Validation of a multiple marker test for early pregnancy outcome prediction

Abstract

Purpose: To validate the use of a multiple biomarker test panel for predicting first trimester pregnancy outcome in a multi-center cohort.

Methods: A case-control study of women presenting with pain and bleeding in early pregnancy at 5-10 weeks gestational age was performed at three academic centers. Sera from women with ectopic pregnancy (EP), viable intrauterine pregnancy (IUP), and miscarriage (SAB) were analyzed via immunoassay for Activin A (AA), Progesterone (P4), A Disintegrin And Metalloprotease-12 (ADAM12), pregnancy-associated plasma protein A (PAPP-A), glycodelin (Glyc), and human chorionic gonadotropin (hCG). Biomarkers were assessed for reproducibility using medians, ranges, standard deviations, and area under receiver-operating characteristic curve (AUC) and accuracy in early pregnancy outcome classification compared to a previous derivation population.

Results: In 192 pregnancies, the biomarkers demonstrated good reproducibility with similar medians, ranges, and AUCs when compared to the derivation population except glycodelin. Pregnancy location was conclusively classified in 53% (n = 94) of the whole study sample with 78% accuracy. Pregnancy viability was conclusively classified in 58% (n = 112) of the new sample with 89% accuracy. Results were similar with subsequent model revisions where glycodelin was excluded and in the subgroups of subjects with a hCG below 2000 mIU/mL and a gestational age less than 6 weeks.

Conclusion: The use of a panel of biomarkers to maximize test accuracy of a prediction of pregnancy location and prediction of pregnancy viability was reproducible and validated in an external population from which it was derived, but clinical utility is limited based on the test characteristics obtained.

Keywords: Biomarker; Ectopic pregnancy; Validation.

Fig. 1

Final diagnosis of each individual was only made if classification of the subject was in agreement from both trees. If both trees classified the subject as a case or a control, definitive classification was determined. If the classification did not agree (i.e., one tree classified the subject as case and the other a control), the subjects were not definitively diagnosed and were labeled “indeterminate.” a Derived decision trees to predict pregnancy location. b Derived decision trees to predict pregnancy viability