Xiang Junmiao, Liu Lin, Bao Ruru, Cai Zhuhua
Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang ProvinceDian, Diagnostics Group Co.,Ltd, Hangzhou, Zhejiang Province, China.
BMC Pregnancy Childbirth. 2025 Mar 11;25(1):271. doi: 10.1186/s12884-025-07396-4.
Pregnancy loss significantly affects physical and mental health. A nomogram for predicting spontaneous abortion risk was developed to improve pregnancy outcomes.
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.
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).
The developed nomogram, incorporating AT-III, Hcy, C3, PC, and anti-β2GP1, aids clinicians in identifying pregnant women at high risk for spontaneous abortion.
妊娠丢失对身心健康有显著影响。为改善妊娠结局，开发了一种预测自然流产风险的列线图。
从温州医科大学附属第三医院招募了1346名孕妇（2020年5月至2022年5月）。训练集包括941名参与者，验证集有405名。使用随机森林模型优化特征选择，并通过多变量逻辑回归构建预测模型。用受试者工作特征（ROC）曲线、Hosmer-Lemeshow检验、校准曲线和临床影响曲线（CIC）评估列线图的性能。比较列线图与其单个变量之间的辨别力和临床效用。
抗凝血酶III（AT-III）、同型半胱氨酸（Hcy）、补体成分3（C3）、蛋白C（PC）和抗β2糖蛋白I抗体（抗β2GP1）被确定为危险因素。列线图显示出令人满意的辨别力（训练集AUC：0.813，95%CI：0.790-0.842；验证集AUC：0.792，95%CI：0.741-0.838）。Hosmer-Lemeshow检验（P = 0.331）表明拟合良好，CIC显示出临床净效益。列线图在辨别力方面优于单个变量（AUC：0.804，95%CI：0.779-0.829）。
所开发的列线图纳入了AT-III、Hcy、C3、PC和抗β2GP1，有助于临床医生识别自然流产高危孕妇。
