A proposed method to predict preterm birth using clinical data, standard maternal serum screening, and cholesterol

Abstract

Objective: The objective of the study was to create a predictive model for preterm birth (PTB) from available clinical data and serum analytes.

Study design: Serum analytes and routine pregnancy screening plus cholesterol and corresponding health information were linked to birth certificate data for a cohort of 2699 Iowa women with serum sampled in the first and second trimester. Stepwise logistic regression was used to select the best predictive model for PTB.

Results: Serum screening markers remained significant predictors of PTB, even after controlling for maternal characteristics. The best predictive model included maternal characteristics, first-trimester total cholesterol, total cholesterol change between trimesters, and second-trimester alpha-fetoprotein and inhibin A. The model showed better discriminatory ability than PTB history alone and performed similarly in subgroups of women without past PTB.

Conclusion: Using clinical and serum screening data, a potentially useful predictor of PTB was constructed. Validation and replication in other populations, and incorporation of other measures that identify PTB risk, like cervical length, can be a step toward identifying additional women who may benefit from new or currently available interventions.