Development of a Risk Score for the Prediction and Management of Pre-Eclampsia in Low-Resource Settings.

Background

Pre-eclampsia is a significant hypertensive disorder affecting 2-8% of pregnancies globally, significantly contributing to maternal/perinatal deaths. Early identification of at-risk patients is crucial for reducing these mortalities, yet first-trimester screening remains inaccessible in many low-resource settings. This study aims to develop a second-trimester risk stratification model based on clinical parameters to assist in managing pre-eclampsia in diverse healthcare contexts.

Methods

This retrospective cohort study analyzed medical records from 700 pregnancies (350 with preeclampsia, 350 controls) between January 2021 and August 2024 at a tertiary medical center in western Romania. Sample size was calculated to achieve 90% power with α = 0.05 for detecting clinically significant differences between groups. Data analysis focused on clinical variables such as maternal age, hypertension, diabetes, and socioeconomic factors. A scoring model was developed using logistic regression and validated for predictive accuracy using ROC curve analysis, with AUC as the primary metric. Calibration was assessed using the Hosmer-Lemeshow test.

Results

The risk stratification model demonstrated an AUC of 0.91 (95% CI: 0.88-0.94), indicating high discriminative capability. The model showed good calibration (p = 0.78). Sensitivity was 74.4%, and specificity reached 97.8%. Patients were categorized into low (0-4 points), moderate (5-7 points), and high-risk (≥8 points) groups based on optimized cut-off values. High-risk patients showed significantly higher rates of adverse outcomes, including eclampsia (12.3% vs. 0% in low-risk, p < 0.001) and HELLP syndrome (8.7% vs. 0.5% in low-risk, p < 0.001). Neonates born to high-risk mothers had lower birth weight (mean difference: 486 g, p < 0.001), smaller head circumference (mean difference: 2.3 cm, p < 0.001), and lower APGAR scores (median difference: 2 points, p < 0.001).

Conclusions

This novel model offers a practical second-trimester risk assessment tool that leverages routine clinical data available after 20 weeks of gestation. It facilitates targeted care and resource allocation, particularly benefiting settings lacking early screening access. Implementation of risk-stratified management protocols could significantly improve maternal and neonatal outcomes in diverse healthcare environments.