Abstract:Objective: To explore the influencing factors of adverse pregnancy outcomes in preeclampsia (PE) and build a risk prediction model. Methods: The retrospective selection of PE patients admitted to the Ninth Affiliated Hospital of Suzhou University and the Affiliated Hospital of Nantong University from June 2018 to December 2020 as the modeling set (model development). According to whether the patients had adverse pregnancy outcomes within 48 hours of admission, they were divided into adverse group and nonadverse group. Single factor screening was performed for each index of the two groups. Multivariate logistic regression analysis was performed to analyze the influencing factors of adverse pregnancy outcomes in PE. Based on the filtered results, R language was used to build a risk prediction nomogram model. Receiver operating characteristic curve analysis and goodness-of-fit bias tests were used to evaluate model performance. Bootstrap method (self-sampling method) was used to verify and make calibration chart. A decision curve was used to evaluate the clinical benefit rate of the model. The PE patients admitted to Suzhou Ninth Hospital affiliated to Suzhou University from January 2021 to March 2022 were taken as the validation set. Results: A total of 381 PE patients were included as the modeling set. Among them, 126 cases had adverse pregnancy outcomes, and 255 cases had no adverse pregnancy outcomes. A total of 102 PE patients were included as the validation set, among which, 34 had adverse pregnancy outcomes, and 68 had no adverse pregnancy outcomes. Logistic regression analysis showed: that the smaller the gestational age at admission (OR=2.672, 95%CI=1.495-5.153) , the more the number of clinical symptoms (OR=2.643, 95%CI=1.394-4.917) , and the higher the 24-hour proteinuria (OR=3.662, 95%CI=1.982-7.604) , lower platelet count (OR=2.396, 95%CI=1.307-4.653) , higher D-dimer (OR=2.929, 95%CI=1.728-5.843) , higher miR-21 expression levels (OR=4.302, 95%CI=2.426-9.185) were all influencing factors of adverse pregnancy outcomes in PE patients (P<0.05). Based on the above six factors, a risk prediction nomogram model was constructed. The area under the curve (AUC) of the model was 0.912 (95%CI=0.864-0.956) , the optimal cutoff value (the threshold probability) was 0.32, and the sensitivity and specificity at this time were 0.889 and 0.845, respectively; goodness-of-fit bias test ( χ2=4.214, P=0.837) ; the mean absolute error of the bias calibration curve was 0.013. The AUC of the validation set was 0.904 (95%CI=0.842-0.936) ; the sensitivity and specificity were 0.874 and 0.823, respectively; goodness-of-fit bias test (χ2=3.720, P=0.729) ; The mean absolute error of the bias calibration curve was 0.021. When the threshold probability value in the decision curve was set to 32.0%, clinical benefit rates for the modeling set and validation set were 69% and 76%, respectively. Conclusion: Adverse pregnancy outcomes in PE patients are related to gestational age at admission, number of clinical symptoms, 24-h proteinuria, platelet count, D-dimer, and miR-21 expression. Risk prediction nomogram model constructed in this way has high prediction performance.
