Objective To analyze the influencing factors of preeclampsia (PE) complicated with renal impairment and construct a prediction model.

Methods The clinical materials of 300 PE patients with antenatal examination in the hospital from January 2020 to January 2023 were retrospectively collected and divided into modeling set of 200 cases and validation set of 100 cases by a random number table in a ratio of 2 to 1. All the patients were followed up for 3 months after delivery. Patients in the modeling set with renal impairment were included in occurrence group, and the rest were included in non-occurrence group. Clinical materials between the modeling set and the validation set as well as between the occurrence group and the non-occurrence group within the modeling set were compared. Logistic regression analysis was conducted to identify the influencing factors of renal impairment in PE and construct a prediction model. The model performance was evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results There was no significant difference in clinical data between the modeling set and validation set (P>0.05). After completion of follow-up for 300 PE patients, renal impairment occurred in 75 cases (25.00%), including 24 cases (24.00%) in the validation set and 51 cases (25.50%) in the modeling set. A total of 51 patients with renal impairment in the modeling set were included in the occurrence group, and the remaining 149 patients were included in the non-occurrence group. Compared with the non-occurrence group, the occurrence group had significant older age, higher proportions of HELLP syndrome, early-onset PE and severe manifestations of PE, increased systolic blood pressure, diastolic blood pressure, resistance index (RI) and pulsatility index (PI) of renal interlobar artery as well as decreased peak systolic velocity (PSV) and end-diastolic velocity (EDV) of renal interlobar artery and platelet (PLT) (P < 0.05). Logistic regression analysis showed that age, HELLP syndrome, early-onset of PE, severe manifestations of PE, and RI of renal interlobar artery were influencing factors of renal impairment in PE (P < 0.05). A prediction model for renal impairment in PE was established based on the results of Logistic regression analysis in the modeling set. The area under the curve (AUC) of this model for predicting renal impairment was 0.949 (95%CI, 0.917 to 0.971) in the modeling set and 0.944 (95%CI, 0.900 to 0.972) in the validation set. Calibration curves for the modeling set and validation set were plotted, and the Hosmer-Lemeshow test showed no significant differences (P>0.05). Internal validation by the Bootstrap method showed consistency index was 0.913 (95%CI, 0.828 to 0.998) for the modeling set and 0.907 (95%CI, 0.840 to 0.974) for the validation set. According to DCA, the clinical net benefit of the prediction model was obtained at risk thresholds of 0.13 to 0.92 for the modeling set and 0.18 to 0.87 for the validation set.

Conclusion The influencing factors of renal impairment in PE include age, HELLP syndrome, early-onset of PE, severe manifestations of PE, and RI of renal interlobar artery. The prediction model established based on these factors demonstrates good performance in predicting renal impairment in PE, providing evidences for clinical assessment of renal impairment risk in PE.