Risk Prediction for Adverse Pregnancy Outcomes in a Medicaid Population.

Abstract

Despite prior efforts to develop pregnancy risk prediction models, there remains a lack of evidence to guide implementation in clinical practice. The current aim was to develop and validate a risk tool grounded in social determinants theory for use among at-risk Medicaid patients. This was a retrospective cohort study of 409 women across 17 Cincinnati health centers between September 2013 and April 2014. The primary outcomes included preterm birth, low birth weight, intrauterine fetal demise, and neonatal death. After random allocation into derivation and validation samples, a multivariable model was developed, and a risk scoring system was assessed and validated using area under the receiver operating characteristic curve (AUROC) values. The derived multivariable model (n=263) included: prior preterm birth, interpregnancy interval, late prenatal care, comorbid conditions, history of childhood abuse, substance use, tobacco use, body mass index, race, twin gestation, and short cervical length. Using a weighted risk score, each additional point was associated with an odds ratio of 1.57 for adverse outcomes, p<0.001, AUROC=0.79. In the validation sample (n=146), each additional point conferred an odds ratio of 1.20, p=0.03, AUROC=0.63. Using a cutoff of 20% probability for the outcome, sensitivity was 29%, with specificity 82%. Positive and negative predictive values were 22% and 85%, respectively. Risk scoring based on social determinants can discriminate pregnancy risk within a Medicaid population; however, performance is modest and consistent with prior prediction models. Future research is needed to evaluate whether implementation of risk scoring in Medicaid prenatal care programs improves clinical outcomes.