Abstract
BACKGROUND: Pregnancy loss significantly affects physical and mental health. A nomogram for predicting spontaneous abortion risk was developed to improve pregnancy outcomes. METHODS: A total of 1346 pregnant women were enrolled from The Third Affiliated Hospital of Wenzhou Medical University (May 2020 - May 2022). The training set included 941 participants, and the validation set had 405. Feature selection was optimized using a random forest model, and a predictive model was constructed via multivariable logistic regression. The nomogram's performance was assessed with receiver operator characteristic (ROC), Hosmer-Lemeshow test, calibration curve, and clinical impact curve (CIC). Discrimination and clinical utility were compared between the nomogram and its individual variables. RESULTS: Antithrombin III (AT-III), homocysteine (Hcy), complement component 3 (C3), protein C (PC), and anti-β2 glycoprotein I antibody (anti-β2GP1) were identified as risk factors. The nomogram demonstrated satisfactory discrimination (Training AUC: 0.813, 95% CI: 0.790-0.842; Validation AUC: 0.792, 95% CI: 0.741-0.838). The Hosmer-Lemeshow test (P = 0.331) indicated a good fit, and the CIC showed clinical net benefit. The nomogram outperformed individual variables in discrimination (AUC: 0.804, 95% CI: 0.779-0.829). CONCLUSION: The developed nomogram, incorporating AT-III, Hcy, C3, PC, and anti-β2GP1, aids clinicians in identifying pregnant women at high risk for spontaneous abortion.