Diabetes, Metabolic Syndrome and Obesity (Mar 2024)
A Prediction Model of Preeclampsia in Hyperglycemia Pregnancy
Abstract
Yan Fang,1,2 Huali Liu,1,2 Yuan Li,1,2 Ji Cheng,1 Xia Wang,1 Bing Shen,3 Hongbo Chen,1,2 Qunhua Wang4 1Department of Obstetrics and Gynaecology, Maternal and Child Health Hospital Affiliated to Anhui Medical University, Hefei, People’s Republic of China; 2The Fifth Clinical College of Anhui Medical University, Hefei, People’s Republic of China; 3School of Basic Medicine, Anhui Medical University, Hefei, People’s Republic of China; 4Department of Obstetrics and Gynaecology, the First Affiliated Hospital of USTC, Hefei, People’s Republic of ChinaCorrespondence: Hongbo Chen, Maternal and Child Health Hospital Affiliated to Anhui Medical University, No. 15 Yimin Street, Luyang District, Hefei City, Anhui Province, People’s Republic of China, Email [email protected] Qunhua Wang, Department of Obstetrics and Gynaecology, the First Affiliated Hospital of USTC, Hefei, People’s Republic of China, Email [email protected]: To investigate the risk factors associated with preeclampsia in hyperglycemic pregnancies and develop a predictive model based on routine pregnancy care.Patients and Methods: The retrospective collection of clinical data was performed on 951 pregnant women with hyperglycemia, including those diagnosed with diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM), who delivered after 34 weeks of gestation at the Maternal and Child Health Hospital Affiliated to Anhui Medical University between January 2017 and December 2019. Observation indicators included liver and kidney function factors testing at 24– 29+6 weeks gestation, maternal age, and basal blood pressure. The indicators were screened univariately, and the “rms” package in R language was applied to explore the factors associated with PE in HIP pregnancy by stepwise regression. Multivariable logistic regression analysis was used to develop the prediction model. Based on the above results, a nomogram was constructed to predict the risk of PE occurrence in pregnant women with HIP. Then, the model was evaluated from three aspects: discrimination, calibration, and clinical utility. The internal validation was performed using the bootstrap procedure.Results: Multivariate logistic regression analysis showed that cystatin C, uric acid, glutamyl aminotransferase, blood urea nitrogen, and basal systolic blood pressure as predictors of PE in pregnancy with HIP. The predictive model yielded an area under curve (AUC) value of 0.8031 (95% CI: 0.7383– 0.8679), with an optimal threshold of 0.0805, at which point the sensitivity was 0.8307 and specificity of 0.6604. Hosmer–Lemeshow test values were P = 0.3736, Brier score value was 0.0461. After 1000 Bootstrap re-samplings for internal validation, the AUC was 0.7886, the Brier score was 0.0478 and the predicted probability of the calibration curve was similar to the actual probability. A nomogram was constructed based on the above to visualize the model.Conclusion: This study developed a model for predicting PE in pregnant women with HIP, achieving high predictive performance of PE risk through the information of routine pregnancy care.Keywords: hyperglycemia in pregnancy, preeclampsia, prediction model, nomogram, internal validation
