Is the heart rate variability monitoring using the analgesia nociception index a predictor of illness severity and mortality in critically ill patients with COVID-19? A pilot study

Does whole-body vibration improve risk of falls, balance, and heart rate variability in post-COVID-19 patients? A randomized clinical trial

INTRODUCTION

Whole-Body Vibration (WBV) can be a therapeutic recovery strategy for patients hospitalized for COVID-19.

OBJECTIVES

To evaluate the effects of a 36-session WBV protocol on the risk of falls, balance, mobility and heart rate variability (HRV).

STUDY DESIGN

A randomized clinical trial.

METHODS

13 patients affected by COVID-19, trained with WBV, 3×/week on alternate days, totaling 36 sessions, were evaluated before and after the intervention.

RESULTS

WBV training at 2 mm and 4 mm amplitude resulted in a reduction in the risk of falls when compared to Sham (p = 0.023), with effect size of 0.530. No changes were observed for mobility and balance outcomes (p = 0.127) or for any of the HRV variables (p = 0.386).

CONCLUSION

WBV training reduced the risk of falls in post-COVID patients. No changes were observed regarding balance and mobility, nor for HRV.

Applicability of heart rate variability for cardiac autonomic assessment in long-term COVID patients: A systematic review

PURPOSE

To carry out a systematic review to determine the main methods used to study the heart rate variability (HRV) in individuals after the acute phase of COVID-19.

METHODS

The study followed the Preferred Items for Reporting for Systematic Reviews and Meta-Analyses (PRISMA) statement. PubMed, Web of Science, Scopus and CINAHAL electronic databases were searched from the inception to November 2022. The studies were included if they used HRV assessment based on linear and non-linear methods in long-term COVID-19 patients. Review studies, theses and dissertations, conference abstracts, longitudinal studies, studies conducted on animals and studies that included individuals in the acute phase of the COVID-19 were excluded. The methodological quality of the studies was analyzed using the Joanna Briggs Institute's critical evaluation checklist for cross-sectional analytical studies.

RESULTS

HRV was mainly assessed using 24-h Holter monitoring in 41.6% (5/12) of the studies, and 12‑lead ECG was used in 33.3% (4/12). Regarding the type of assessment, 66.6% (8/12) of the studies only used linear analysis, where 25% (3/12) used analysis in the time domain, and 41.6% (5/12) used both types. Non-linear methods were combined with the previously cited linear method in 25% (3/12) of the studies. Moreover, 50% (6/12) of the studies demonstrated post-COVID-19 autonomic dysfunction, with an increase in the predominance of cardiac sympathetic modulation. The average score of the evaluation checklist was 6.6, characterized as having reasonable methodological quality.

CONCLUSION

24-h Holter and 12‑lead ECG are considered effective tools to assess HRV in post-COVID-19 patients. Furthermore, the findings reveal diverse effects of COVID-19 on the autonomic nervous system's sympathovagal balance, which might be influenced by secondary factors such as disease severity, patients' overall health, evaluation timing, post-infection complications, ventilatory functions, and age.

Heart rate variability is reduced in COVID-19 survivors and associated with physical activity and fatigue

Reduced heart rate variability (HRV) and fatigue are common after COVID-19 infection and both are potentially influenced by physical activity (PA). We compared resting HRV, PA from accelerometers and questionnaires, and self-reported fatigue in 41 COVID-19 survivors (~8 months postinfection, 38 ± 17 years) with 41 matched controls. Differences in HRV were observed on acceleration capacity (p = 0.041), deceleration capacity (p = 0.032), high-frequency peak frequency (p = 0.019), absolute low-frequency power (p = 0.042), relative very low-frequency power (p = 0.012), SD2 (from Poincare plot; p = 0.047), and DFA2 (slope of long-term detrended fluctuation analysis; p = 0.004). Fatigue was greater in COVID-19 survivors (p < 0.001) with no differences in PA. Moderate-vigorous physical activity (MVPA) (Standardized Beta = -0.427, p = 0.003) and steps per day (Standardized Beta = -0.402, p = 0.007) were associated with DFA2 in COVID-19 survivors after controlling for age, sex, and body fat percentage. Fatigue was correlated to less MVPA (Spearman's rho = 0.342, p = 0.031) and fewer steps per day (rho = 0.329, p = 0.038) in COVID-19 survivors, and was indirectly linked to HRV through these PA mediators (Estimate = -0.20; p = 0.040). We present a model showing the complex relations between HRV, PA, and fatigue that provides the foundation for strategies to improve outcomes and rehabilitation after COVID-19 infection.

Impact of long COVID on the heart rate variability at rest and during deep breathing maneuver

While the majority of individuals with coronavirus disease 2019 (COVID-19) recover completely, a significant percentage experience persistent symptom, which has been characterized as Long COVID and may be associated with cardiac and autonomic dysfunction. We evaluated heart rate variability (HRV) at rest and during deep-breathing (M-RSA) in patients with Long COVID. Case-control design involved 21 patients with Long COVID and 20 controls; the HRV was evaluated (POLAR system) at rest in the supine position and during M-RSA and expressed in time domain and non-linear analysis. In the supine position we found a reduction HRV measures in Long COVID' patients compared to controls for: Mean_iRR (p < 0.001), STD_iRR (p < 0.001); STD_HR (p < 0.001); SD1 (p < 0.001); SD2 (p < 0.001); alpha2 (p < 0.001). In the M-RSA we found reduction Mean_iRR (p < 0.001), STD_iRR (p < 0.001), STD_HR (p < 0.001), rMSSD (p < 0.001), RR_tri-index (p < 0.001) in Long COVID' patients except for highest Mean_HR p < 0.001. In conclusion, Long COVID reduced HRV at rest and during deep breathing. These findings may imply impairment of cardiac autonomic control when symptoms of COVID-19 persist following initial recovery.

Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis

BACKGROUND

Recent studies have linked low heart rate variability (HRV) with COVID-19, indicating that this parameter can be a marker of the onset of the disease and its severity and a predictor of mortality in infected people. Given the large number of wearable devices that capture physiological signals of the human body easily and noninvasively, several studies have used this equipment to measure the HRV of individuals and related these measures to COVID-19.

OBJECTIVE

The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators of COVID-19, as well as the onset and worsening of symptoms in affected individuals.

METHODS

A systematic review was conducted searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE Xplore. Studies had to include (1) measures of HRV in patients with COVID-19 and (2) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce possible biases and increase the statistical power of the primary research.

RESULTS

The main finding was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported that a reduction in HRV parameters is associated with COVID-19. Individuals with COVID-19 presented a reduction in the SD of the normal-to-normal interbeat intervals and root mean square of the successive differences compared with healthy individuals. The decrease in the SD of the normal-to-normal interbeat intervals was 3.25 ms (95% CI -5.34 to -1.16 ms), and the decrease in the root mean square of the successive differences was 1.24 ms (95% CI -3.71 to 1.23 ms).

CONCLUSIONS

Wearable devices that measure changes in HRV, such as smartwatches, rings, and bracelets, provide information that allows for the identification of COVID-19 during the presymptomatic period as well as its worsening through an indirect and noninvasive self-diagnosis.

Linear and non‑linear indices of vagal nerve in relation to sex and inflammation in patients with Covid‑19

Hyperinflammation is one of the most important pathophysiological risk factors for poor prognosis in patients with coronavirus disease-2019 (Covid-19). Low vagal neuro-immune modulation can lead into this kind of immune dysregulation. The association between vagal activity, sex and inflammatory markers were investigated in patients with Covid-19. A total of 19 patients with Covid-19 were included in the present study. Vagus nerve activity was indexed by heart rate variability (HRV) derived from electrocardiogram at hospital admission. Linear HRV parameters included the root mean square of successive RR interval differences (RMSSD) and high-frequency HRV (HF-HRV), while non-linear parameters included 2 UV%. Immune/inflammatory parameters included C-reactive protein (CRP), interleukin-6 (IL-6), neutrophil/lymphocyte ratio (NLR), systemic inflammatory index (SII), and procalcitonin (PCT). It has been revealed that both linear HRV indices HF-HRV and RMSSD, are significantly negatively correlated with CRP and IL-6, independent of age. The non-linear index of 2 UV% is significantly negatively correlated with NLR and SII, which reflect subtle changes in the response of immunocompetent cells. Patients that received high-flow nasal oxygen therapy had significantly higher IL-6 and CRP levels and lower levels of HF-HRV and RMSSD. These patients also had a significantly longer length of stay in hospital (LOS) than patients receiving low-flow oxygen therapy. Men had higher plasma PCT levels and longer LOS in hospital than women, and PCT statistically explained (mediated) the association between sex and LOS. The present study showed different correlations of linear and non-linear vagal indexes of HRV and inflammatory markers in patients with Covid-19. Significant sex differences in certain inflammatory markers were also observed, which may very well verify previous findings of poor prognosis in men with Covid-19. HRV reflects a continuous interaction between the sympathetic and parasympathetic autonomic nervous systems, which are affected by mental or physical stress, and certain disease states. The increased sympathetic and decreased parasympathetic vagal tone contribute to a higher risk of diseases associated with inflammation, cardiovascular disease, cancer, pulmonary diseases and other pathologies, including infectious diseases such as Covid-19. The present study showed that higher RMSSD (a marker of vagal activity) in Covid-19 patients is associated with lower levels of inflammatory biomarkers, a lower need for treatment and is negatively correlated with intensive care unit admission, leading to a shorter hospital stay. These findings support the idea that activation of vagus nerve may help certain Covid-19 patients by reducing the cytokine storm and excessive inflammation.

Effects of COVID-19 on the Autonomic Cardiovascular System: Heart Rate Variability and Turbulence in Recovered Patients

BACKGROUND

COVID-19 may be a risk factor for developing cardiovascular autonomic dysfunction. Data are limited, however, on the association between heart rate variability, heart rate turbulence, and COVID-19. The aims of this study were to evaluate the effect of COVID-19 on the cardiovascular autonomic system in patients with persistent symptoms after recovering from COVID-19 and to determine whether these patients showed changes in ambulatory electrocardiography monitoring.

METHODS

Fifty-one adults who had confirmed SARS-CoV-2 infection and presented with persistent symptoms to the cardiology outpatient clinic after clinical recovery between April and June 2021 were included. Patients were prospectively followed for 6 months. The patients were evaluated at the time of first application to the cardiology outpatient clinic and at 6 months after presentation. Ambulatory electrocardiography monitoring and echocardiographic findings were compared with a control group of 95 patients.

RESULTS

Patients in the post-COVID-19 group had significantly higher mean (SD) turbulence onset (0.39% [1.82%] vs -1.37% [2.93%]; P < .001) and lower heart rate variability than those in the control group at both initial and 6-month evaluations. The post-COVID-19 group had no significant differences in echocardiographic findings compared with the control group at 6 months, except for right ventricle late diastolic mitral annular velocity (P = .034). Furthermore, turbulence onset was significantly correlated with turbulence slope (r = -0.232; P = .004), heart rate variability, and the parameters of left (r = -0.194; P=.049) and right (r = 0.225; P = .02) ventricular diastolic function.

CONCLUSIONS

COVID-19 may cause cardiovascular autonomic dysfunction. Heart rate variability and turbulence parameters can be used to recognize cardiovascular autonomic dysfunction in patients who have recovered from COVID-19 but have persistent symptoms.

The validity and applications of the analgesia nociception index: a narrative review

Pain refers to the subjective, unpleasant experience that is related to illness or injury. In contrast to pain, nociception refers to the physiological neural processing of noxious stimuli, such as intra-operative surgical stimuli. One novel device, the Analgesia Nociception Index (ANI), aims to objectively measure intra-operative nociception by analyzing the heart rate variability in patients undergoing surgery. Through this method of nociceptive monitoring, the ANI device aims to provide an objective, continuous evaluation of patient comfort levels and allow anesthesiologists to better manage surgical stress and patient analgesia, perhaps with even better efficacy than current practices used to assess nociception. Additionally, ANI may have clinical application in settings outside of the operating room, such as in the intensive care unit. In this narrative review, we compiled and summarized the findings of many studies that have investigated ANI's validity and applications in different clinical settings. Currently, the literature appears mostly supportive of ANI's ability to detect nociception in both surgical and non-surgical settings. However, the ability for ANI to provide clinical benefits, such as decreased intra-operative opioid use, post-operative opioid use, and post-operative pain compared to standard practices appear controversial. Because of the wide variety of methodology, clinical settings, patient populations, and limitations in these studies, more investigation of ANI is needed before any firm conclusions can be drawn on its clinical benefits.

Effect of COVID-19 on Stress and Biomarkers: An Exploratory Cross-Sectional Study

Background Anxiety and stress in COVID-19 lead to continual pro-inflammatory cytokine activity resulting in excessive inflammation. Levels of different bio indices of COVID-19 may predict clinical outcomes and the severity of COVID-19 disease and may correlate with anxiety and stress levels. Objectives To measure the level of anxiety in COVID-19 patients using the coronavirus anxiety scale (CAS) as an assessment of psychological stress. To measure the levels of blood biomarkers and biochemical and hematological markers of inflammation in COVID-19. To record and measure the indices of short-term HRV in COVID-19 patients to assess their physiological and psychological stress levels. To determine the relationship between anxiety scores, levels of laboratory indices (blood biomarkers), and HRV parameters across mild, moderate and severe cases of COVID-19. Material and method A total of 300 COVID-19 patients aged between 18 and 55 years were included. A questionnaire-based CAS was used to assess anxiety levels. Short-term HRV was recorded to measure stress. Blood biomarkers: Biochemical and hemato-cytological markers of inflammation were measured. Statistical analyses were performed using the SPSS software version 20.0. Results Anxiety and stress increased with the severity of COVID-19. A positive correlation was detected between anxiety and serum ferritin, IL-6, MCV, and MCH levels, and a negative correlation between the corona anxiety score and RBC count. The increase in the severity of COVID-19 showed elevated levels of WBC count, neutrophil%, platelet count, neutrophil/lymphocyte ratio, serum ferritin, D-dimer, C-reactive protein, procalcitonin, interleukin-6, and lactate dehydrogenase, and decreased lymphocyte and monocyte percentages. The increase in the severity of COVID-19 decreased lymphocyte, monocyte, and eosinophil counts. Conclusion The Corona Anxiety Scale and heart rate variability can be used as complementary tools to index COVID-19-related anxiety and stress. An association exists between immune dysregulation and heart rate variability, which can be used to predict the inflammatory response and prognosis of COVID-19.

Analgesia nociception index and high frequency variability index: promising indicators of relative parasympathetic tone

At present, there is no objective and absolute measure of nociception, although various monitoring techniques have been developed. One such technique is the Analgesia Nociception Index (ANI), which is calculated from heart rate variability that reflects the relative parasympathetic tone. ANI is expressed on a non-unit scale of 0-100 (100 indicates maximal relative parasympathetic tone). Several studies indicated that ANI-guided anesthesia may help reduce intraoperative opioid use. The usefulness of ANI in the intensive care unit (ICU) and during surgery has also been reported. However, some limitations of ANI have also been reported; for example, ANI is affected by emotions and some drugs. In 2022, a high frequency variability index (HFVI), which was renamed from ANI and uses the same algorithm as ANI, was commercialized; therefore, ANI/HFVI are currently in the spotlight. Unlike ANI, HFVI can be displayed along with other biometric information on the Root® monitor. ANI/HFVI monitoring may affect the prognosis of not only patients in the perioperative period but those in ICU, those who receive home medical care, or outpatients. In this article, we present an updated review on ANI that has been published in the last decade, introduce HFVI, and discuss the outlooks of ANI/HFVI.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

The Impact of SARS-CoV-2 Infection on Heart Rate Variability: A Systematic Review of Observational Studies with Control Groups

Autonomic nervous system (ANS) dysfunction can arise after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and heart rate variability (HRV) tests can assess its integrity. This review investigated the relationship between the impact of SARS-CoV-2 infection on HRV parameters. Comprehensive searches were conducted in four electronic databases. Observational studies with a control group reporting the direct impact of SARS-CoV-2 infection on the HRV parameters in July 2022 were included. A total of 17 observational studies were included in this review. The square root of the mean squared differences of successive NN intervals (RMSSD) was the most frequently investigated. Some studies found that decreases in RMSSD and high frequency (HF) power were associated with SARS-CoV-2 infection or the poor prognosis of COVID-19. Also, decreases in RMSSD and increases in the normalized unit of HF power were related to death in critically ill COVID-19 patients. The findings showed that SARS-CoV-2 infection, and the severity and prognosis of COVID-19, are likely to be reflected in some HRV-related parameters. However, the considerable heterogeneity of the included studies was highlighted. The methodological quality of the included observational studies was not optimal. The findings suggest rigorous and accurate measurements of HRV parameters are highly needed on this topic.

Cardiac Autonomic Function in Long COVID-19 Using Heart Rate Variability: An Observational Cross-Sectional Study

BACKGROUND

Heart rate variability is a non-invasive, measurable, and established autonomic nervous system test. Long-term COVID-19 sequelae are unclear; however, acute symptoms have been studied.

OBJECTIVES

To determine autonomic cardiac differences between long COVID-19 patients and healthy controls and evaluate associations among symptoms, comorbidities, and laboratory findings.

METHODS

This single-center study included long COVID-19 patients and healthy controls. The heart rate variability (HRV), a quantitative marker of autonomic activity, was monitored for 24 h using an ambulatory electrocardiogram system. HRV indices were compared between case and control groups. Symptom frequency and inflammatory markers were evaluated. A significant statistical level of 5% (p-value 0.05) was adopted.

RESULTS

A total of 47 long COVID-19 patients were compared to 42 healthy controls. Patients averaged 43.8 (SD14.8) years old, and 60.3% were female. In total, 52.5% of patients had moderate illness. Post-exercise dyspnea was most common (71.6%), and 53.2% lacked comorbidities. CNP, D-dimer, and CRP levels were elevated (p-values of 0.0098, 0.0023, and 0.0015, respectively). The control group had greater SDNN24 and SDANNI (OR = 0.98 (0.97 to 0.99; p = 0.01)). Increased low-frequency (LF) indices in COVID-19 patients (OR = 1.002 (1.0001 to 1.004; p = 0.030)) and high-frequency (HF) indices in the control group (OR = 0.987 (0.98 to 0.995; p = 0.001)) were also associated.

CONCLUSIONS

Patients with long COVID-19 had lower HF values than healthy individuals. These variations are associated with increased parasympathetic activity, which may be related to long COVID-19 symptoms and inflammatory laboratory findings.

Prospective multicenter study of heart rate variability with ANI monitor as predictor of mortality in critically ill patients with COVID-19

The purpose of this study is to demonstrate that the most critically ill patients with COVID-19 have greater autonomic nervous system dysregulation and assessing the heart rate variability, allows us to predict severity and 30-day mortality. This was a multicentre, prospective, cohort study. Patients were divided into two groups depending on the 30-day mortality. The heart rate variability and more specifically the relative parasympathetic activity (ANIm), and the SDNN (Energy), were measured. To predict severity and mortality multivariate analyses of ANIm, Energy, SOFA score, and RASS scales were conducted. 112 patients were collected, the survival group (n = 55) and the deceased group (n = 57). The ANIm value was higher (p = 0.013) and the Energy was lower in the deceased group (p = 0.001); Higher Energy was correlated with higher survival days (p = 0.009), and a limit value of 0.31 s predicted mortalities with a sensitivity of 71.9% and a specificity of 74.5%. Autonomic nervous system and heart rate variability monitoring in critically ill patients with COVID-19 allows for predicting survival days and 30-day mortality through the Energy value. Those patients with greater severity and mortality showed higher sympathetic depletion with a predominance of relative parasympathetic activity.

Predicting the outcome for COVID-19 patients by applying time series classification to electronic health records

Background

COVID-19 caused more than 622 thousand deaths in Brazil. The infection can be asymptomatic and cause mild symptoms, but it also can evolve into a severe disease and lead to death. It is difficult to predict which patients will develop severe disease. There are, in the literature, machine learning models capable of assisting diagnose and predicting outcomes for several diseases, but usually these models require laboratory tests and/or imaging.

Methods

We conducted a observational cohort study that evaluated vital signs and measurements from patients who were admitted to Hospital das Clínicas (São Paulo, Brazil) between March 2020 and October 2021 due to COVID-19. The data was then represented as univariate and multivariate time series, that were used to train and test machine learning models capable of predicting a patient’s outcome.

Results

Time series-based machine learning models are capable of predicting a COVID-19 patient’s outcome with up to 96% general accuracy and 81% accuracy considering only the first hospitalization day. The models can reach up to 99% sensitivity (discharge prediction) and up to 91% specificity (death prediction).

Conclusions

Results indicate that time series-based machine learning models combined with easily obtainable data can predict COVID-19 outcomes and support clinical decisions. With further research, these models can potentially help doctors diagnose other diseases.

Autonomic dysfunction in non-critically ill COVID-19 patients during the acute phase of disease: an observational, cross-sectional study

Introduction

Evidence is emerging about an extra-pulmonary involvement of SARS-CoV-2, including the nervous system. Autonomic dysfunction in patients recovering from acute coronavirus disease 2019 (COVID-19) has been recently described. Dysautonomic symptoms have been reported in the acute phase of the disease, but clear evidence is lacking, especially in the non-critical forms of the infection.

Objective

The aim of this study is to assess the prevalence of dysautonomia in acute, non-critically ill COVID-19 patients.

Methods

In this observational, cross-sectional study, we compared 38 non-critically ill patients with acute COVID-19 (COVID + group) to 38 healthy volunteers (COVID − group) in order to assess the prevalence of signs and symptoms of dysautonomia through the administration of the composite autonomic symptom score 31 (COMPASS-31) and an active standing test. Comparisons between groups were performed by means of both univariate and multivariate analyses.

Results

The prevalence of orthostatic hypotension was significantly higher in the COVID + group. Higher total scores of COMPASS-31 were observed in the COVID + group than controls. Significant differences between groups emerged in the secretomotor, orthostatic intolerance, and gastrointestinal COMPASS-31 domains. All these results maintained the statistical significance after the adjustment for concomitant drugs with a known effect on the autonomic nervous system assumed by the study participants, except for the differences in the gastrointestinal domain of COMPASS-31.

Conclusion

Our results suggest that an autonomic dysfunction could be an early manifestation of COVID-19, even in the contest of mild forms of the infection.

Potential autonomic nervous system dysfunction in COVID-19 patients detected by heart rate variability is a sign of SARS-CoV-2 neurotropic features

Increasing evidence strongly support that the newly identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to the development of COVID-19-associated central nervous system (CNS) manifestations. The presence of SARS-CoV-2 viral protein in the brainstem, which includes cardiovascular control centers, has been documented previously. Given the changes in autonomic nervous system function evaluated by heart rate variability (HRV) metrics, which are observed even prior to clinical signs, the potential effect of SARS-CoV-2 on the autonomic nervous system (ANS) center is likely. The integral parts of the brain renin-angiotensin system, as ACE2 enzyme, are highly expressed in the brainstem, which may also be involved in baroreflex sensitivity, playing an important role in HRV. SARS-CoV-2 may bind to ACE2 in order to enter the host brainstem cell and change baroreflex sensitivity due to the altered ratio of the concentration of angiotensin II (Ag II) to angiotensin (1-7). In this article, we discussed the information on the possibility that the SARS-CoV-2 viral particle by disrupting the homeostasis of the brain renin-angiotensin system even without brainstem neuropathological changes, may affect the function of the ANS center in the brainstem. SARS-CoV-2 could influence ANS function before affecting the immune system. It is possible that the altered HRV parameters imply the potential neurotropic characteristics of SARS-CoV-2. Therefore, this potential feature should be taken into account in diagnostic and therapeutic approaches for COVID-19 patients.

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers

Objective

To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

Materials and Methods

Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app.

Results

We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age.

Discussion

We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection.

Conclusion

Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

Heart rate variability comparison between young males after 4-6 weeks from the end of SARS-CoV-2 infection and controls

Due to the prolonged inflammatory process induced by infection of the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), indices of autonomic nervous system dysfunction may persist long after viral shedding. Previous studies showed significant changes in HRV parameters in severe (including fatal) infection of SARS-CoV-2. However, few studies have comprehensively examined HRV in individuals who previously presented as asymptomatic or mildly symptomatic cases of COVID-19. In this study, we examined HRV in asymptomatic or mildly symptomatic individuals 5–7 weeks following positive confirmation of SARS-CoV-2 infection. Sixty-five ECG Holter recordings from young (mean age 22.6 ± 3.4 years), physically fit male subjects 4–6 weeks after the second negative test (considered to be the start of recovery) and twenty-six control male subjects (mean age 23.2 ± 2.9 years) were considered in the study. Night-time RR time series were extracted from ECG signals. Selected linear as well as nonlinear HRV parameters were calculated. We found significant differences in Porta’s symbolic analysis parameters V0 and V2 (p < 0.001), α₂ (p < 0.001), very low-frequency component (VLF; p = 0.022) and respiratory peak (from the PRSA method; p = 0.012). These differences may be caused by the changes of activity of the parasympathetic autonomic nervous system as well as by the coupling of respiratory rhythm with heart rate due to an increase in pulmonary arterial vascular resistance. The results suggest that the differences with the control group in the HRV parameters, that reflect the functional state of the autonomic nervous system, are measurable after a few weeks from the beginning of the recovery even in the post-COVID group—a young and physically active population. We indicate HRV sensitive markers which may be used in long-term monitoring of patients after recovery.

Heart rate variability and cardiac autonomic functions in post-COVID period

Background

The heart rate variability (HRV) is a non-invasive, objective and validated method for the assessment of autonomic nervous system. Although acute manifestations of COVID-19 were widely researched, long-term sequela of COVID-19 are still unknown. This study aimed to analyze autonomic function using HRV indices in the post-COVID period that may have a potential to enlighten symptoms of COVID long-haulers.

Methods

The 24-h ambulatory electrocardiography (ECG) recordings obtained >12 weeks after the diagnosis of COVID-19 were compared with age–gender-matched healthy controls. Patients who used drugs or had comorbidities that affect HRV and who were hospitalized with severe COVID-19 were excluded from the study.

Results

Time domain indices of HRV analysis (standard deviation of normal RR intervals in 24 h (SDNN 24 h) and root mean square of successive RR interval differences (RMSSD)) were significantly higher in post-COVID patients (p < 0.05 for all). Among frequency domain indices, high frequency and low frequency/high frequency ratio was significantly higher in post-COVID patients (p = 0.037 and p = 0.010, respectively). SDNN >60 ms [36 (60.0%) vs. 12 (36.4%), p = 0.028)] and RMSSD >40 ms [31 (51.7%) vs. 7 (21.2%), p = 0.003)] were more prevalent in post-COVID patients. Logistic regression models were created to evaluate parasympathetic overtone in terms of SDNN >60 ms and RMSSD >40 ms. After covariate adjustment, post-COVID patients were more likely to have SDNN >60 msn (OR: 2.4, 95% CI:1.2–12.8) and RMSSD >40 ms (OR: 2.5, 95% CI: 1.4–9.2).

Conclusion

This study revealed parasympathetic overtone and increased HRV in patients with history of COVID-19. This may explain the unresolved orthostatic symptoms occurring in post-COVID period which may be associated with autonomic imbalance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10840-022-01138-8.

Instrumental Evaluation of COVID-19 Related Dysautonomia in Non-Critically-Ill Patients: An Observational, Cross-Sectional Study

Coronavirus disease-19 (COVID-19) is a predominantly respiratory syndrome. Growing reports about a SARS-CoV-2 neurological involvement, including autonomic dysfunction (AD), have been reported, mostly in critically-ill patients, or in the long-COVID syndrome. In this observational, cross-sectional study, we investigated the prevalence of AD in 20 non-critically-ill COVID-19 patients (COVID+ group) in the acute phase of the disease through a composite instrumental evaluation consisting of Sudoscan, automated pupillometry, heart rate variability (HRV), and pulse transit time (PTT). All the parameters were compared to a control group of 20 healthy volunteers (COVID− group). COVID+ group presented higher values of pupillary dilatation velocities, and baseline pupil diameter than COVID− subjects. Moreover, COVID+ patients presented a higher incidence of feet sudomotor dysfunction than COVID− group. No significant differences emerged in HRV and PTT parameters between groups. In this study we observed the occurrence of autonomic dysfunction in the early stage of the disease.

Heart-rate-variability (HRV), predicts outcomes in COVID-19

BACKGROUND

Patients with COVID-19 present with a variety of clinical manifestations, ranging from mild or asymptomatic disease to severe illness and death. Whilst previous studies have clarified these and several other aspects of COVID-19, one of the ongoing challenges regarding COVID-19 is to determine which patients are at risk of adverse outcomes of COVID-19 infection. It is hypothesized that this is the result of insufficient inhibition of the immune response, with the vagus nerve being an important neuro-immuno-modulator of inflammation. Vagus nerve activity can be non-invasively indexed by heart-rate-variability (HRV). Therefore, we aimed to assess the prognostic value of HRV, as a surrogate marker for vagus nerve activity, in predicting mortality and intensive care unit (ICU) referral, in patients hospitalized with COVID-19.

METHODS

A retrospective cohort study including all consecutive patients (n = 271) diagnosed and hospitalized with COVID-19 between March 2020 and May 2020, without a history of cardiac arrhythmias (including atrial and ventricular premature contractions), pacemaker, or current bradycardia (heart rate <50 bpm) or tachycardia (heart rate >110 bpm). HRV was based on one 10s ECG recorded at admission. 3-week survival and ICU referral were examined.

RESULTS

HRV indexed as standard deviation of normal to normal heartbeat intervals (SDNN) predicted survival (H.R. = 0.53 95%CI: 0.31-0.92). This protective role was observed only in patients aged 70 years and older, not in younger patients. HRV below median value also predicted ICU referral within the first week of hospitalization (H.R = 0.51, 95%CI: 0.29-0.90, P = 0.021).

CONCLUSION

Higher HRV predicts greater chances of survival, especially in patients aged 70 years and older with COVID-19, independent of major prognostic factors. Low HRV predicts ICU indication and admission in the first week after hospitalization.

Heart rate variability follow-up during COVID-19: Case Report

Background

To detect an early increase in the inflammatory response might prove to be vital for mitigating the deleterious effects of the disease over time.

Case

A 52-year-old obese man with moderate asthma and hypertension, who developed COVID-19 and had moderate symptoms, used a wearable device to record heart rate variability (HRV) during his illness. He had low parasympathetic tone, which decreased daily until it reached almost 2 standard deviations (SD) below normal values at the end of the second week. His sympathetic tone increased from > 3 SD to > 5 SD.

Conclusions

These findings suggest an altered modulation of the sympathetic and parasympathetic nervous systems in COVID-19, such that the sympathetic tone is augmented and the parasympathetic tone is reduced. Population norms of COVID-19 infections should be further studied over the short-term and using 24 h HRV measurements.

Auricular Neuromodulation for Mass Vagus Nerve Stimulation: Insights From SOS COVID-19 a Multicentric, Randomized, Controlled, Double-Blind French Pilot Study

Importance: An exacerbated inflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is believed to be one of the major causes of the morbidity and mortality of the coronavirus disease 2019 (COVID-19). Neuromodulation therapy, based on vagus nerve stimulation, was recently hypothesized to control both the SARS-CoV-2 replication and the ensuing inflammation likely through the inhibition of the nuclear factor kappa-light-chain-enhancer of activated B cells pathway and could improve the clinical outcomes as an adjunct treatment. We proposed to test it by the stimulation of the auricular branch of the vagus nerve, i.e., auricular neuromodulation (AN), a non-invasive procedure through the insertion of semipermanent needles on the ears.

Objective: The aim of this study was to assess the effect of AN on the clinical outcomes in patients affected by COVID-19.

Design, Setting, and Participants: A multicenter, randomized, placebo-controlled, double-blind clinical trial included 31 patients with respiratory failure due to COVID-19 requiring hospitalization. Within 72 h after admission, patients received either AN (n = 14) or sham neuromodulation (SN, n = 15) in addition to the conventional treatments.

Main Outcome and Measures: The primary endpoint of the study was the rate of a clinical benefit conferred by AN at Day 14 (D14) as assessed by a 7-point Clinical Progression Scale. The secondary endpoint of the study was the impact of AN on the rate of transfer to the intensive care unit (ICU) and on the survival rate at D14.

Results: The AN procedure was well-tolerated without any reported side effects but with no significant improvement for the measures of both primary (p > 0.3) and secondary (p > 0.05) endpoints at the interim analysis. None of the AN-treated patients died but one in the SN group did (81 years). Two AN-treated patients (73 and 79 years, respectively) and one SN-treated patient (59 years) were transferred to ICU. Remarkably, AN-treated patients were older with more representation by males than in the SN arm (i.e., the median age of 75 vs. 65 years, 79% male vs. 47%).

Conclusion: The AN procedure, which was used within 72 h after the admission of patients with COVID-19, was safe and could be successfully implemented during the first two waves of COVID-19 in France. Nevertheless, AN did not significantly improve the outcome of the patients in our small preliminary study. It is pertinent to explore further to validate AN as the non-invasive mass vagal stimulation solution for the forthcoming pandemics.

Clinical Trial Registration: [https://clinicaltrials.gov/], identifier [NCT04341415].