Optimal First Trimester Preeclampsia Prediction: a Comparison of Multimarker Algorithm, Risk Profiles and Their Sequential Application

Abstract

OBJECTIVE: To compare performance of multimarker algorithm, risk profiles and their sequential application in prediction of preeclampsia and determining potential intervention targets. STUDY DESIGN: Maternal characteristics, ultrasound variables and serum biomarkers were collected prospectively at first trimester. Univariate analysis identified preeclampsia associated variables followed by logistic regression analysis to determine the prediction rule. Combined characteristics of the cardiovascular, metabolic and the personal risk factors were compared to the multimarker algorithm and the sequential application of both methods. RESULTS: Out of 2433 women, 108 developed preeclampsia (4.4%). Probability scores considering nulliparity, prior preeclampsia, body mass index, diastolic blood pressure and placental growth factor had an area under the receiver operating characteristic curve 0.784 (95% CI = 0.721-0.847). While the multimarker algorithm had the lowest false negative rate, sequential application of cardiovascular and metabolic risk profiles in screen positives reduced false positives by 26% and identified blood pressure and metabolic risk in 49/54 (91%) women with subsequent preeclampsia as treatable risk factors. CONCLUSION: Sequential application of a multimarker algorithm followed by determination of treatable risk factors in screen positive women is the optimal approach for first trimester preeclampsia prediction and identification of women that may benefit from targeted metabolic or cardiovascular treatmentinfo:eu-repo/semantics/publishedVersio