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

Area of Science:

• Obstetrics and Gynecology
• Reproductive Medicine
• Clinical Prediction Modeling

Background:

• Preeclampsia (PE) is a significant cause of maternal and fetal morbidity worldwide.
• Early identification of PE risk factors is crucial for timely intervention and improved outcomes.
• Existing predictive models may not encompass all relevant clinical and biochemical markers.

Purpose of the Study:

• To investigate predictive factors for preeclampsia (PE) using single-center data.
• To establish and validate a nomogram prediction model for PE risk assessment.
• To aid in the early identification of high-risk PE pregnancies for individualized management.

Main Methods:

• Retrospective analysis of clinical data from 93 PE patients and 170 normal pregnant women.
• Logistic regression analysis to screen independent risk factors for PE.
• Construction and validation of a nomogram prediction model using AUC, sensitivity, and specificity.

Main Results:

• Maternal age, prepregnancy BMI, vitamin E deficiency, 25-(OH)D, placental growth factor (PLGF), pregnancy-associated plasma protein-A (PAPP-A), and pulsatility index (PI) were identified as independent risk factors for PE.
• The nomogram model demonstrated high predictive performance with an AUC of 0.957, sensitivity of 0.892, and specificity of 0.912.
• Internal validation using bootstrap testing confirmed the model's good calibration and fitting degree.

Conclusions:

• Maternal age, prepregnancy BMI, vitamin E status, 25-(OH)D, PLGF, PAPP-A, and PI are significant independent risk factors for predicting PE.
• The developed nomogram model offers a valuable tool for early clinical identification of PE high-risk groups.
• This model can support individualized clinical diagnosis and treatment strategies for PE.