Predictors of Miscarriage in Polycystic Ovary Syndrome Patients with Threatened Abortion: Development and Validation of a Nomogram Model

Luhang MaQingdiao ZHOU^*

• Wenzhou People's Hospital, Wenzhou, China

Objective: To investigate the risk factors associated with failed pregnancy maintenance in patients with polycystic ovary syndrome (PCOS) presenting with threatened abortion. Methods: Based on clinical diagnostic outcomes, 150 PCOS patients with early threatened abortion (gestational age ≤12 weeks) were categorized into two groups: a successful pregnancy maintenance group (n=100) and a failed pregnancy maintenance group (n=50). Relevant clinical parameters were collected, and binary logistic regression analysis was performed to identify independent risk factors for failed pregnancy maintenance. A nomogram prediction model was constructed using R software (version 4.21). The discriminative ability of the nomogram was evaluated using receiver operating characteristic (ROC) curve analysis, and a calibration curve was generated to assess model performance. Decision curve analysis (DCA) was employed to determine clinical utility. Results: The nomogram prediction model identified the following independent risk factors for failed pregnancy maintenance in PCOS patients (P < 0.05): testosterone levels, fasting insulin, and fasting blood glucose. These factors were incorporated into the final nomogram. The area under the ROC curve (AUC) ranged from 0.635 to 0.955, indicating strong discriminative power. The calibration curve closely approximated the ideal curve, demonstrating excellent model fit. Furthermore, decision curve analysis revealed that the model's clinical utility was superior to extreme scenarios, confirming its practical value. Conclusion: Three clinical variables were independently associated with failed pregnancy maintenance in PCOS patients with threatened abortion. The developed prediction model, based on these variables, exhibits high accuracy and clinical applicability, providing a reliable tool for risk stratification and clinical decision-making.