Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage

Future of neurocritical care: Integrating neurophysics, multimodal monitoring, and machine learning

Multimodal monitoring (MMM) in the intensive care unit (ICU) has become increasingly sophisticated with the integration of neurophysical principles. However, the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes. This manuscript reviewed current neuromonitoring tools, focusing on intracranial pressure, cerebral electrical activity, metabolism, and invasive and noninvasive autoregulation monitoring. In addition, the integration of advanced machine learning and data science tools within the ICU were discussed. Invasive monitoring includes analysis of intracranial pressure waveforms, jugular venous oximetry, monitoring of brain tissue oxygenation, thermal diffusion flowmetry, electrocorticography, depth electroencephalography, and cerebral microdialysis. Noninvasive measures include transcranial Doppler, tympanic membrane displacement, near-infrared spectroscopy, optic nerve sheath diameter, positron emission tomography, and systemic hemodynamic monitoring including heart rate variability analysis. The neurophysical basis and clinical relevance of each method within the ICU setting were examined. Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools, helping clinicians make more accurate and timely decisions. These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies. MMM, grounded in neurophysics, offers a more nuanced understanding of cerebral physiology and disease in the ICU. Although each modality has its strengths and limitations, its integrated use, especially in combination with machine learning algorithms, can offer invaluable information for individualized patient care.

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

BACKGROUND

Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.

METHODS

aSAH patients from 2 prospective cohorts (Zurich-derivation cohort, Aachen-validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1-4 vs. 5-8) or ordinal outcome (GOSE-extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.

RESULTS

A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.

CONCLUSIONS

MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Development of a predictive nomogram for 28-day mortality risk in non-traumatic or post-traumatic subarachnoid hemorrhage patients

OBJECTIVE

Subarachnoid hemorrhage (SAH) is associated with high rates of mortality and permanent disability. At present, there are few definite clinical tools to predict prognosis in SAH patients. The current study aims to develop and assess a predictive nomogram model for estimating the 28-day mortality risk in both non-traumatic or post-traumatic SAH patients.

METHODS

The MIMIC-III database was searched to select patients with SAH based on ICD-9 codes. Patients were separated into non-traumatic and post-traumatic SAH groups. Using LASSO regression analysis, we identified independent risk factors associated with 28-day mortality and incorporated them into nomogram models. The performance of each nomogram was assessed by calculating various metrics, including the area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

RESULTS

The study included 999 patients with SAH, with 631 in the non-traumatic group and 368 in the post-traumatic group. Logistic regression analysis revealed critical independent risk factors for 28-day mortality in non-traumatic SAH patients, including gender, age, glucose, platelet, sodium, BUN, WBC, PTT, urine output, SpO2, and heart rate and age, glucose, PTT, urine output, and body temperature for post-traumatic SAH patients. The prognostic nomograms outperformed the commonly used SAPSII and APSIII systems, as evidenced by superior AUC, NRI, IDI, and DCA results.

CONCLUSION

The study identified independent risk factors associated with the 28-day mortality risk and developed predictive nomogram models for both non-traumatic and post-traumatic SAH patients. The nomogram holds promise in guiding prognosis improvement strategies for patients with SAH.

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.

Aberrant brain-heart coupling is associated with the severity of post cardiac arrest brain injury

OBJECTIVE

To investigate autonomic nervous system activity measured by brain-heart interactions in comatose patients after cardiac arrest in relation to the severity and prognosis of hypoxic-ischemic brain injury.

METHODS

Strength and complexity of bidirectional interactions between EEG frequency bands (delta, theta, and alpha) and ECG heart rate variability frequency bands (low frequency, LF and high frequency, HF) were computed using a synthetic data generation model. Primary outcome was the severity of brain injury, assessed by (i) standardized qualitative EEG classification, (ii) somatosensory evoked potentials (N20), and (iii) neuron-specific enolase levels. Secondary outcome was the 3-month neurological status, assessed by the Cerebral Performance Category score [good (1-2) vs. poor outcome (3-4-5)].

RESULTS

Between January 2007 and July 2021, 181 patients were admitted to ICU for a resuscitated cardiac arrest. Poor neurological outcome was observed in 134 patients (74%). Qualitative EEG patterns suggesting high severity were associated with decreased LF/HF. Severity of EEG changes were proportional to higher absolute values of brain-to-heart coupling strength (p < 0.02 for all brain-to-heart frequencies) and lower values of alpha-to-HF complexity (p = 0.049). Brain-to-heart coupling strength was significantly higher in patients with bilateral absent N20 and correlated with neuron-specific enolase levels at Day 3. This aberrant brain-to-heart coupling (increased strength and decreased complexity) was also associated with 3-month poor neurological outcome.

INTERPRETATION

Our results suggest that autonomic dysfunctions may well represent hypoxic-ischemic brain injury post cardiac arrest pathophysiology. These results open avenues for integrative monitoring of autonomic functioning in critical care patients.

The Clinical Characteristics of Heart Rate Variability After Stroke: A Systematic Review

The autonomic nervous system dysfunction has been reported in up to 76% of stroke patients 7 days after an acute stroke. Heart rate variability (HRV) is one of the important indicators reflecting the balance of sympathetic and parasympathetic nerves. Therefore, we performed a systematic literature review of existing literature on the association between heart rate variability and the different types of stroke. We included studies published in the last 32 years (1990 to 2022). The electronic databases MEDLINE and PubMed were searched. We selected the research that met the inclusion or exclusion criteria. A narrative synthesis was performed. This review aimed to summarize evidence regarding the potential mechanism of heart rate variability among patients after stroke. In addition, the association of clinical characteristics of heart rate variability and stroke has been depicted. The review further discussed the relationship between post-stroke infection and heart rate variability, which could assist in curbing clinical infection in patients with stroke. HRVas a noninvasive clinical monitoring tool can quantitatively assess the changes in autonomic nervous system activity and further predict the outcome of stroke. HRV could play an important role in guiding the clinical practice for autonomic nervous system disorder after stroke.

Predictor role of heart rate variability in subarachnoid hemorrhage: A systematic review

Background- Subarachnoid hemorrhage (SAH) is one of the most devastating diseases with a high rate of morbidity and mortality. The heart rate variability (HRV) is a non-invasive method of monitoring various components of the autonomic nervous system activity that can be utilized to delineate autonomic dysfunctions associated with various physiological and pathological conditions. The reliability of HRV as a predictor of clinical outcome in aneurysmal subarachnoid hemorrhage (aSAH) is not yet well investigated in literature. Methods- A systematic review and in depth analysis of 10 articles on early HRV changes in SAH patients was performed. Results- This systematic review demonstrates a correlation between early changes in HRV indices (time and frequency domain) and the development of neuro-cardiogenic complications and poor neurologic outcome in patients with SAH. Conclusions- A correlation between absolute values or changes of the LF/HF ratio and neurologic and cardiovascular complications was found in multiple studies. Because of significant limitations of included studies, a large prospective study with proper handling of confounders is needed to generate high-quality recommendations regarding HRV as a predictor of post SAH complications and poor neurologic outcome.

Heart rate variability analysis in toxic leukoencephalopathy-induced malignant catatonia: A case report

RATIONALE

Toxic leukoencephalopathy, a condition resulting from exposure to toxic substances, can lead to malignant catatonia, a severe motor dysfunction with symptoms such as muscle rigidity and high-spiking fever, hypertensive urgency, and tachycardia. This case study investigates the relationship between toxic leukoencephalopathy-induced malignant catatonia and heart rate variability (HRV), a marker of autonomic nervous system function.

PATIENT CONCERNS

A 51-year-old male presented to the emergency department with acute onset of progressively worsening mental status.

DIAGNOSES

The patient was diagnosed with cocaine-induced toxic leukoencephalopathy causing malignant catatonia.

INTERVENTIONS

A 5-day escalating treatment regimen was instituted for the management of malignant catatonia until resolution. Daily HRV parameters in the temporal and frequency domain, geometric data, and cardiac entropy were recorded using HRVAnalysis v.1.2 (ANS Lab Tools). The HRV analysis was correlated with pharmacologic management, the Bush-Francis catatonia rating scale, and hemodynamic parameters, including blood pressure, heart rate, and temperature.

OUTCOMES

The results showed a correlation between the severity and frequency of malignant catatonic episodes and the patient autonomic dysfunction. Improvement in malignant catatonia with pharmacological management was associated with an improved HRV, including elevated rMSSD, SDNN, cardiac entropy, and pNN50%.

LESSONS

Malignant catatonia is associated with decreased HRV, and its management is associated with an increase. This suggests a link between malignant catatonia and autonomic dysfunction, highlighting the potential benefits of treating malignant catatonia to improve autonomic function and reduce cardiovascular risk.

A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques

The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these mass effects, unruptured aneurysms frequently generate symptoms, however, the real hazard occurs when an aneurysm ruptures and results in a cerebral hemorrhage known as a subarachnoid hemorrhage. The objective is to study the multiple kinds of hemorrhage and aneurysm detection problems and develop machine and deep learning models to recognise them. Due to its early stage, subarachnoid hemorrhage, the most typical symptom after aneurysm rupture, is an important medical condition. It frequently results in severe neurological emergencies or even death. Although most aneurysms are asymptomatic and won't burst, because of their unpredictable growth, even small aneurysms are susceptible. A timely diagnosis is essential to prevent early mortality because a large percentage of hemorrhage cases present can be fatal. Physiological/imaging markers and the degree of the subarachnoid hemorrhage can be used as indicators for potential early treatments in hemorrhage. The hemodynamic pathomechanisms and microcellular environment should remain a priority for academics and medical professionals. There is still disagreement about how and when to care for aneurysms that have not ruptured despite studies reporting on the risk of rupture and outcomes. We are optimistic that with the progress in our understanding of the pathophysiology of hemorrhages and aneurysms and the advancement of artificial intelligence has made it feasible to conduct analyses with a high degree of precision, effectiveness and reliability.

Heart Rate Variability for Outcome Prediction in Intracerebral and Subarachnoid Hemorrhage: A Systematic Review

This systematic review presents clinical evidence on the association of heart rate variability with outcome prediction in intracerebral and subarachnoid hemorrhages. The literature search led to the retrieval of 19 significant studies. Outcome prediction included functional outcome, cardiovascular complications, secondary brain injury, and mortality. Various aspects of heart rate recording and analysis, based on linear time and frequency domains and a non-linear entropy approach, are reviewed. Heart rate variability was consistently associated with poor functional outcome and mortality, while controversial results were found regarding the association between heart rate variability and secondary brain injury and cardiovascular complications.

Heart rate variability as a predictor of stroke course, functional outcome, and medical complications: A systematic review

Background: Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function that is based on the analysis of length differences between subsequent RR intervals of the electrocardiogram. The aim of this systematic review was to assess the current knowledge gap in the utility of HRV parameters and their value as predictors of the acute stroke course. Methods: A systematic review was performed in accordance with the PRISMA guidelines. Relevant articles published between 1 January 2016 and 1 November 2022 available in the PubMed, Web of Science, Scopus, and Cochrane Library databases were obtained using a systematic search strategy. The following keywords were used to screen the publications: "heart rate variability" AND/OR "HRV" AND "stroke." The eligibility criteria that clearly identified and described outcomes and outlined restrictions on HRV measurement were pre-established by the authors. Articles assessing the relationship between HRV measured in the acute phase of stroke and at least one stroke outcome were considered. The observation period did not exceed 12 months. Studies that included patients with medical conditions influencing HRV with no established stroke etiology and non-human subjects were excluded from the analysis. To minimize the risk of bias, disagreements throughout the search and analysis were resolved by two independent supervisors. Results: Of the 1,305 records obtained from the systematic search based on keywords, 36 were included in the final review. These publications provided insight into the usability of linear and non-linear HRV analysis in predicting the course, complications, and mortality of stroke. Furthermore, some modern techniques, such as HRV biofeedback, for the improvement of cognition performance after a stroke are discussed. Discussion: The present study showed that HRV could be considered a promising biomarker of a stroke outcome and its complications. However, further research is needed to establish a methodology for appropriate quantification and interpretation of HRV-derived parameters.

Association between postictal EEG suppression, postictal autonomic dysfunction, and sudden unexpected death in epilepsy: Evidence from intracranial EEG

OBJECTIVE

The association between postictal electroencephalogram (EEG) suppression (PES), autonomic dysfunction, and Sudden Unexpected Death in Epilepsy (SUDEP) remains poorly understood. We compared PES on simultaneous intracranial and scalp-EEG and evaluated the association of PES with postictal heart rate variability (HRV) and SUDEP outcome.

METHODS

Convulsive seizures were analyzed in patients with drug-resistant epilepsy at 5 centers. Intracranial PES was quantified using the Hilbert transform. HRV was quantified using root mean square of successive differences of interbeat intervals, low-frequency to high-frequency power ratio, and RR-intervals.

RESULTS

There were 64 seizures from 63 patients without SUDEP and 11 seizures from 6 SUDEP patients. PES occurred in 99% and 87% of seizures on intracranial-EEG and scalp-EEG, respectively. Mean PES duration in intracranial and scalp-EEG was similar. Intracranial PES was regional (<90% of channels) in 46% of seizures; scalp PES was generalized in all seizures. Generalized PES showed greater decrease in postictal parasympathetic activity than regional PES. PES duration and extent were similar between patients with and without SUDEP.

CONCLUSIONS

Regional intracranial PES can be present despite scalp-EEG demonstrating generalized or no PES. Postictal autonomic dysfunction correlates with the extent of PES.

SIGNIFICANCE

Intracranial-EEG demonstrates changes in autonomic regulatory networks not seen on scalp-EEG.

Heart rate variability and adrenal size provide clues to sudden cardiac death in hospitalized COVID-19 patients

PURPOSE

To examine the association between a measure of heart rate variability and sudden cardiac death (SCD) in COVID-19 patients.

METHODS

Patients with SARS-COV-2 infection admitted to Columbia University Irving Medical Center who died between 4/25/2020 and 7/14/2020 and had an autopsy were examined for root mean square of successive differences (RMSSD), organ weights, and evidence of SCD.

RESULTS

Thirty COVID-19 patients were included and 12 had SCD. The RMSSD over 7 days without vs with SCD was median 0.0129 (IQR 0.0074-0.026) versus 0.0098 (IQR 0.0056-0.0197), p < 0.0001. The total adjusted adrenal weight of the non-SCD group was 0.40 g/kg (IQR 0.35-0.55) versus 0.25 g/kg (IQR 0.21-0.31) in the SCD group, p = 0.0007.

CONCLUSIONS

Hospitalized patients with COVID-19 who experienced SCD had lower parasympathetic activity (RMSSD) and smaller sized adrenal glands. Further research is required to replicate these findings.

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience.

The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery.

Association of Troponin T levels and functional outcome 3 months after subarachnoid hemorrhage

TroponinT levels are frequently elevated after subarachnoid hemorrhage (SAH). However, their clinical impact on long term outcomes still remains unclear. This study evaluates the association of TroponinT and functional outcomes 3 months after SAH. Data were obtained in the frame of a randomized controlled trial exploring the association of Goal-directed hemodynamic therapy and outcomes after SAH (NCT01832389). TroponinT was measured daily for the first 14 days after admission or until discharge from the ICU. Outcome was assessed using Glasgow Outcome Scale (GOS) 3 months after discharge. Logistic regression was used to explore the association between initial TroponinT values stratified by tertiles and admission as well as outcome parameters. TroponinT measurements were analyzed in 105 patients. TroponinT values at admission were associated with outcome assessed by GOS in a univariate analysis. TroponinT was not predictive of vasospasm or delayed cerebral ischemia, but an association with pulmonary and cardiac complications was observed. After adjustment for age, history of arterial hypertension and World Federation of Neurosurgical Societies (WFNS) grade, TroponinT levels at admission were not independently associated with worse outcome (GOS 1–3) or death at 3 months. In summary, TroponinT levels at admission are associated with 3 months-GOS but have limited ability to independently predict outcome after SAH.

Autonomic Nervous System Activity during Refractory Rise in Intracranial Pressure

Refractory intracranial hypertension (RIH) is a dramatic increase in intracranial pressure (ICP) that cannot be controlled by treatment. Recent reports suggest that the autonomic nervous system (ANS) activity may be altered during changes in ICP. Our study aimed to assess ANS activity during RIH and the causal relationship between rising in ICP and autonomic activity. We reviewed retrospectively 24 multicenter (Cambridge, Tromso, Berlin) patients in whom RIH developed as a pre-terminal event after acute brain injury (ABI). They were monitored with ICP, arterial blood pressure (ABP), and electrocardiography (ECG) using ICM+ software. Parameters reflecting autonomic activity were computed in time and frequency domain through the measurement of heart rate variability (HRV) and baroreflex sensitivity (BRS). Our results demonstrated that a rise in ICP was associated to a significant rise in HRV and BRS with a higher significance level in the high-frequency HRV (p < 0.001). This increase was followed by a significant decrease in HRV and BRS above the upper-breakpoint of ICP where ICP pulse-amplitude starts to decrease whereas the mean ICP continues to rise. Temporality measured with a Granger test suggests a causal relationship from ICP to ANS. The above results suggest that a rise in ICP interacts with ANS activity, mainly interfacing with the parasympathetic-system. The ANS seems to react to the rise in ICP with a response possibly focused on maintaining the cerebrovascular homeostasis. This happens until the critical threshold of ICP is reached above which the ANS variables collapse, probably because of low perfusion of the brain and the central autonomic network.

Continuous heart rate variability and electroencephalography monitoring in severe acute brain injury: a preliminary study

Background

Decreases in heart rate variability have been shown to be associated with poor outcomes in severe acute brain injury. However, it is unknown whether the changes in heart rate variability precede neurological deterioration in such patients. We explored the changes in heart rate variability measured by electrocardiography in patients who had neurological deterioration following severe acute brain injury, and examined the relationship between heart rate variability and electroencephalography parameters.

Methods

Retrospective analysis of 25 patients who manifested neurological deterioration after severe acute brain injury and underwent simultaneous electroencephalography plus electrocardiography monitoring.

Results

Eighteen electroencephalography channels and one simultaneously recorded electrocardiography channel were segmented into epochs of 120-second duration and processed to compute 10 heart rate variability parameters and three quantitative electroencephalography parameters. Raw electroencephalography of the epochs was also assessed by standardized visual interpretation and categorized based on their background abnormalities and ictalinterictal continuum patterns. The heart rate variability and electroencephalography parameters showed consistent changes in the 2-day period before neurological deterioration commenced. Remarkably, the suppression ratio and background abnormality of the electroencephalography parameters had significant reverse correlations with all heart rate variability parameters.

Conclusions

We observed a significantly progressive decline in heart rate variability from the day before the neurological deterioration events in patients with severe acute brain injury were first observed.

Dynamic change of heart rate in the acute phase and clinical outcomes after intracerebral hemorrhage: a cohort study

BACKGROUND

Dynamic change of heart rate in the acute phase and clinical outcomes after intracerebral hemorrhage (ICH) remains unknown. We aimed to investigate the associations of heart rate trajectories and variability with functional outcome and mortality in patients with acute ICH.

METHODS

This prospective study was conducted among 332 patients with acute ICH. Latent mixture modeling was used to identify heart rate trajectories during the first 72 h of hospitalization after ICH onset. Mean and coefficient of variation of heart rate measurements were calculated. The study outcomes included unfavorable functional outcome, ordinal shift of modified Rankin Scale score, and all-cause mortality.

RESULTS

We identified 3 distinct heart rate trajectory patterns (persistent-high, moderate-stable, and low-stable). During 3-month follow-up, 103 (31.0%) patients had unfavorable functional outcome and 46 (13.9%) patients died. In multivariable-adjusted model, compared with patients in low-stable trajectory, patients in persistent-high trajectory had the highest odds of poor functional outcome (odds ratio 15.06, 95% CI 3.67-61.78). Higher mean and coefficient of variation of heart rate were also associated with increased risk of unfavorable functional outcome (P trend < 0.05), and the corresponding odds ratios (95% CI) comparing two extreme tertiles were 4.69 (2.04-10.75) and 2.43 (1.09-5.39), respectively. Likewise, similar prognostic effects of heart rate dynamic changes on high modified Rankin Scale score and all-cause mortality were observed.

CONCLUSIONS

Persistently high heart rate and higher variability in the acute phase were associated with increased risk of unfavorable functional outcome in patients with acute ICH.