The influence of anesthesia on heart rate complexity during elective and urgent surgery in 128 patients

Different acquisition systems for heart rate variability analysis may lead to diverse outcomes

Heart rate variability (HRV) is a relevant physiological variable for the estimation of cardiac autonomic function. Although the gold standard for HRV registration is the electrocardiogram (ECG), several applications (APPs) have been increasingly developed. The evaluation carried out by these devices must be compatible with ECG standards. The aim of this study was to compare the data obtained simultaneously with ECG and APP with chest heart rate transmitters. Fifty-six healthy individuals (28 men and 28 women) were evaluated at rest through a short simultaneous HRV measurement with both devices. Data from both acquisition systems were analyzed separately using their own analysis software and exported and analyzed using a validated software. Signal recordings were compatible between the two acquisition systems (Pearson r=0.99; P<0.0001). Although a high correlation was found for the HRV variables obtained in the time domain (Spearman r=0.99; P<0.0001), the correlation decreased in the frequency domain (Pearson r=0.85; P<0.0001) when two software programs were used. Comparison of the averages of spectral analysis parameters also showed differences when HRV data were analyzed separately in each device for low-frequency (LF) and high-frequency (HF) bands. Although the portability of these mobile devices allows for optimal HRV evaluation, the direct analysis obtained from these devices must be carefully evaluated with respect to frequency domain parameters.

Heart rate complexity: An early prognostic marker of patient outcome after cardiac arrest

OBJECTIVE

Early prognostication in comatose patients after cardiac arrest (CA) is difficult but essential to inform relatives and optimize treatment. Here we investigate the predictive value of heart-rate variability captured by multiscale entropy (MSE) for long-term outcomes in comatose patients during the first 24 hours after CA.

METHODS

In this retrospective analysis of prospective multi-centric cohort, we analyzed MSE of the heart rate in 79 comatose patients after CA while undergoing targeted temperature management and sedation during the first day of coma. From the MSE, two complexity indices were derived by summing values over short and long time scales (CI_s and CI_l). We splitted the data in training and test datasets for analysing the predictive value for patient outcomes (defined as best cerebral performance category within 3 months) of CI_s and CI_l.

RESULTS

Across the whole dataset, CI_l provided the best sensitivity, specificity, and accuracy (88%, 75%, and 82%, respectively). Positive and negative predictive power were 81% and 84%.

CONCLUSIONS

Characterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes.

SIGNIFICANCE

The analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.

Heart Rate Complexity in US Army Forward Surgical Teams During Pre Deployment Training

INTRODUCTION

For trauma triage, the US Army has developed a portable heart rate complexity (HRC) monitor, which estimates cardiac autonomic input and the activity of the hypothalamic-pituitary-adrenal (HPA) axis. We hypothesize that autonomic/HPA stress associated with predeployment training in U.S. Army Forward Surgical Teams will cause changes in HRC.

MATERIALS AND METHODS

A prospective observational study was conducted in 80 soldiers and 10 civilians at the U.S. Army Trauma Training Detachment. Heart rate (HR, b/min), cardiac output (CO, L/min), HR variability (HRV, ms), and HRC (Sample Entropy, unitless), were measured using a portable non-invasive hemodynamic monitor during postural changes, a mass casualty (MASCAL) situational training exercise (STX) using live tissue, a mock trauma (MT) STX using moulaged humans, and/or physical exercise.

RESULTS

Baseline HR, CO, HRV, and HRC averaged 72 ± 11b/min, 5.6 ± 1.2 L/min, 48 ± 24 ms, and 1.9 ± 0.5 (unitless), respectively. Supine to sitting to standing caused minimal changes. Before the MASCAL or MT, HR and CO both increased to ~125% baseline, whereas HRV and HRC both decreased to ~75% baseline. Those values all changed an additional ~5% during the MASCAL, but an additional 10 to 30% during the MT. With physical exercise, HR and CO increased to >200% baseline, while HRV and HRC both decreased to 40 to 60% baseline; these changes were comparable to those caused by the MT. All the changes were P < 0.05.

CONCLUSIONS

Various forms of HPA stress during Forward Surgical Team STXs can be objectively quantitated continuously in real time with a portable non-invasive monitor. Differences from resting baseline indicate stress anticipating an impending STX whereas differences between average and peak responses indicate the relative stress between STXs. Monitoring HRC could prove useful to field commanders to rapidly and objectively assess the readiness status of troops during STXs or repeated operational missions. In the future, health care systems and regulatory bodies will likely be held accountable for stress in their trainees and/or obliged to develop wellness options and standardize efforts to ameliorate burnout, so HRC metrics might have a role, as well.

Incidence of post-anesthetic respiratory complications in pediatrics. Observational, single-center study in Medellin, Colombia

Introduction: Post-anesthetic complications, particularly respiratory complications, continue to be a source of concern due to their high frequency, particularly in pediatrics. Objective: To describe the incidence of respiratory complications in the post-anesthesia care unit of an intermediate complexity center during a six-month period, and to explore the variables associated with major respiratory complications. Materials and Methods: Retrospective cohort study based on clinical record reviews. The records of the post-anesthesia care unit of an intermediate complexity pediatric institution located in Medellin, Colombia, were reviewed. This center uses a nursing-based care model that includes patient extubation in the post-anesthesia care unit. Results: The records of 1181 patients were analyzed. The cumulative incidences of major complications were bronchospasm 1.44%, laryngospasm 0.68% and respiratory depression 0.59%. There were no cases of cardiac arrest or acute pulmonary edema. A history of respiratory infection less than 15 days before the procedure, rhinitis and female sex were associated with major respiratory complications. Conclusions: A low frequency of respiratory complications was found during care provided by nursing staff trained in anesthesia recovery and pediatric airway in the post-anesthesia care unit.

A Heartbeat Away From Consciousness: Heart Rate Variability Entropy Can Discriminate Disorders of Consciousness and Is Correlated With Resting-State fMRI Brain Connectivity of the Central Autonomic Network

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.