Journal of International Obstetrics and Gynecology ›› 2024, Vol. 51 ›› Issue (1): 21-27.doi: 10.12280/gjfckx.20230728

• Obstetric Physiology & Obstetric Disease: Original Article • Previous Articles     Next Articles

Construction and Validation of A Predictive Model for Adverse Pregnancy Outcomes in Preeclampsia

ZHANG Ting, CHEN Zhen-yu(), LIU Sen, ZHANG Xiao-hong, LI Ya-meng, LI Cai-xi   

  1. Postgraduate Training Base, General Hospital of Northern Theater Command, Jinzhou Medical University, Shenyang 110003, China (ZHANG Ting, LIU Sen, ZHANG Xiao-hong, LI Ya-meng, LI Cai-xi); Department of Obstetrics and Gynecology, General Hospital of Northern Theater Command, Shenyang 110003, China (CHEN Zhen-yu)
  • Received:2023-09-15 Published:2024-02-15 Online:2024-02-19
  • Contact: CHEN Zhen-yu E-mail:czy740704@163.com

Abstract:

Objective: To analyze the risk factors associated with the occurrence of adverse perinatal pregnancy outcomes in preeclampsia, construct a risk prediction model and validate it. Methods: A retrospective analysis was conducted on clinical data of patients who delivered and diagnosed with preeclampsia at General Hospital of Northern Theater Command (our hospital) from January 1st, 2018 to December 31st, 2022. Multivariable logistic regression analysis was used to screen for independent risk factors for adverse perinatal pregnancy outcomes in preeclampsia. The R language was utilized to construct the risk prediction columnar graphical model. The predictive performance and goodness of fit of the model were evaluated based on the area under the receiver operator characteristic curve (AUC) and the Hosmer-Lemeshow test, the calibration curves were assessed for accuracy, decision analysis curve was used to assess the clinical use of the model. Patients with preeclampsia who delivered at our hospital from January 1st, 2023 to June 30th, 2023 were selected for external validation. Results: A total of 1 057 patients with preeclampsia were included for modelling, divided into a training set (739 patients) and a validation set (318 patients) in a 7∶3 ratio. 125 patients with preeclampsia were included for external validation. Multifactorial logistic regression analysis showed that the following factors were independent risk factors for adverse pregnancy outcomes in patients with preeclampsia: gestational week of onset ≤34 weeks, mean arterial pressure ≥120 mmHg (1 mmHg=0.133 kPa), fetal growth restriction, fibrinogen ≤4 g/L, urine protein qualification of ++ or higher, serum albumin ≤30 g/L, and lactate dehydrogenase ≥263 U/L (all P<0.05). Logistic risk prediction model was established accordingly. The AUC of the model was 0.941 (95%CI: 0.925-0.958), with Jordon index 0.382, specificity 87.8%, sensitivity 85.1%, and calibration curves show good agreement between the probability of predicting the occurrence of adverse pregnancy outcomes in preeclampsia and the probability of actual occurrence, internal validation calibration curves showed good model agreement. The external validation calibration curve showed that the model was well calibrated, and the Hosmer-Lemeshow test showed that the difference between the predicted probability of the model and the actual observed probability was not statistically significant ( χ2=12.164, P=0.144), clinical decision analysis curves indicated the clinical utility of nomograms to some extent. Conclusions: The prediction model of adverse pregnancy outcome in preeclampsia constructed has good accuracy, the selected indicators are simple and easy to obtain, and the nomograms are easy to apply, which can provide a certain reference basis for clinicians to assess the maternal and infant outcomes of patients with preeclampsia.

Key words: Pre-eclampsia, Pregnancy outcome, Nomograms, Risk assessment, Forecasting