most important factor contributing to miscarriage in the early warning model of miscarriage risk, in addition to the fact that the use of aspirin, low molecular heparin, glucocorticoid hormone analogues, progesterone analogues, and hydroxychloroquine during pregnancy reduced the risk of miscarriage. This comprehensive approach provides valuable insights for early risk assessment and intervention in immunologically abnormal pregnancies.