Predicting the risk of pre-eclampsia between 11 and 13 weeks' gestation by combining maternal characteristics and serum analytes, PAPP-A and free β-hCG

Objective: To determine if a simplified model for predicting pre-eclampsia (PEC) can be developed by combining first-trimester serum analytes, pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotrophin (β-hCG), and maternal characteristics. Methods: A retrospective cohort study of patients seen for first-trimester aneuploidy screening from 2003 to 2009. The 5th, 10th, 90th, and 95th percentiles for the analyte multiples of the medians (MoMs) for our population were determined and evaluated for association with PEC. Univariate and backward stepwise logistic regression analyses were performed and the area under the receiver operating characteristic (ROC) curves [area under curve (AUC)] used to determine the best models for predicting PEC. Results: Among 4020 women meeting the inclusion criteria, outcome data was available for 3716 (93%). There were 293 cases of PEC. The final model identified a history of pre-gestational diabetes [aOR 2.6, 95% confidence interval (CI) 1.7-3.9], chronic hypertension (cHTN) (aOR 2.6, 95% CI 1.7-3.9), maternal body mass index (BMI) > 25 (aOR 2.5, 95% CI 1.9-3.4), African American race (aOR 1.8, 95% CI 1.3-2.6), and PAPP-A MoM < 10th percentile (aOR 1.6, 95% CI 1.1-2.4) to be significant predictors of PEC (AUC = 0.70, 95% CI 0.65-0.72). Conclusion: Low first-trimester PAPP-A levels are associated with the development of PEC; however, the model was only modestly efficient in its predictive ability.