A nomogram for preeclampsia risk prediction in the first trimester: a nested case-control study.

BACKGROUND

Preeclampsia (PE) is a serious and progressive multisystem disease that often results in negative outcomes for neonates. This study aimed to create a nomogram to identify high-risk women with PE in their first trimester.

METHODS

This study involved a nested case-control cohort including 47 PE patients and 122 controls from The Second Affiliated Hospital of Zhengzhou University from January 1, 2023, to May 31, 2024. We identified independent risk factors for PE via multivariate logistic regression and developed a nomogram model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was used to evaluate clinical applicability.

RESULTS

Multivariate logistic regression revealed that pre-pregnancy body mass index (BMI), mean arterial pressure (MAP), and uric acid (UA) levels were positively correlated with PE, whereas placental growth factor (PLGF) was negatively correlated. The area under the curve (AUC) for the combined diagnostic value was 0.97 (95% CI: 0.96-0.99), suggesting satisfactory discrimination. DCA demonstrated that the predictive model provided high net benefits and significant clinical utility.

CONCLUSIONS

The nomogram developed in this study, which includes pre-pregnancy BMI, PLGF, MAP, and UA for the prediction of PE risk, can assist clinicians in identifying high-risk individuals during the first trimester.