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

Abstract

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