Factors influencing recurrence of preeclampsia in pregnant women with a history of preeclampsia and the establishment of a predictive model.

Objectives: To establish and verify the prediction model of recurrent preeclampsia (PE) in pregnant women with a history of PE.

Methods: Totally 130 pregnant women with a history of PE from Jan 2021 to Jan 2023 were selected retrospectively. The patients were randomly matched according to the proportion of 1:4 to establish a verification group (nasty 26) and a modeling group (nasty 104). The modeling patients were divided into two groups according to the occurrence of preeclampsia: recurrent group (nasty 50) and non-recurrent group (nasty 54). Multivariate logistic regression analysis of influencing factors was established. Calibration curve was performed to verify, decision curve analysis (DCA) was used to evaluate the clinical practicability of the prediction model, and ROC analysis was used to show the prediction value of the model.

Results: Multivariate logistic regression analysis showed that there were significant differences in age, gestational age, gestational interval, systolic blood pressure and diastolic blood pressure of previous pregnancy. (p<0.05) According to the results of logistic regression analysis, a prediction model was constructed. Logit(P)=(0.910Age)+(0.987Age of onset of previous pregnancy)+(1.167Gestational interval)+(1.186Systolic blood pressure in previous pregnancy)+(0.970Diastolic blood pressure in previous pregnancy).The slope of the calibration curve was close to one in the training set and verification set. The results showed that the prediction of recurrent PE risk of pregnant women with history of eclampsia was consistent with the actual risk. ROC analysis showed that the area under curve was 0.991. The results of DCA analysis showed that the model had good clinical practicability.

Conclusions: In this study, a prediction model is successfully established and verified according to the influencing factors.