A mathematical model of nutrient delivery during labour: predicting fetal distress due to severe acidemia

Abstract

The mainstay of human labour monitoring, fetal heart rate (FHR), has low positive predictive value (PPV) for severe acidemia, which is associated with increased risk for brain injury. Fetal electroencephalogram (EEG) is feasible during labour and FHR-EEG monitoring would improve the detection of early onset of acidemia. The physiological challenge is generalizing the observations on the dynamic FHR-EEG interaction predictive of incipient severe acidemia from a limited spectrum of FHR-EEG responses that can be induced in vivo. We present a mathematical model that includes blood flow to the heart and brain, so that model output corresponds to FHR and EEG measurements. Model simulations are compared to FHR and EEG data from fetal ovine studies and show a qualitatively good fit: The coherence shows a correlation between ECOG and FHR signals when occlusion is severe, identical to that seen in experimental data. Fuzzy Entropy for simulated ECOG at four occlusion levels distinguishes severe occlusion from less severe levels. In conclusion, our heart-brain oscillator model, coupled by the nutrient flow, reproduces some of the salient dynamics observed in the data. In silico derived theoretical conclusions can be tested in vivo and against clinical data, suggesting new diagnostic criteria of fetal acidemia associated with cerebral compromise for timely intervention during labour.