Uterine artery hemodynamic parameters as key predictors of missed abortion risk: clinical validation based on a multimodal predictive model.

OBJECTIVE: To investigate the predictive value of uterine artery hemodynamic parameters for missed abortion and to develop and validate a multimodal prediction model for clinical use. METHODS: This retrospective study included 162 women with missed abortion and 812 women with normal early pregnancy from January 2023 to May 2024. Baseline clinical characteristics, including age, body mass index (BMI), and history of miscarriage, together with uterine artery Doppler parameters, including pulsatility index (PI), resistance index (RI), peak systolic velocity (PSV), and end-diastolic velocity (EDV), were collected from medical records. Group differences were analyzed using the t-test, Mann-Whitney U test, and chi-square test. Logistic regression was used to identify independent risk factors. A nomogram was constructed and evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The cohort was randomly divided into training and validation sets at a ratio of 7:3. RESULTS: The missed abortion group had significantly higher PI, RI, PSV, and EDV values than the normal pregnancy group (all P < 0.001). Among the four hemodynamic parameters, PI showed the best predictive performance, with an area under the curve (AUC) of 0.838. Multivariate logistic regression identified age, BMI, history of miscarriage, PI, RI, PSV, and EDV as independent predictors of missed abortion (all P < 0.05), with PI showing the strongest predictive value (odds ratio [OR] = 13.784). The nomogram achieved an AUC of 0.908 (95% confidence interval [CI]: 0.882-0.935) in the training set and 0.873 (95% CI: 0.819-0.927) in the validation set, with good calibration and clinical utility. CONCLUSIONS: Uterine artery hemodynamic parameters, particularly PI, are valuable predictors of missed abortion. The multimodal nomogram demonstrated good predictive performance and may assist in early risk stratification and individualized clinical management.