Prognostic value of variables derived from heart rate variability in patients with traumatic brain injury after decompressive surgery

Heart rate variability (HRV) after traumatic brain injury (TBI): a scoping review

BACKGROUND

Heart rate variability (HRV), defined as the variability between successive heart beats, is a noninvasive measure of autonomic nervous system (ANS) function, which may be altered following traumatic brain injury (TBI). This scoping review summarizes the existing literature regarding changes in HRV after TBI as well as the association between measures of HRV and outcomes following TBI.

METHODS

A literature search for articles assessing 'heart rate variability' and 'brain injury' or 'concussion' was completed. Articles were included if HRV was measured in human subjects with TBI or concussion. Review articles, protocol papers, and studies including non-traumatic injuries were excluded.

RESULTS

Sixty-three articles were included in this review. Varied methods were used to measure HRV in the different studies. Forty articles included information about differences in HRV measures after TBI and/or longitudinal changes after TBI. Fifteen studies assessed HRV and symptoms following TBI, and 15 studies assessed HRV and either functional or cognitive outcomes after TBI.

CONCLUSIONS

HRV has been studied in the context of mortality, clinical symptoms, and medical, functional, or cognitive outcomes following TBI. Methods used to measure HRV have varied amongst the different studies, which may impact findings, standardized protocols are needed for future research.

Synergistic dynamics of heart rate variability and systolic blood pressure revealed by dual Poincaré plot analysis

Cardiac arrest (CA) survivors often suffer secondary brain injury due to impaired autonomic nervous system (reflected by heart rate HR) and hemodynamic function (reflected by blood pressure BP and baroreflex regulation). This study proposes a Dual Poincaré Plot, a novel method analyzing coupled variability in HR - BP dynamics to assess these impairments. Ten rats were categorized into good and poor neurological outcome groups in a rat model of CA. Dual Poincaré plot analysis, squared partial directed coherence, and sequence method were used to characterize the outcome. Results revealed differences in variability of HR, BP, and baroreflex, as well as coupling strength, between groups. Good outcome subjects exhibited 1) increased variability in both BP and HR, 2) enhanced baroreflex regulation, and 3) weakened cardiovascular coupling. Conversely, subjects with poor outcomes displayed 1) decreased HR-BP variability, 2) impaired baroreflex, and 3) stronger coupling. The Baroreflex Index from the Dual Poincaré plot showed a high correlation with the traditional sequence method (R2 = 0.91). These results imply that Dual Poincaré offers a real-time assessment of autonomic-hemodynamic interaction, effectively stratifying post-CA neurological recovery and potentially enhancing prognostic accuracy for timely interventions.

Neuromonitoring in the ICU - what, how and why?

PURPOSE OF REVIEW

We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury.

RECENT FINDINGS

Noninvasive intracranial pressure evaluation with optic nerve sheath diameter measurements, transcranial Doppler waveform analysis, or skull mechanical extensometer waveform recordings have potential safety and resource-intensity advantages when compared to standard invasive monitors, however each of these techniques has limitations. Quantitative electroencephalography can be applied for detection of cerebral ischemia and states of covert consciousness. Near-infrared spectroscopy may be leveraged for cerebral oxygenation and autoregulation computation. Automated quantitative pupillometry and heart rate variability analysis have been shown to have diagnostic and/or prognostic significance in selected subtypes of acute brain injury. Finally, artificial intelligence is likely to transform interpretation and deployment of neuromonitoring paradigms individually and when integrated in multimodal paradigms.

SUMMARY

The ability to detect brain dysfunction and injury in critically ill patients is being enriched thanks to remarkable advances in neuromonitoring data acquisition and analysis. Studies are needed to validate the accuracy and reliability of these new approaches, and their feasibility and implementation within existing intensive care workflows.

Central autonomic network and early prognosis in patients with disorders of consciousness

The central autonomic network (CAN) plays a crucial role in modulating the autonomic nervous system. Heart rate variability (HRV) is a valuable marker for assessing CAN function in disorders of consciousness (DOC) patients. We used HRV analysis for early prognosis in 58 DOC patients enrolled within ten days of hospitalization. They underwent a five-minute electrocardiogram during baseline and acoustic/visual stimulation. The coma recovery scale-revised (CRS-R) was used to define the patient's consciousness level and categorize the good/bad outcome at three months. The high-frequency Power Spectrum Density and the standard deviation of normal-to-normal peaks in baseline, the sample entropy during the stimulation, and the time from injury features were used in the support vector machine analysis (SVM) for outcome prediction. The SVM predicted the patients' outcome with an accuracy of 96% in the training test and 100% in the validation test, underscoring its potential to provide crucial clinical information about prognosis.

Development and Application of a Novel Pressure System for Evaluating Trauma Severities Using a Physiological Approach After Traumatic Brain Injury in Rats

OBJECTIVE

The fluid percussion injury (FPI) model is a surgical method for mimicking traumatic brain injury (TBI) models as it automatically and accurately measures peak impact pressure. Nevertheless, its elevated costs have led numerous researchers to develop more inexpensive alternative methods. Therefore, we used a copy of the classic FPI device to develop a novel method to evaluate the pressure pulse and determine injury severity with even more precision during the surgical procedure to induce an injury.

METHODS

The electronic components, algorithms, and hardware assembly were initially studied. Adult male Wistar rats received 2 different impact forces, and our novel system measured the pressure pulse in atmospheres to verify the differences between mild and moderate severity and the physiological alterations.

RESULTS

The newly developed system was capable of detecting differences between mild and moderate severity, and severity parameters (e.g., apnea and unconsciousness) were more significant in animals with more moderate FPI than those with mild FPI. Additionally, electrocardiographic signals were modified 1 day after TBI, and mild and moderate FPI decreased R-wave peak to R-wave peak intervals (increased heart rate) and high frequency (HF) index as well as increased low frequency (LF) and low frequency/high frequency ratio indices. All electrocardiographic parameters evaluated were more expressive in the more moderate FPI than in the mild one, corroborating clinical heart impairments after TBI.

CONCLUSIONS

The method developed to evaluate pressure pulse in an FPI model proved capable of precisely determining different degrees of injury.

Electrocardiogram monitoring as a predictor of neurological and survival outcomes in patients with out-of-hospital cardiac arrest: a single-center retrospective observational study

Introduction

This study hypothesized that monitoring electrocardiogram (ECG) waveforms in patients with out-of-hospital cardiac arrest (OHCA) could have predictive value for survival or neurological outcomes. We aimed to establish a new prognostication model based on the single variable of monitoring ECG waveforms in patients with OHCA using machine learning (ML) techniques.

Methods

This observational retrospective study included successfully resuscitated patients with OHCA aged ≥ 18 years admitted to an intensive care unit in Japan between April 2010 and April 2020. Waveforms from ECG monitoring for 1 h after admission were obtained from medical records and examined. Based on the open-access PTB-XL dataset, a large publicly available 12-lead ECG waveform dataset, we built an ML-supported premodel that transformed the II-lead waveforms of the monitoring ECG into diagnostic labels. The ECG diagnostic labels of the patients in this study were analyzed for prognosis using another model supported by ML. The endpoints were favorable neurological outcomes (cerebral performance category 1 or 2) and survival to hospital discharge.

Results

In total, 590 patients with OHCA were included in this study and randomly divided into 3 groups (training set, n = 283; validation set, n = 70; and test set, n = 237). In the test set, our ML model predicted neurological and survival outcomes, with the highest areas under the receiver operating characteristic curves of 0.688 (95% CI: 0.682-0.694) and 0.684 (95% CI: 0.680-0.689), respectively.

Conclusion

Our ML predictive model showed that monitoring ECG waveforms soon after resuscitation could predict neurological and survival outcomes in patients with OHCA.

Association of Depressive and Somatic Symptoms with Heart Rate Variability in Patients with Traumatic Brain Injury

Depressive and somatic symptoms are common after traumatic brain injury (TBI). Depression after TBI can relate to worsened cognitive functioning, functional impairment, higher rates of suicide attempts, and larger health care costs. Heart rate variability (HRV) represents the activity of the autonomic nervous system (ANS), which regulates almost all vascular, visceral, and metabolic functions. Several studies show a correlation between HRV, depression, and somatic symptoms in other diseases. However, studies on autonomic dysfunction, depression, and somatic symptoms in TBI patients are lacking. This study investigated the association between reduced ANS function, depression, and somatic symptoms in TBI patients. We retrospectively recruited 136 TBI patients who underwent 24 h ambulatory Holter electrocardiography to measure autonomic dysfunction within 1 month of onset. Patients who used BDI and PHQ-15 to evaluate depressive and somatic symptoms were included. Using Pearson's correlation analysis and multiple linear regression, the association between HRV parameters and BDI and PHQ-15 was determined. The HRV parameters and BDI and PHQ-15 showed statistical significance. In addition, HRV was shown to be a significantly associated factor of BDI and PHQ-15. HRV was associated with depressive and somatic symptom severity in TBI patients. Additionally, autonomic dysfunction may serve as an associated factor of depressive and somatic symptoms in patients with TBI.