Clinical Application of a Multiparameter-Based Nomogram Model in Predicting Preeclampsia

Abstract

Based on single-center data, the related predictive factors of preeclampsia (PE) were investigated, and a nomogram prediction model was established and validated. A retrospective collection of 93 PE patients admitted to our hospital from January 2019 to January 2021 were included in the PE group. In addition, non-PE pregnant women were selected for physical examination during the same period for matching, and 170 normal pregnant women who met the matching conditions were found as the normal pregnancy group. Clinical data of the selected candidates were collected. The risk factors of PE were screened by logistic regression analysis, and the lipopograph prediction model was constructed and verified. Logistic analysis results showed that age (OR = 3.069, 95% CI = 1.233–7.638), prepregnancy BMI (OR = 2.896, 95% CI = 1.193–7.029), vitamin E deficiency (OR = 2.803, 95% CI = 1.134–6.928), 25-(OH)D (OR = 0.944, 95% CI = 0.903∼9.988), PLGF (OR = 0.887, 95% CI = 0.851∼0.924), PAPP-A (OR = 1.240, 95% CI = 1.131∼1.360), and PI (OR = 6.376, 95% CI = 1.163∼34.967) were the independent risk factors for PE prediction ( $P<0.05$ ). The ROC curve showed that the AUC of the model for predicting the risk of PE was 0.957 (95% CI: 0.935–0.979), and the specificity and sensitivity were 0.912 and 0.892, respectively. H-L goodness of the fit test showed that there was no statistical significance in the deviation between the actual observed value and the predicted value of the risk in the line graph model (χ2 = 7.001, $P=0.536$ ). The bootstrap test was used for internal verification, and the original data were repeatedly sampled 1000 times. The average absolute error of the calibration curve is 0.014, and the fitting degree between the calibration curve and the ideal curve is good. Age, prepregnancy BMI, lack of vitamin E, 25-(OH)D, PLGF, PAPP-A, and PI are independent risk factors for predicting PE. The establishment of a nomogram prediction model based on the above parameters can help identify PE high-risk groups in the early clinical stage and provide a reference for individualized clinical diagnosis and treatment.