Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus

Author:

Li Hanbing1ORCID,Gao Lingling2,Yang Xiao2,Chen Lu2

Affiliation:

1. School of Nursing University of South China Hengyang Hunan China

2. School of Nursing Sun Yat‐sen University Guangzhou China

Abstract

AbstractObjectivesThis study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM).DesignWe conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat‐sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets.Patients and MeasurementsPatients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75‐g oral glucose tolerance test during 24‐28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks.ResultsThe incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high‐density lipoprotein, homocysteine, and fasting blood glucose of 75‐g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer–Lemeshow test, demonstrating a correlation between the predicted and observed risk.ConclusionThis study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

Funder

National Natural Science Foundation of China

Publisher

Wiley

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