A Multimarker Approach for Early Prediction of Preeclampsia Using Uterine Artery Doppler Pulsatility Index, Hemodynamic, and Biochemical Parameters: A Prospective Observational Study.

Background Early identification of preeclampsia remains challenging, particularly in resource-limited settings. Although several clinical, biochemical, and Doppler markers have been explored, their individual predictive performance remains limited, necessitating evaluation of combined approaches. Objectives This study assesses the predictive value of mean arterial pressure (MAP), uterine artery Doppler pulsatility index (PI), pregnancy-associated plasma protein-A (PAPP-A), and mean platelet volume (MPV), individually and in combination, for early prediction of preeclampsia. Materials and methods In this prospective study, 150 singleton pregnancies were evaluated between 11 and 14 weeks of gestation. MAP, uterine artery Doppler PI, serum PAPP-A, and MPV were measured at recruitment. Participants were followed until delivery. Univariate analysis, logistic regression, receiver operating characteristic analysis, and detection rate assessment were performed. The study was approved by the Institutional Ethics Committee and conducted in accordance with the Declaration of Helsinki. Results Preeclampsia was observed in 19 women (12.7%). Among the individual markers, the highest discriminative ability was observed for MAP (AUC 0.84), and the MPV was the only predictor to show significance on multivariate analysis. PAPP-A and uterine artery Doppler PI were significantly different between the normotensive and preeclampsia groups in univariate analysis but did not demonstrate high predictive ability. The four-parameter model incorporating MAP, MPV, PAPP-A, and uterine artery Doppler PI demonstrated the highest predictive ability for preeclampsia (AUC 0.87; sensitivity 83.3%, specificity 81.5%). Conclusion The combination of MAP, MPV, PAPP-A, and uterine artery Doppler PI demonstrated higher predictive performance than individual markers in this study population and may support first-trimester risk stratification for preeclampsia.