A new risk score model to predict preeclampsia using maternal factors and mean arterial pressure in early pregnancy

The purpose of this study was to establish a multivariable risk-scoring model for preeclampsia (PE) prediction based on maternal characteristics and mean arterial pressure (MAP). Multivariate logistic regression analysis from 4600 pregnancies during a 10-year period was used to create the best fitting model. Significant risk factors and weighted scores consisted of age ≥30 years (3), BMI ≥25 kg/m2 (2), multifetal pregnancy (9), history of PE (9), adverse perinatal outcomes (6), pregnancy interval >10 years (5), nulliparous (5), underlying renal disease (10), chronic hypertension (6), autoimmune disease (5), diabetes (2) and MAP ≥95 mmHg (5). The model achieved an ROC area 0.771 with detection rates of 34%, 44%, 53% and 58% at 5%, 10%, 15% and 20% fixed false-positive rates, respectively. The new risk score model could be a clinically useful screening tool for PE. Pregnant women who have total scores of 9–13 (high risk) and more than 14 (very high risk) should receive aspirin prophylaxis.Impact Statement What is already known on this subject? Preeclampsia (PE) is the major cause of maternal and perinatal mortality and morbidity; it can be prevented by antiplatelet agents. What the results of this study add? A new model for identifying maternal at risk for PE using clinical risk factors and MAP was created. Weighted scores were defined for each variable for easy use in clinical practice. According to their probability for PE, pregnant women were classified into three subgroups: low risk (score 0–8), high risk (score 9–13) and very high risk groups (score ≥ 14). Aspirin should be prescribed to high risk and very high risk groups. For safety concerns, very high risk pregnancies should have close antenatal surveillance in a tertiary care hospital to reduce adverse outcomes during pregnancy and childbirth. What the implications are of these findings for clinical practice and/or further research? This new model for identifying pregnant women at high risk for PE has the potential to reduce the morbidity and mortality associated with this disease