COVID-19 and silent hypoxemia in a minimal closed-loop model of the respiratory rhythm generator

Casey O. Diekman, Peter J. Thomas, Christopher G. Wilson

Research output: Contribution to journalArticlepeer-review

Abstract

Silent hypoxemia, or “happy hypoxia,” is a puzzling phenomenon in which patients who have contracted COVID-19 exhibit very low oxygen saturation (SaO2 < 80%) but do not experience discomfort in breathing. The mechanism by which this blunted response to hypoxia occurs is unknown. We have previously shown that a computational model of the respiratory neural network (Diekman et al. in J Neurophysiol 118(4):2194–2215, 2017) can be used to test hypotheses focused on changes in chemosensory inputs to the central pattern generator (CPG). We hypothesize that altered chemosensory function at the level of the carotid bodies and/or the nucleus tractus solitarii are responsible for the blunted response to hypoxia. Here, we use our model to explore this hypothesis by altering the properties of the gain function representing oxygen sensing inputs to the CPG. We then vary other parameters in the model and show that oxygen carrying capacity is the most salient factor for producing silent hypoxemia. We call for clinicians to measure hematocrit as a clinical index of altered physiology in response to COVID-19 infection.

Original languageEnglish (US)
JournalBiological Cybernetics
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • General Computer Science

Keywords

  • Breathing control
  • Central pattern generator
  • Computational modeling
  • COVID-19
  • Polycythemia
  • Sensory feedback
  • Silent hypoxemia

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