Mathematical Modeling of the Biomarker Milieu to Characterize Preterm Birth and Predict Adverse Neonatal Outcomes.

PROBLEM: To identify preterm neonates at risk for adverse neonatal outcomes.

METHOD OF STUDY: A nested case-control study from the prospectively followed Boston Birth Cohort of mother-neonate pairs was performed. A classification model for preterm-born neonates was derived from 27 cord blood biomarkers using orthogonal projections to latent structures discriminant analysis. Predictive relationships were made between biomarkers and adverse outcomes using logistic regression.

RESULTS

From 926 births (53% of which were preterm), using weighted values for 27 biomarkers, a score was created that classified 73% of preterm deliveries. Soluble TNF-R1, NT-3, MCP-1, BDNF, IL-4, MMP-9, TREM-1, TNF-α, IL-5 and IL-10 were most influential. Our model was more sensitive for birth <34 weeks (sensitivity 89.5%, specificity 76.9%). IL-10, TNF-α, BDNF, NT-3, MMP-9, sTNF-R1 and MCP-1 were significantly predictive of NEC, IVH, sepsis and infections.

CONCLUSION

We developed a novel mathematical model of 27 biomarkers associated with adverse neonatal outcomes in neonates born preterm.