Mathematical Modeling of the Biomarker Milieu to Characterize Preterm Birth and Predict Adverse Neonatal Outcomes. (19th February 2016)

• Record Type: Journal Article
• Authors: Cordeiro, Christina N.; Savva, Yulia; Vaidya, Dhananjay; Argani, Cynthia H.; Hong, Xiumei; Wang, Xiaobin; Burd, Irina
• Is Part Of: American journal of reproductive immunology. Volume 75:Number 5(2016)
• Page Start: 594
• Page End: 601
• Publication Date: 2016-02-19
• Subjects: Cytokines -- mathematical model -- neonatal brain injury -- neonatal sepsis -- preterm birth; Human reproduction -- Immunological aspects -- Periodicals; 616.69206
• DOI: 10.1111/aji.12502
• Languages: English
• ISSNs: 1046-7408

Abstract

Abstract : 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.