Autonomic nervous system monitoring in intensive care as a prognostic tool. Systematic review

Exploration of COVID-19 associated bradycardia using heart rate variability analysis in a case-control study of ARDS patients

BACKGROUND

Bradycardia and dysautonomia observed during SARS-Cov2 infection suggests involvement of the autonomic nervous system (ANS). Limited data exists on ANS dysregulation and its association with outcomes in patients with acute respiratory distress syndrome (ARDS) related to COVID-19 (C-ARDS) or other etiologies (NC-ARDS).

OBJECTIVES

We aimed to explore sympathovagal balance, assessed by heart rate variability (HRV), and its clinical prognostic value in C-ARDS compared with NC-ARDS.

METHODS

A single-center, prospective case-control study was conducted. Consecutive patients meeting ARDS criteria between 2020 and 2022 were included. HRV was assessed using 1-hour electrographic tracing during a stable, daytime period.

RESULTS

Twenty-four patients with C-ARDS and 19 with NC-ARDS were included. Age, sex and ARDS severity were similar between groups. The median heart rate was markedly lower in the C-ARDS group than in the NC-ARDS group (60 [53-72] versus 101 [91-112] bpm, p<.001). Most of HRV parameters were significantly increased in patients with C-ARDS. HRV correlated with heart rate only in patients with C-ARDS. A positive correlation was found between the low-to high-frequency ratio (LF/HF) and length of intensive care unit stay (r = 0.576, p<.001).

CONCLUSION

This study confirmed that C-ARDS was associated with marked bradycardia and severe ANS impairment, suggesting a sympathovagal imbalance with vagal overtone. Poor outcomes appeared to be more related to sympathetic rather than parasympathetic hyperactivation.

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

Introduction

The analysis of heart rate variability (HRV) has proven to be an important tool for the management of autonomous nerve system in both surgical and critically ill patients. We conducted this study to show the different spectral frequency and time domain parameters of HRV as a prospective predictor for critically ill patients, and in particular for COVID-19 patients who are on mechanical ventilation. The hypothesis is that most severely ill COVID-19 patients have a depletion of the sympathetic nervous system and a predominance of parasympathetic activity reflecting the remaining compensatory anti-inflammatory response.

Materials and methods

A single-center, prospective, observational pilot study which included COVID-19 patients admitted to the Surgical Intensive Care Unit was conducted. The normalized high-frequency component (HFnu), i.e. ANIm, and the standard deviation of RR intervals (SDNN), i.e. Energy, were recorded using the analgesia nociception index monitor (ANI). To estimate the severity and mortality we used the SOFA score and the date of discharge or date of death.

Results

A total of fourteen patients were finally included in the study. ANIm were higher in the non-survivor group (p = 0.003) and were correlated with higher IL-6 levels (p = 0.020). Energy was inversely correlated with SOFA (p = 0.039) and fewer survival days (p = 0.046). A limit value at 80 of ANIm, predicted mortalities with a sensitivity of 100% and specificity of 85.7%. In the case of Energy, a limit value of 0.41 ms predicted mortality with all predictive values of 71.4%.

Conclusion

A low autonomic nervous system activity, i.e. low SDNN or Energy, and a predominance of the parasympathetic system, i.e. low HFnu or ANIm, due to the sympathetic depletion in COVID-19 patients are associated with a worse prognosis, higher mortality, and higher IL-6 levels.

Heart rate variability as a marker of recovery from critical illness in children

Objectives

The purpose of this study was to Identify whether changes in heart rate variability (HRV) could be detected as critical illness resolves by comparing HRV from the time of pediatric intensive care unit (PICU) admission with HRV immediately prior to discharge. We also sought to demonstrate that HRV derived from electrocardiogram (ECG) data from bedside monitors can be calculated in critically-ill children using a real-time, streaming analytics platform.

Methods

This was a retrospective, observational pilot study of 17 children aged 0 to 18 years admitted to the PICU of a free-standing, academic children’s hospital. Three time-domain measures of HRV were calculated in real-time from bedside monitor ECG data and stored for analysis. Measures included: root mean square of successive differences between NN intervals (RMSSD), percent of successive NN interval differences above 50 ms (pNN50), and the standard deviation of NN intervals (SDNN).

Results

HRV values calculated from the first and last 24 hours of PICU stay were analyzed. Mixed effects models demonstrated that all three measures of HRV were significantly lower during the first 24 hours compared to the last 24 hours of PICU admission (p<0.001 for all three measures). In models exploring the relationship between time from admission and log HRV values, the predicted average HRV remained consistently higher in the last 24 hours of PICU stay compared to the first 24 hours.

Conclusion

HRV was significantly lower in the first 24 hours compared to the 24 hours preceding PICU discharge, after resolution of critical illness. This demonstrates that it is feasible to detect changes in HRV using an automated, streaming analytics platform. Continuous tracking of HRV may serve as a marker of recovery in critically ill children.