Cerebrovascular Signal Complexity Six Hours after Intensive Care Unit Admission Correlates with Outcome after Severe Traumatic Brain Injury

Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury

BACKGROUND

Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.

METHODS

We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.

RESULTS

The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.

CONCLUSIONS

The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Associations Between Intracranial Pressure Extremes and Continuous Metrics of Cerebrovascular Pressure Reactivity in Acute Traumatic Neural Injury: A Scoping Review

Cerebrovascular pressure reactivity plays a key role in maintaining constant cerebral blood flow. Unfortunately, this mechanism is often impaired in acute traumatic neural injury states, exposing the already injured brain to further pressure-passive insults. While there has been much work on the association between impaired cerebrovascular reactivity following moderate/severe traumatic brain injury (TBI) and worse long-term outcomes, there is yet to be a comprehensive review on the association between cerebrovascular pressure reactivity and intracranial pressure (ICP) extremes. Therefore, we conducted a systematic review of the literature for all studies presenting a quantifiable statistical association between a continuous measure of cerebrovascular pressure reactivity and ICP in a human TBI cohort. The methodology described in the Cochrane Handbook for Systematic Reviews was used. BIOSIS, Cochrane Library, EMBASE, Global Health, MEDLINE, and SCOPUS were all searched from their inceptions to March of 2023 for relevant articles. Full-length original works with a sample size of ≥10 patients with moderate/severe TBI were included in this review. Data were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A total of 16 articles were included in this review. Studies varied in population characteristics and statistical tests used. Five studies looked at transcranial Doppler-based indices and 13 looked at ICP-based indices. All but two studies were able to present a statistically significant association between cerebrovascular pressure reactivity and ICP. Based on the findings of this review, impaired reactivity seems to be associated with elevated ICP and reduced ICP waveform complexity. This relationship may allow for the calculation of patient-specific ICP thresholds, past which cerebrovascular reactivity becomes persistently deranged. However, further work is required to better understand this relationship and improve algorithmic derivation of such individualized ICP thresholds.

Predicting the Severity and Discharge Prognosis of Traumatic Brain Injury Based on Intracranial Pressure Data Using Machine Learning Algorithms

OBJECTIVE

This study aimed to explore the potential of employing machine learning algorithms based on intracranial pressure (ICP), ICP-derived parameters, and their complexity to predict the severity and short-term prognosis of traumatic brain injury (TBI).

METHODS

A single-center prospectively collected cohort of neurosurgical intensive care unit admissions was analyzed. We extracted ICP-related data within the first 6 hours and processed them using complex algorithms. To indicate TBI severity and short-term prognosis, Glasgow Coma Scale score on the first postoperative day and Glasgow Outcome Scale-Extended score at discharge were used as binary outcome variables. A univariate logistic regression model was developed to predict TBI severity using only mean ICP values. Subsequently, 3 multivariate Random Forest (RF) models were constructed using different combinations of mean and complexity metrics of ICP-related data. To avoid overfitting, five-fold cross-validations were performed. Finally, the best-performing multivariate RF model was used to predict patients' discharge Glasgow Outcome Scale-Extended score.

RESULTS

The logistic regression model exhibited limited predictive ability with an area under the curve (AUC) of 0.558. Among multivariate models, the RF model, combining the mean and complexity metrics of ICP-related data, achieved the most robust ability with an AUC of 0.815. Finally, in terms of predicting discharge Glasgow Outcome Scale-Extended score, this model had a consistent performance with an AUC of 0.822. Cross-validation analysis confirmed the performance.

CONCLUSIONS

This study demonstrates the clinical utility of the RF model, which integrates the mean and complexity metrics of ICP data, in accurately predicting the TBI severity and short-term prognosis.

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.

Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in intensive care units, but only a small part of the information available in the ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose using permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We analyzed the results of a pig experiment with sliding windows of 3600 samples and 1000 displacement samples, and estimated their respective PEs, their associated probability distributions, and the number of missing patterns (NMP). We observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, and normalized NMP is less than 90% and p(s1)>p(s720). Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion, the normalized NMP is higher than 95%, and PE is not sensitive to changes in ICP and p(s720)>p(s1). The results show that it could be used for real-time patient monitoring or as input for a machine learning tool.

Development of a Machine-Learning Model for Prediction of Extubation Failure in Patients with Difficult Airways after General Anesthesia of Head, Neck, and Maxillofacial Surgeries

(1) Background: Extubation failure after general anesthesia is significantly associated with morbidity and mortality. The risk of a difficult airway after the general anesthesia of head, neck, and maxillofacial surgeries is significantly higher than that after general surgery, increasing the incidence of extubation failure. This study aimed to develop a multivariable prediction model based on a supervised machine-learning algorithm to predict extubation failure in adult patients after head, neck, and maxillofacial surgeries. (2) Methods: A single-center retrospective study was conducted in adult patients who underwent head, neck, and maxillofacial general anesthesia between July 2015 and July 2022 at the Shanghai Ninth People's Hospital. The primary outcome was extubation failure after general anesthesia. The dataset was divided into training (70%) and final test sets (30%). A five-fold cross-validation was conducted in the training set to reduce bias caused by the randomly divided dataset. Clinical data related to extubation failure were collected and a stepwise logistic regression was performed to screen out the key features. Six machine-learning methods were introduced for modeling, including random forest (RF), k-nearest neighbor (KNN), logistic regression (LOG), support vector machine (SVM), extreme gradient boosting (XGB), and optical gradient boosting machine (GBM). The best performance model in the first cross-validation dataset was further optimized and the final performance was assessed using the final test set. (3) Results: In total, 89,279 patients over seven years were reviewed. Extubation failure occurred in 77 patients. Next, 186 patients with a successful extubation were screened as the control group according to the surgery type for patients with extubation failure. Based on the stepwise regression, seven variables were screened for subsequent analysis. After training, SVM and LOG models showed better prediction ability. In the k-fold dataset, the area under the curve using SVM and LOG were 0.74 (95% confidence interval, 0.55-0.93) and 0.71 (95% confidence interval, 0.59-0.82), respectively, in the k-fold dataset. (4) Conclusion: Applying our machine-learning model to predict extubation failure after general anesthesia in clinical practice might help to reduce morbidity and mortality of patients with difficult airways after head, neck, and maxillofacial surgeries.

Does the Heart Fall Asleep?-Diurnal Variations in Heart Rate Variability in Patients with Disorders of Consciousness

The current study investigated heart rate (HR) and heart rate variability (HRV) across day and night in patients with disorders of consciousness (DOC). We recorded 24-h electrocardiography in 26 patients with DOC (i.e., unresponsive wakefulness syndrome (UWS; n = 16) and (exit) minimally conscious state ((E)MCS; n = 10)). To examine diurnal variations, HR and HRV indices in the time, frequency, and entropy domains were computed for periods of clear day- (forenoon: 8 a.m.-2 p.m.; afternoon: 2 p.m.-8 p.m.) and nighttime (11 p.m.-5 a.m.). The results indicate that patients' interbeat intervals (IBIs) were larger during the night than during the day, indicating HR slowing. The patients in UWS showed larger IBIs compared to the patients in (E)MCS, and the patients with non-traumatic brain injury showed lower HRV entropy than the patients with traumatic brain injury. Additionally, higher HRV entropy was associated with higher EEG entropy during the night. Thus, cardiac activity varies with a diurnal pattern in patients with DOC and can differentiate between patients' diagnoses and etiologies. Moreover, the interaction of heart and brain appears to follow a diurnal rhythm. Thus, HR and HRV seem to mirror the integrity of brain functioning and, consequently, might serve as supplementary measures for improving the validity of assessments in patients with DOC.

Dynamic Temporal Relationship Between Autonomic Function and Cerebrovascular Reactivity in Moderate/Severe Traumatic Brain Injury

There has been little change in morbidity and mortality in traumatic brain injury (TBI) in the last 25 years. However, literature has emerged linking impaired cerebrovascular reactivity (a surrogate of cerebral autoregulation) with poor outcomes post-injury. Thus, cerebrovascular reactivity (derived through the pressure reactivity index; PRx) is emerging as an important continuous measure. Furthermore, recent literature indicates that autonomic dysfunction may drive impaired cerebrovascular reactivity in moderate/severe TBI. Thus, to improve our understanding of this association, we assessed the physiological relationship between PRx and the autonomic variables of heart rate variability (HRV), blood pressure variability (BPV), and baroreflex sensitivity (BRS) using time-series statistical methodologies. These methodologies include vector autoregressive integrative moving average (VARIMA) impulse response function analysis, Granger causality, and hierarchical clustering. Granger causality testing displayed inconclusive results, where PRx and the autonomic variables had varying bidirectional relationships. Evaluating the temporal profile of the impulse response function plots demonstrated that the autonomic variables of BRS, ratio of low/high frequency of HRV and very low frequency HRV all had a strong relation to PRx, indicating that the sympathetic autonomic response may be more closely linked to cerebrovascular reactivity, then other variables. Finally, BRS was consistently associated with PRx, possibly demonstrating a deeper relationship to PRx than other autonomic measures. Taken together, cerebrovascular reactivity and autonomic response are interlinked, with a bidirectional impact between cerebrovascular reactivity and circulatory autonomics. However, this work is exploratory and preliminary, with further study required to extract and confirm any underlying relationships.

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury

Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the "one-treatment fits all" approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex "-omics" data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.

Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery

With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.

Fractal motor activity regulation and sex differences in preclinical Alzheimer's disease pathology

INTRODUCTION

Degradation in fractal motor activity regulation (FMAR), a measure of multiscale self-similarity of motor control, occurs in aging and accelerates with clinical progression to Alzheimer's disease (AD). Whether FMAR changes occur during the pre-symptomatic phase of the disease in women and men remains unknown.

METHODS

FMAR was assessed in cognitively normal participants (n = 178) who underwent 7 to 14 days of home actigraphy. Preclinical AD pathology was determined by amyloid imaging-Pittsburgh compound B (PiB) and cerebrospinal fluid (CSF) phosphorylated-tau181 (p-tau) to amyloid beta 42 (Aβ42) ratio.

RESULTS

Degradation in daytime FMAR was overall significantly associated with preclinical amyloid plaque pathology via PiB+ imaging (beta coefficient β = 0.217, standard error [SE] = 0.101, P = .034) and increasing CSF tau181-Aβ42 ratio (β = 0.220, SE = 0.084, P = .009). In subset analysis by sex, the effect sizes were significant in women for PiB+ (β = 0.279, SE = 0.112, P = .015) and CSF (β = 0.245, SE = 0.094, P = .011) but not in men (both Ps > .05). These associations remained after inclusion of daily activity level, apolipoprotein E ε4 carrier status, and rest/activity patterns.

DISCUSSION

Changes in daytime FMAR from actigraphy appear to be present in women early in preclinical AD. This may be a combination of earlier pathology changes in females reflected in daytime FMAR, and a relatively underpowered male group. Further studies are warranted to test FMAR as an early noncognitive physiological biomarker that precedes the onset of cognitive symptoms.

The role of brain barriers in the neurokinetics and pharmacodynamics of lithium

Lithium (Li) is the most widely used mood stabilizer in treating patients with bipolar disorder. However, more than half of the patients do not or partially respond to Li therapy, despite serum Li concentrations in the serum therapeutic range. The exact mechanisms underlying the pharmacokinetic-pharmacodynamic (PK-PD) relationships of lithium are still poorly understood and alteration in the brain pharmacokinetics of lithium may be one of the mechanisms explaining the variability in the clinical response to Li. Brain barriers such as the blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier (BCSFB) play a crucial role in controlling blood-to-brain and brain-to-blood exchanges of various molecules including central nervous system (CNS) drugs. Recent in vivo studies by nuclear resonance spectroscopy revealed heterogenous brain distribution of Li in human that were not always correlated with serum concentrations, suggesting regional and variable transport mechanisms of Li through the brain barriers. Moreover, alteration in the functionality and integrity of brain barriers is reported in various CNS diseases, as a cause or a consequence and in this regard, Li by itself is known to modulate BBB properties such as the expression and activity of various transporters, metabolizing enzymes, and the specialized tight junction proteins on BBB. In this review, we will focus on recent knowledge into the role of the brain barriers as key-element in the Li neuropharmacokinetics which might improve the understanding of PK-PD of Li and its interindividual variability in drug response.

Resting Heartbeat Complexity Predicts All-Cause and Cardiorespiratory Mortality in Middle- to Older-Aged Adults From the UK Biobank

Background

Spontaneous heart rate fluctuations contain rich information related to health and illness in terms of physiological complexity, an accepted indicator of plasticity and adaptability. However, it is challenging to make inferences on complexity from shorter, more practical epochs of data. Distribution entropy (DistEn) is a recently introduced complexity measure that is designed specifically for shorter duration heartbeat recordings. We hypothesized that reduced DistEn predicted increased mortality in a large population cohort.

Method and Results

The prognostic value of DistEn was examined in 7631 middle‐older–aged UK Biobank participants who had 2‐minute resting ECGs conducted (mean age, 59.5 years; 60.4% women). During a median follow‐up period of 7.8 years, 451 (5.9%) participants died. In Cox proportional hazards models with adjustment for demographics, lifestyle factors, physical activity, cardiovascular risks, and comorbidities, for each 1‐SD decrease in DistEn, the risk increased by 36%, 56%, and 73% for all‐cause, cardiovascular, and respiratory disease–related mortality, respectively. These effect sizes were equivalent to the risk of death from being >5 years older, having been a former smoker, or having diabetes mellitus. Lower DistEn was most predictive of death in those <55 years with a prior myocardial infarction, representing an additional 56% risk for mortality compared with older participants without prior myocardial infarction. These observations remained after controlling for traditional mortality predictors, resting heart rate, and heart rate variability.

Conclusions

Resting heartbeat complexity from short, resting ECGs was independently associated with mortality in middle‐ to older‐aged adults. These risks appear most pronounced in middle‐aged participants with prior MI, and may uniquely contribute to mortality risk screening.

Impaired Dynamic Cerebral Autoregulation in Cerebral Venous Thrombosis

Background: Cerebral autoregulation is crucial in traumatic brain injury, which might be used for determining the optimal intracranial pressure. Cerebral venous thrombosis (CVT) is a cerebral vascular disease with features of high intracranial pressure. However, the autoregulatory mechanism of CVT remains unknown. We aimed to investigate the capacity of cerebral autoregulation in patients with CVT. Methods: This study consecutively enrolled 23 patients with CVT and 16 controls from December 2018 to May 2019. Cerebral autoregulation was assessed by transfer function analysis (rate of recovery/phase/gain) using the spontaneous oscillations of the cerebral blood flow velocity and arterial blood pressure. Results: In total, 76 middle cerebral arteries (MCAs) were investigated, including 44 MCAs in patients with CVT and 32 normal ones. The phase shift estimated in patients with CVT was significantly different from that of the controls (37.37 ± 36.53 vs. 54.00 ± 26.78, p = 0.03). The rate of recovery and gain in patients with CVT were lower than those in controls but without statistical significance. Conclusion: To our knowledge, this is the first time that a study has indicated that patients with CVT were more likely to have impaired cerebral autoregulation. Hence, cautious blood pressure control is required in such patients to prevent hyper- or hypoperfusion.

The Limited Impact of Current Therapeutic Interventions on Cerebrovascular Reactivity in Traumatic Brain Injury: A Narrative Overview

Current intensive care unit (ICU) treatment strategies for traumatic brain injury (TBI) care focus on intracranial pressure (ICP)- and cerebral perfusion pressure (CPP)-directed therapeutics, dictated by guidelines. Impaired cerebrovascular reactivity in moderate/severe TBI is emerging as a major associate with poor outcome and appears to dominate the landscape of physiologic derangement over the course of a patient's ICU stay. Within this article, we review the literature on the known drivers of impaired cerebrovascular reactivity in adult TBI, highlight the current knowledge surrounding the impact of guideline treatment strategies on continuously monitored cerebrovascular reactivity, and discuss current treatment paradigms for impaired reactivity. Finally, we touch on the areas of future research, as we strive to develop specific therapeutics for impaired cerebrovascular reactivity in TBI. There exists limited literature to suggest advanced age, intracranial injury patterns of diffuse injury, and sustained ICP elevations may drive impaired cerebrovascular reactivity. To date, the literature suggests there is a limited impact of such ICP/CPP guideline-based therapies on cerebrovascular reactivity, with large portions of a given patients ICU period spent with impaired cerebrovascular reactivity. Emerging treatment paradigms focus on the targeting individualized CPP and ICP thresholds based on cerebrovascular reactivity, without directly targeting the pathways involved in its dysfunction. Further work involved in uncovering the molecular pathways involved in impaired cerebrovascular reactivity is required, so that we can develop therapeutics directed at its prevention and treatment.

Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

Signal Information Prediction of Mortality Identifies Unique Patient Subsets after Severe Traumatic Brain Injury: A Decision-Tree Analysis Approach

Nonlinear physiological signal features that reveal information content and causal flow have recently been shown to be predictors of mortality after severe traumatic brain injury (TBI). The extent to which these features interact together, and with traditional measures to describe patients in a clinically meaningful way remains unclear. In this study, we incorporated basic demographics (age and initial Glasgow Coma Scale [GCS]) with linear and non-linear signal information based features (approximate entropy [ApEn], and multivariate conditional Granger causality [GC]) to evaluate their relative contributions to mortality using cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10 year period. Beyond linear modelling, we employed a decision tree analysis approach to define a predictive hierarchy of features. We found ApEn (p = 0.009) and GC (p = 0.004) based features to be independent predictors of mortality at a time when mean intracranial pressure (ICP) was not. Our combined model with both signal information-based features performed the strongest (area under curve = 0.86 vs. 0.77 for linear features only). Although low "intracranial" complexity (ApEn-ICP) outranked both age and GCS as crucial drivers of mortality (fivefold increase in mortality where ApEn-ICP <1.56, 36.2% vs. 7.8%), decision tree analysis revealed clear subsets of patient populations using all three predictors. Patients with lower ApEn-ICP who were >60 years of age died, whereas those with higher ApEn-ICP and GCS ≥5 all survived. Yet, even with low initial intracranial complexity, as long as patients maintained robust GC and "extracranial" complexity (ApEn of mean arterial pressure), they all survived. Incorporating traditional linear and novel, non-linear signal information features, particularly in a framework such as decision trees, may provide better insight into "health" status. However, caution is required when interpreting these results in a clinical setting prior to external validation.

Fragmentation of Rest/Activity Patterns in Community-Based Elderly Individuals Predicts Incident Heart Failure

STUDY OBJECTIVES

Heart failure has previously been linked to sleep disorders that are often associated with frequent disturbances to human rest/activity patterns. We tested whether fragmentation of sustained rest/activity patterns derived from actigraphic recordings at baseline predicts incident heart failure in community-based elderly individuals.

METHODS

We studied 1099 community-based elderly adults participating in the Rush Memory and Aging Project who had baseline motor activity monitoring up to 11 days and were followed annually for up to 14 years. Fragmentation was assessed using previously validated indexes, derived from the probability of transitions once sustained rest or activity has been established. Heart failure was recorded via a clinical interview during the annual follow-up. Cox proportional hazards models were constructed to examine the relationship between rest fragmentation index and incident heart failure. Covariates grouped in terms of demographics, lifestyle factors and co-morbidities and cardiovascular risk factors/diseases were included.

RESULTS

Increased rest fragmentation (but not activity fragmentation) was associated with higher risk for incident heart failure. Specifically, a subject with a rest fragmentation at the 90th percentile showed a 57% increased risk of developing incident heart failure compared to a subject at the 10th percentile in this cohort. This effect was equivalent to that of being over a decade older. These observations were consistent after adjusting for all covariates.

CONCLUSION

Increased rest fragmentation, a potential surrogate for sleep fragmentation, is independently associated with a higher risk of developing heart failure in community-based elderly adults during up to 14 years of follow-up. Further work is required to examine the specific contributions from daytime napping versus nighttime sleep periods in the elderly, as well as the underlying autonomic and cardio-dynamic pathways that may explain the effects on heart function.

Measures of CNS-Autonomic Interaction and Responsiveness in Disorder of Consciousness

Neuroimaging studies have demonstrated functional interactions between autonomic (ANS) and brain (CNS) structures involved in higher brain functions, including attention and conscious processes. These interactions have been described by the Central Autonomic Network (CAN), a concept model based on the brain-heart two-way integrated interaction. Heart rate variability (HRV) measures proved reliable as non-invasive descriptors of the ANS-CNS function setup and are thought to reflect higher brain functions. Autonomic function, ANS-mediated responsiveness and the ANS-CNS interaction qualify as possible independent indicators for clinical functional assessment and prognosis in Disorders of Consciousness (DoC). HRV has proved helpful to investigate residual responsiveness in DoC and predict clinical recovery. Variability due to internal (e.g., homeostatic and circadian processes) and environmental factors remains a key independent variable and systematic research with this regard is warranted. The interest in bidirectional ANS-CNS interactions in a variety of physiopathological conditions is growing, however, these interactions have not been extensively investigated in DoC. In this brief review we illustrate the potentiality of brain-heart investigation by means of HRV analysis in assessing patients with DoC. The authors' opinion is that this easy, inexpensive and non-invasive approach may provide useful information in the clinical assessment of this challenging patient population.

EZ Entropy: a software application for the entropy analysis of physiological time-series

Background

Entropy analysis has been attracting increasing attentions in the recent two or three decades. It assesses complexity, or irregularity, of time-series which is extraordinarily relevant to physiology and diseases as demonstrated by tremendous studies. However, the complexity can hardly be appreciated by traditional methods including time-, frequency-domain analysis, and time-frequency analysis that are the common built-in options in commercialized measurement and statistical software. To facilitate the entropy analysis of physiological time-series, a new software application, namely EZ Entropy, was developed and introduced in this article.

Results

EZ Entropy was developed in MATLAB^® environment. It was programmed in an object-oriented style and was constructed with a graphical user interface. EZ Entropy is easy to operate through its compact graphical interface, thus allowing researchers without knowledge of programming like clinicians and physiologists to perform such kind of analysis. Besides, it offers various settings to meet different analysis needs including (1) processing single data recording, (2) batch processing multiple data files, (3) sliding window calculations, (4) recall, (5) displaying intermediate data and final results, (6) adjusting input parameters, and (7) exporting calculation results after the run or in real-time during the analysis. The analysis results could be exported, either manually or automatically, to comma-separated ASCII files, thus being compatible to and easily imported into the common statistical analysis software. Code-wise, EZ Entropy is object-oriented, thus being quite easy to maintain and extend.

Conclusions

EZ Entropy is a user-friendly software application to perform the entropy analysis of time-series, as well as to simplify and to speed up this useful analysis.

Electronic supplementary material

The online version of this article (10.1186/s12938-019-0650-5) contains supplementary material, which is available to authorized users.

Autonomic Dysfunction and Associations with Functional and Neurophysiological Outcome in Moderate/Severe Traumatic Brain Injury: A Scoping Review

The quantification and objective documentation of autonomic dysfunction in traumatic brain injury (TBI) is neither well studied nor extensively validated. Most of the descriptions of autonomic dysfunction in the literature are in the form of vague non-specific clinical manifestations. Few studies propose the use of objective measures of assessing the extent of autonomic dysfunction to link them to the outcome of TBI. Our goal was to perform a scoping systematic review of the literature on the objective documentation of autonomic dysfunction in terms of functional and physiological variables to be linked to outcome of TBI. PubMed/MEDLINE^®, BIOSIS, Scopus, Embase, Cochrane Libraries, and Global Health databases were searched. Two reviewers independently screened the results. Full texts for citations passing this initial screen were obtained. Inclusion and exclusion criteria were applied to each article to obtain final articles for review. The initial search yielded 2619 citations. Of 69 articles selected for final review, 14 were chosen based on the inclusion and exclusion criteria and are included in the results of this article. 9 of these articles assessed autonomic dysfunction using functional variables and 7 assessed autonomic dysfunction using physiological variables. Some studies included both functional and physiological variables. Of the nine studies linking autonomic dysfunction to functional variables, nine included heart rate variability (HRV), three included baroreflex sensitivity (BRS), and two included blood pressure variability (BPV). A total of 2714 adult patients were studied. Although the nature of association between autonomic dysfunction and outcome is unclear, the objective quantification of autonomic dysfunction seems to be associated with global patient outcome and other neurophysiological measures. Further studies are needed to validate its use and explore the underlying molecular mechanisms of the described associations.

Statistical Signal Properties of the Pressure-Reactivity Index (PRx)

OBJECTIVES

The pressure-reactivity index (PRx) is defined in terms of the moving correlation coefficient between intracranial pressure (ICP) and mean arterial pressure (MAP) and is a measure of cerebral autoregulation ability. Plots of PRx against cerebral perfusion pressure (CPP) show a U-shaped behaviour: the minimum reflecting optimal cerebral autoregulation (CPPopt). However U-shaped behaviour may also occur by chance. To date there has been no evaluation of the statistical properties of these signals.

MATERIALS AND METHODS

We simulated PRx/CPP distributions using synthetic ICP and MAP signals from Gaussian noise with known cross-correlation and calculated the statistical distribution of extrema in the PRx/CPP relationship.

RESULTS

The calculation of PRx on random data is statistically biased to show a U-shaped behaviour when the signals are positively cross-correlated (equivalent to PRx > 0). For PRx < 0, the bias is towards an inverse U-shaped behaviour. We demonstrate that this bias is eliminated by Fisher transforming the PRx data before CPPopt analysis.

CONCLUSIONS

Cross-correlated signals are biased to show a U-shaped distribution. A CPPopt-like behaviour will be observed more often than not even from random ICP and MAP signals that do not exhibit autoregulation, unless PRx is Fisher transformed. Care must be taken in interpreting CPPopt in terms of physiology calculated from untransformed data.

Intracranial Hypertension and Cerebral Autoregulation: A Systematic Review and Meta-Analysis

OBJECTIVE

To present a systematic review and meta-analysis to establish the relation between cerebral autoregulation (CA) and intracranial hypertension.

METHODS

An electronic search using the term "Cerebral autoregulation and intracranial hypertension" was designed to identify studies that analyzed cerebral blood flow autoregulation in patients undergoing intracranial pressure (ICP) monitoring. The data were used in meta-analyses and sensitivity analyses.

RESULTS

A static CA technique was applied in 10 studies (26.3%), a dynamic technique was applied in 25 studies (65.8%), and both techniques were used in 3 studies (7.9%). Static CA studies using the cerebral blood flow technique revealed impaired CA in patients with an ICP ≥20 (standardized mean difference [SMD] 5.44%, 95% confidence interval [CI] 0.25-10.65, P = 0.04); static CA studies with transcranial Doppler revealed a tendency toward impaired CA in patients with ICP ≥20 (SMD -7.83%, 95% CI -17.52 to 1.85, P = 0.11). Moving correlation studies reported impaired CA in patients with ICP ≥20 (SMD 0.06, 95% CI 0.07-0.14, P < 0.00001). A comparison of CA values and mean ICP revealed a correlation between greater ICP and impaired CA (SMD 5.47, 95% CI 1.39-10.1, P = 0.01). Patients with ICP ≥20 had an elevated risk of impaired CA (OR 2.27, 95% CI 1.20-4.31, P = 0.01).

CONCLUSIONS

A clear tendency toward CA impairment was observed in patients with increased ICP.

Pressure Autoregulation Measurement Techniques in Adult Traumatic Brain Injury, Part II: A Scoping Review of Continuous Methods

A scoping review of the literature was performed systematically on commonly described continuous autoregulation measurement techniques in adult traumatic brain injury (TBI) to provide an overview of methodology and comprehensive reference library of the available literature for each technique. Five separate small systematic reviews were conducted for each of the continuous techniques: pressure reactivity index (PRx), laser Doppler flowmetry (LDF), near infrared spectroscopy (NIRS) techniques, brain tissue oxygen tension (PbtO₂), and thermal diffusion (TD) techniques. Articles from MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library (inception to December 2016), and reference lists of relevant articles were searched. A two-tier filter of references was conducted. The literature base identified from the individual searches was limited, except for PRx. The total number of articles using each of the five searched techniques for continuous autoregulation in adult TBI were: PRx (28), LDF (4), NIRS (9), PbtO₂ (10), and TD (8). All continuous techniques described in adult TBI are based on moving correlation coefficients. The premise behind the calculation of these moving correlation coefficients focuses on the impact of slow fluctuations in either mean arterial pressure (MAP) or cerebral perfusion pressure (CPP) on some indirect measure of cerebral blood flow (CBF), such as: intracranial pressure (ICP), LDF, NIRS signals, PbtO₂, or TD CBF. The thought is the correlation between a hemodynamic driving factor, such as MAP or CPP, and a surrogate for CBF or cerebral perfusion sheds insight on the state of cerebral autoregulation. Both PRx and NIRS indices were validated experimentally against the "gold standard" static autoregulatory curve (Lassen curve) at least around the lower threshold of autoregulation. The PRx has the largest literature base supporting the association with patient outcome. Various methods of continuous autoregulation assessment are described within the adult TBI literature. Many studies exist on these various indices, suggesting an association between their values and patient morbidity/death.

Continuous Autoregulatory Indices Derived from Multi-Modal Monitoring: Each One Is Not Like the Other

We assess the relationships between various continuous measures of autoregulatory capacity in a cohort of adults with traumatic brain injury (TBI). We assessed relationships between autoregulatory indices derived from intracranial pressure (ICP: PRx, PAx, RAC), transcranial Doppler (TCD: Mx, Sx, Dx), brain tissue-oxygenation (ORx), and spatially resolved near infrared spectroscopy (NIRS resolved: TOx, THx). Relationships between indices were assessed using Pearson correlation coefficient, Friedman test, principal component analysis (PCA), agglomerative hierarchal clustering (AHC) and k-means cluster analysis (KMCA). All analytic techniques were repeated for a range of temporal resolutions of data, including minute-by-minute averages, moving means of 30 samples, and grand mean for each patient. Thirty-seven patients were studied. The PRx displayed strong association with PAx/RAC across all the analytical techniques: Pearson correlation (r = 0.682/r = 0.677, p < 0.0001), PCA, AHC, and KMCA in the grand mean data sheet. Most TCD-based indices (Mx, Dx) were correlated and co-clustered on PCA, AHC, and KMCA. The Sx was found to be more closely associated with ICP-derived indices on Pearson correlation, PCA, AHC, and KMCA. The NIRS indices displayed variable correlation with each other and with indices derived from ICP and TCD signals. Of interest, TOx and THx co-cluster with ICP-based indices on PCA and AHC. The ORx failed to display any meaningful correlations with other indices in neither of the analytical method used. Thirty-minute moving average and minute-by-minute data set displayed similar results across all the methods. The RAC, Mx, and Sx were the strongest predictors of outcome at six months. Continuously updating autoregulatory indices are not all correlated with one another. Caution must be advised when utilizing less commonly described autoregulation indices (i.e., ORx) for the clinical assessment of autoregulatory capacity, because they appear to not be related to commonly measured/establish indices, such as PRx. Further prospective validation is required.

Early Asymmetric Cardio-Cerebral Causality and Outcome after Severe Traumatic Brain Injury

The brain and heart are two vital systems in health and disease, increasingly recognized as a complex, interdependent network with constant information flow in both directions. After severe traumatic brain injury (TBI), the causal, directed interactions between the brain, heart, and autonomic nervous system have not been well established. Novel methods are needed to probe unmeasured, potentially prognostic information in complex biological networks that are not revealed by traditional means. In this study, we examined potential bidirectional causality between intracranial pressure (ICP), mean arterial pressure (MAP), and heart rate (HR) and its relationship to mortality in a 24-h period early post-TBI. We applied Granger causality (GC) analysis to cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10-year period. There was significant bidirectional causality between ICP and MAP, MAP and HR, and ICP and HR in the majority of patients (p < 0.01). MAP influenced both ICP and HR to a greater extent (higher GC, p < 0. 00001), but there was no dominant unidirectional causality between ICP and HR (p = 0.85). Those who died had significantly lower GC for ICP causing MAP and HR causing ICP (p = 0.006 and p = 0.004, respectively) and were predictors of mortality independent of age, sex, and traditional intracranial variables (ICP, cerebral perfusion pressure, GCS, and pressure reactivity index). Examining the brain and heart with GC-based features for the first time in severe TBI patients has confirmed strong interdependence and reveals a significant relationship between select causality pairs and mortality. These results support the notion that impaired causal information flow between the cerebrovascular, autonomic, and cardiovascular systems are of central importance in severe TBI.

Low-Dose Lithium Stabilizes Human Endothelial Barrier by Decreasing MLC Phosphorylation and Universally Augments Cholinergic Vasorelaxation Capacity in a Direct Manner

Lithium at serum concentrations up to 1 mmol/L has been used in patients suffering from bipolar disorder for decades and has recently been shown to reduce the risk for ischemic stroke in these patients. The risk for stroke and thromboembolism depend not only on cerebral but also on general endothelial function and health; the entire endothelium as an organ is therefore pathophysiologically relevant. Regardless, the knowledge about the direct impact of lithium on endothelial function remains poor. We conducted an experimental study using lithium as pharmacologic pretreatment for murine, porcine and human vascular endothelium. We predominantly investigated endothelial vasorelaxation capacities in addition to human basal and dynamic (thrombin-/PAR-1 receptor agonist-impaired) barrier functioning including myosin light chain (MLC) phosphorylation (MLC-P). Low-dose therapeutic lithium concentrations (0.4 mmol/L) significantly augment the cholinergic endothelium-dependent vasorelaxation capacities of cerebral and thoracic arteries, independently of central and autonomic nerve system influences. Similar concentrations of lithium (0.2–0.4 mmol/L) significantly stabilized the dynamic thrombin-induced and PAR-1 receptor agonist-induced permeability of human endothelium, while even the basal permeability appeared to be stabilized. The lithium-attenuated dynamic permeability was mediated by a reduced endothelial MLC-P known to be followed by a lessening of endothelial cell contraction and paracellular gap formation. The well-known lithium-associated inhibition of inositol monophosphatase/glycogen synthase kinase-3-β signaling-pathways involving intracellular calcium concentrations in neurons seems to similarly occur in endothelial cells, too, but with different down-stream effects such as MLC-P reduction. This is the first study discovering low-dose lithium as a drug directly stabilizing human endothelium and ubiquitously augmenting cholinergic endothelium-mediated vasorelaxation. Our findings have translational and potentially clinical impact on cardiovascular and cerebrovascular disease associated with inflammation explaining why lithium can reduce, e.g., the risk for stroke. However, further clinical studies are warranted.