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  • Title: Temporal Patterns in Sheep Fetal Heart Rate Variability Correlate to Systemic Cytokine Inflammatory Response: A Methodological Exploration of Monitoring Potential Using Complex Signals Bioinformatics.
    Author: Herry CL, Cortes M, Wu HT, Durosier LD, Cao M, Burns P, Desrochers A, Fecteau G, Seely AJ, Frasch MG.
    Journal: PLoS One; 2016; 11(4):e0153515. PubMed ID: 27100089.
    Abstract:
    Fetal inflammation is associated with increased risk for postnatal organ injuries. No means of early detection exist. We hypothesized that systemic fetal inflammation leads to distinct alterations of fetal heart rate variability (fHRV). We tested this hypothesis deploying a novel series of approaches from complex signals bioinformatics. In chronically instrumented near-term fetal sheep, we induced an inflammatory response with lipopolysaccharide (LPS) injected intravenously (n = 10) observing it over 54 hours; seven additional fetuses served as controls. Fifty-one fHRV measures were determined continuously every 5 minutes using Continuous Individualized Multi-organ Variability Analysis (CIMVA). CIMVA creates an fHRV measures matrix across five signal-analytical domains, thus describing complementary properties of fHRV. We implemented, validated and tested methodology to obtain a subset of CIMVA fHRV measures that matched best the temporal profile of the inflammatory cytokine IL-6. In the LPS group, IL-6 peaked at 3 hours. For the LPS, but not control group, a sharp increase in standardized difference in variability with respect to baseline levels was observed between 3 h and 6 h abating to baseline levels, thus tracking closely the IL-6 inflammatory profile. We derived fHRV inflammatory index (FII) consisting of 15 fHRV measures reflecting the fetal inflammatory response with prediction accuracy of 90%. Hierarchical clustering validated the selection of 14 out of 15 fHRV measures comprising FII. We developed methodology to identify a distinctive subset of fHRV measures that tracks inflammation over time. The broader potential of this bioinformatics approach is discussed to detect physiological responses encoded in HRV measures.
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