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Published on: February 14, 2021
A model-based spectral directional approach reveals the long-term impact of COVID-19 on cardiorespiratory control and
Beatrice Cairo1, Francesca Gelpi1, Vlasta Bari1,2
1Department of Biomedical Sciences for Health, University of Milan, Milan, Italy.
Background:
Coronavirus disease 19 (COVID-19) patients might develop sequelae after apparent resolution of the infection. Autonomic dysfunction and baroreflex failure have been frequently reported. However, the long-term effect of COVID-19 on cardiorespiratory and cardiovascular neural controls has not been investigated with directional approaches able to open the closed-loop relationship between physiological variables.
Methods:
A model-based causal spectral approach, namely causal squared coherence (CK2), was applied to the beat-to-beat variability series of heart period (HP) and systolic arterial pressure (SAP), and to the respiratory signal (RESP) acquired at rest in supine position and during active standing (STAND) in COVID-19 survivors 9 months after their hospital discharge. Patients were categorized according to their need of ventilatory support during hospitalization as individuals that had no need of continuous positive airway pressure (noCPAP, n = 27), need of continuous positive airway pressure in sub-intensive care unit (CPAP, n = 14) and need of invasive mechanical ventilation in intensive care unit (IMV, n = 8).
Results:
The expected decrease of the strength of the HP-RESP dynamic interactions as well as the expected increase of the dependence of HP on SAP along baroreflex during STAND was not observed and this result held regardless of the severity of the disease, namely in noCPAP, CPAP and IMV cohorts. Regardless of the experimental condition, spectral causality markers did not vary across groups either.
Conclusions:
CK2 markers, in association with an orthostatic challenge, were able to characterize the impairment of cardiorespiratory control and baroreflex in COVID-19 patients long after acute infection resolution and could be exploited to monitor the evolution of the COVID-19 patients after hospital discharge.
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