Abstract
Heart rate variability (HRV) provides an excellent proxy for monitoring of autonomic function, but the clinical utility of such characterization has not been investigated. In a clinical setting, the baseline autonomic function can reflect ability to adapt to stressors such as anesthesia. No monitoring tool has yet been developed that is able to track changes in HRV in real time. This study is a proof-of-concept for a non-invasive, real-time monitoring model for autonomic function via continuous Poincaré quantification of HRV dynamics. Anonymized heart rate data of 18 healthy individuals (18–45 years) undergoing minor procedures and 18 healthy controls (21–35 years) were analyzed. Patients underwent propofol and fentanyl anesthesia, and controls were at rest. Continuous heart rate monitoring was carried out from before aesthetic induction to the end of the surgical procedure. HRV components (sympathetic and parasympathetic) were extracted and analyzed using Poincaré quantification, and a real-time assessment tool was developed. In the patient group, a significant decrease in the sympathetic and parasympathetic components of HRV was observed following anesthesia (SD1: p = 0.019; SD2: p = 0.00027). No corresponding change in HRV was observed in controls. HRV parameters were modelled into a real-time graph. Using the monitoring technique developed, autonomic changes could be successfully visualized in real-time. This could provide the basis for a novel, fast and non-invasive method of autonomic assessment that can be delivered at the point of care.
Highlights
The autonomic nervous system (ANS) consists of two main components, the sympathetic and the parasympathetic nervous system (SNS and PSNS respectively), which are responsible for a wide variety of multisystem homeostatic changes, and play a part in the modulation of heart rate variability (HRV)
Quantification of Poincaré plots demonstrated visible changes in Heart rate variability (HRV) (Fig. 1), which were mathematically quantifiable across the two stages
HRV observed in resting controls was unchanged between Stage 1 and Stage 2 (Fig. 3) (Table 1)
Summary
The autonomic nervous system (ANS) consists of two main components, the sympathetic and the parasympathetic nervous system (SNS and PSNS respectively), which are responsible for a wide variety of multisystem homeostatic changes, and play a part in the modulation of heart rate variability (HRV). Variability in heart rate results from continuous modulation of the sino-atrial node (SAN) by the autonomic nervous system, which varies in response to multiple factors such as respiratory rate [1], homeostatic reflexes and centrally generated physiological patterns. Together, these factors influence the sympathovagal balance; it is this balance that defines the heart rate variability [2]. Impairments in autonomic function [3, 4] may be reflected by changes in heart rate variability [2]. This study sets out to provide an initial proof-of-concept for a novel tool that utilizes HRV as a surrogate measure of autonomic function to provide real-time, accurate and noninvasive measurement of autonomic function that can be delivered at the point of care
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