Yang Tian, Li Na, Qiao Chong, Liu Caixia
Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
Key Laboratory of Maternal-Fetal Medicine of Liaoning Province, Benxi, China.
Front Med (Lausanne). 2019 Dec 17;6:289. doi: 10.3389/fmed.2019.00289. eCollection 2019.
The aim of this study was to develop a nomogram to predict the risk of placenta accreta in scarred uterus patients in China. We retrospectively analyzed 8,371 singleton pregnancies with scarred uterus at Shengjing Hospital, affiliated with China Medical University. Two thirds of the patients were randomly assigned to the training set ( = 5,581), and one third were assigned to the validation set ( = 2,790). Multivariate logistic regression was performed by using the training set, and the nomogram was developed. Discrimination and calibration were performed by using both the training and validation sets. The multivariate logistic regression model identified number of previous cesarean section, number of vaginal bleeding, medication during pregnancy, and placenta previa as covariates associated with placenta accreta. A nomogram was developed to predict the risk of placenta accreta in the training set with a Harrell's C-index of 0.93 and 0.927 in the training set and validation set, respectively. Calibration of the nomogram predicted placenta accreta corresponding closely with the actual placenta accreta. We developed a nomogram predicting the risk of placenta accreta in scarred uterus patients in China. Validation using both the training set and the validation set demonstrated good discrimination and calibration, suggesting good clinical utility.
本研究的目的是开发一种列线图，以预测中国瘢痕子宫患者发生胎盘植入的风险。我们回顾性分析了中国医科大学附属盛京医院8371例瘢痕子宫单胎妊娠患者。三分之二的患者被随机分配到训练集（n = 5581），三分之一被分配到验证集（n = 2790）。使用训练集进行多因素逻辑回归分析，并构建列线图。使用训练集和验证集进行判别和校准。多因素逻辑回归模型确定既往剖宫产次数、阴道流血次数、孕期用药情况和前置胎盘为与胎盘植入相关的协变量。构建了一个列线图来预测训练集中胎盘植入的风险，训练集和验证集的Harrell's C指数分别为0.93和0.927。列线图预测的胎盘植入情况与实际胎盘植入情况密切相关。我们开发了一种列线图来预测中国瘢痕子宫患者发生胎盘植入的风险。使用训练集和验证集进行验证显示出良好的判别能力和校准效果，表明具有良好的临床实用性。
