External validity of first-trimester algorithms in the prediction of pre-eclampsia disease severity.

OBJECTIVE: To compare disease features in women with pre-eclampsia between those who are correctly identified (true positive) and those who are missed (false negative) when applying first-trimester prediction algorithms for pre-eclampsia to a prospectively enrolled population.

METHOD: Six first-trimester early (requiring delivery < 34 weeks' gestation) pre-eclampsia algorithms were applied to a prospective cohort of singleton pregnancies enrolled at first-trimester screening. Maternal outcomes, neonatal outcomes and severity parameters for pre-eclampsia were compared between true-positive and false-negative predictions.

RESULTS: Twenty of 2446 (0.8%) women developed early pre-eclampsia, with 65% of these developing severe features and 20% HELLP syndrome. At enrollment, true-positive cases were more likely to be African-American and chronically hypertensive, while false-negative cases were more likely to be Caucasian. At delivery, true-positive cases were more likely to have pre-eclampsia superimposed on hypertension, severely elevated blood pressure and creatinine level > 1.1 mg/dL. False-negative cases were more likely to have HELLP syndrome (all P < 0.05).

CONCLUSION: In an urban population with a high prevalence of chronic hypertension, patients who are correctly identified by first-trimester screening models are more likely to develop pre-eclampsia superimposed on chronic hypertension with severely elevated blood pressure and evidence of renal failure. In contrast, patients who are missed by these algorithms are more likely to have HELLP syndrome. Further research is needed to confirm these findings and the algorithm adjustments that may be necessary to better predict pre-eclampsia phenotypes.