Yang Lilin, Wang Haikuan, Li Yanfang, Zeng Cheng, Lin Xi, Gao Jie, Luo Songping
Department of Gynecology and Obstetrics, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
Acupuncture, Moxibustion and Rehabilitation School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Med (Lausanne). 2021 Nov 15;8:717978. doi: 10.3389/fmed.2021.717978. eCollection 2021.
The aim of this study was to develop a nomogram to predict the risk of premature rupture of membrane (PROM) in pregnant women with vulvovaginal candidiasis (VVC). We developed a prediction model based on a training dataset of 417 gravidas with VVC, the data were collected from January 2013 to December 2020. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. Predictors contained in the prediction nomogram included age, regular perinatal visits, history of VVC before pregnancy, symptoms with VVC, cured of VVC during pregnancy, and bacterial vaginitis. The model displayed discrimination with a C-index of 0.684 (95% confidence interval: 0.631-0.737). Decision curve analysis showed that the PROM nomogram was clinically useful when intervention was decided at a PROM possibility threshold of 13%. This novel PROM nomogram incorporating age, regular perinatal visits, history of VVC before pregnancy, symptoms with VVC, cured of VVC during pregnancy, and bacterial vaginitis could be conveniently used to facilitate PROM risk prediction in gravidas.
本研究的目的是开发一种列线图，以预测外阴阴道假丝酵母菌病（VVC）孕妇胎膜早破（PROM）的风险。我们基于417例患有VVC的孕妇的训练数据集开发了一种预测模型，数据收集于2013年1月至2020年12月。使用最小绝对收缩和选择算子回归模型对模型进行特征选择优化。应用多变量逻辑回归分析构建一个包含在最小绝对收缩和选择算子回归模型中选择的特征的预测模型。使用C指数、校准图和决策曲线分析评估预测模型的辨别力、校准度和临床实用性。使用自助法验证评估内部验证。预测列线图中包含的预测因素包括年龄、定期产前检查、孕前VVC病史、VVC症状、孕期VVC治愈情况和细菌性阴道炎。该模型的辨别力显示C指数为0.684（95%置信区间：0.631 - 0.737）。决策曲线分析表明，当在PROM可能性阈值为13%时决定干预，PROM列线图具有临床实用性。这种结合年龄、定期产前检查、孕前VVC病史、VVC症状、孕期VVC治愈情况和细菌性阴道炎的新型PROM列线图可方便地用于促进孕妇PROM风险预测。
