Early Prediction Model for Preterm Birth Combining Demographic Characteristics and Clinical Characteristics

Abstract Objective. To create early prediction models for preterm birth (PTB) based on the Chinese population, combining demographic characteristics and clinical characteristics.Methods. A retrospective study on 15197 pregnant women who were recruited in Obstetrics and Gynecology Hospital of Zhejiang University from January1, 2017 to December 31, 2017. Demographic characteristics and clinical characteristics were collected and were randomly divided into the observation group (80%) and the validation group (20%). Multivariable Logistics regression analysis was performed to develop a risk prediction model in the observation group and the validation group. It was evaluated by the value of area under the curve (AUC) of receiver operating characteristic (ROC). Finally, we got a simple scoring system to present the preterm birth risk. Results. There were 1082 pregnant women (8.9%) developed PTB in the observation group and 316 pregnant women (10.3%) in the validation group. Gravidity, educational level, residence, previous history of PTB, twin pregnancy, pre-gestational diabetes mellitus (type I or II), chronic hypertension, placenta previa, gestational hypertension were significant predictors of future PTB. These factors were all included in the model, the AUC was 0.746 with sensitivity of 61.4% (95%CI: 61.4-66.7%) and specificity of 86.6% (95%CI: 85.2-87.9%) at the threshold score of 8.Conclusion. PTB can be predicted by demographic characteristics and clinical characteristics pre-pregnancy or during pregnancy, thus predicting and preventing PTB as early as possible.