A prediction model of preeclampsia in hyperglycemia pregnancy

Abstract

Abstract Background Hyperglycemia in pregnancy (HIP) and preeclampsia (PE) are common pregnancy complications. Identifying high-risk factors for preeclampsia in pregnant women with hyperglycemia and predicting the occurrence of preeclampsia can contribute to disease grading management. This study aimed to develop a predictive model for the development of PE in pregnant women with HIP through the information of routine pregnancy care to provide a primary screening for more effective interventions. Method The clinical data from 951 pregnant women with hyperglycemia who delivered after 34 weeks of gestation were collected retrospectively. 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 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, evaluate the model from different perspectives. The internal validation was performed using the bootstrap procedure. Results Multivariate logistic regression analysis showed that cystatin C, uric acid, glutamyl aminotransferase, 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.