Heart rate variability monitoring for the detection of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

The effect of transcutaneous auricular vagus nerve stimulation on cardiovascular function in subarachnoid hemorrhage patients: a safety study

Introduction

Subarachnoid hemorrhage (SAH) is characterized by intense central inflammation, leading to substantial post-hemorrhagic complications such as vasospasm and delayed cerebral ischemia. Given the anti-inflammatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) and its ability to promote brain plasticity, taVNS has emerged as a promising therapeutic option for SAH patients. 3,10,13 However, the effects of taVNS on cardiovascular dynamics in critically ill patients like those with SAH have not yet been investigated. Given the association between cardiac complications and elevated risk of poor clinical outcomes after SAH, it is essential to characterize the cardiovascular effects of taVNS to ensure this approach is safe in this fragile population 5 . Therefore, we assessed the impact of both acute taVNS and repetitive taVNS on cardiovascular function in this study.

Methods

In this randomized clinical trial, 24 SAH patients were assigned to either a taVNS treatment or a Sham treatment group. During their stay in the intensive care unit, we monitored patient electrocardiogram (ECG) readings and vital signs. We compared long-term changes in heart rate, heart rate variability, QT interval, and blood pressure between the two groups. Additionally, we assessed the effects of acute taVNS by comparing cardiovascular metrics before, during, and after the intervention. We also explored rapidly responsive cardiovascular biomarkers in patients exhibiting clinical improvement.

Results

We found that repetitive taVNS did not significantly alter heart rate, corrected QT interval, blood pressure, or intracranial pressure. However, taVNS increased overall heart rate variability and parasympathetic activity from 5-10 days after initial treatment, as compared to the sham treatment. Acutely, taVNS increased heart rate, blood pressure, and peripheral perfusion index without affecting the corrected QT interval, intracranial pressure, or heart rate variability. The acute post-treatment elevation in heart rate was more pronounced in patients who experienced a decrease of more than 1 point in their Modified Rankin Score at the time of discharge.

Conclusions

Our study found that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation, supporting its development as a safe immunomodulatory treatment approach for SAH patients. The observed acute increase in heart rate after taVNS treatment may serve as a biomarker for SAH patients who could derive greater benefit from this treatment. Trial registration: NCT04557618.

Discriminatory ability of perioperative heart rate variability in predicting postoperative complications in major urologic surgery: a prospective cohort study

We aimed to determine if continuous perioperative heart rate variability (HRV) monitoring could improve risk stratification compared to a short preoperative measurement in radical cystectomy patients. Electrocardiography (ECG) recordings were collected continuously preoperatively to discharge in 83 patients. Two, 5-min ECG signal segments (preoperative and at 24-h post ECG placement) were analyzed offline to extract HRV metrics. HRV metric discriminatory ability to identify patients with 30-day postoperative complications were analyzed using receiver operating characteristics curves. Sixty participants were included for analysis of which 27 (45%) developed a complication within 30 days postoperative. HRV was reduced in patients with complications. Postoperative standard deviation NN intervals and root mean square of successive differences had area under the curves (AUC) of 0.67 (95% CI 0.54 to 0.81) and 0.68 (95% CI 0.54 to 0.82), respectively. Significant discriminatory abilities were also reported for postoperative frequency metrics of absolute low frequency (LF) [AUC = 0.65 (95% CI 0.51 to 0.79)] and high frequency (HF) powers [AUC = 0.69 (95% CI 0.55 to 0.83)] and total power [AUC = 0.66 (95% CI 0.53 to 0.80)]. Postoperative acquired HRV metrics demonstrated improved discriminatory ability. Our findings suggest that longer-term perioperative HRV monitoring presents with superior ability to stratify complication risk.

Sex-Related Differences in Mortality, Delayed Cerebral Ischemia, and Functional Outcomes in Patients with Aneurysmal Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis

Background/Objective: Sex-related differences among patients with aneurysmal subarachnoid hemorrhage (aSAH) and their potential clinical implications have been insufficiently investigated. To address this knowledge gap, we conduct a comprehensive systematic review and meta-analysis. Methods: Sex-specific differences in patients with aSAH, including mortality, delayed cerebral ischemia (DCI), and functional outcomes were assessed. The functional outcome was dichotomized into favorable or unfavorable based on the modified Rankin Scale (mRS), Glasgow Outcome Scale (GOS), and Glasgow Outcome Scale Extended (GOSE). Results: Overall, 2823 studies were identified in EMBASE, MEDLINE, PubMed, and by manual search on 14 February 2024. After an initial assessment, 74 studies were included in the meta-analysis. In the analysis of mortality, including 18,534 aSAH patients, no statistically significant differences could be detected (risk ratio (RR) 0.99; 95% CI, 0.90-1.09; p = 0.91). In contrast, the risk analysis for DCI, including 23,864 aSAH patients, showed an 11% relative risk reduction in DCI in males versus females (RR, 0.89; 95% CI, 0.81-0.97; p = 0.01). The functional outcome analysis (favorable vs. unfavorable), including 7739 aSAH patients, showed a tendency towards better functional outcomes in men than women; however, this did not reach statistical significance (RR, 1.02; 95% CI, 0.98-1.07; p = 0.34). Conclusions: In conclusion, the available data suggest that sex/gender may play a significant role in the risk of DCI in patients with aSAH, emphasizing the need for sex-specific management strategies.

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.

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.

Cerebrospinal Fluid and Serum Biomarker Insights in Aneurysmal Subarachnoid Haemorrhage: Navigating the Brain-Heart Interrelationship for Improved Patient Outcomes

The pathophysiological mechanisms underlying severe cardiac dysfunction after aneurysmal subarachnoid haemorrhage (aSAH) remain poorly understood. In the present study, we focused on two categories of contributing factors describing the brain-heart relationship. The first group includes brain-specific cerebrospinal fluid (CSF) and serum biomarkers, as well as cardiac-specific biomarkers. The secondary category encompasses parameters associated with cerebral autoregulation and the autonomic nervous system. A group of 15 aSAH patients were included in the analysis. Severe cardiac complications were diagnosed in seven (47%) of patients. In the whole population, a significant correlation was observed between CSF S100 calcium-binding protein B (S100B) and brain natriuretic peptide (BNP) (rS = 0.62; p = 0.040). Additionally, we identified a significant correlation between CSF neuron-specific enolase (NSE) with cardiac troponin I (rS = 0.57; p = 0.025) and BNP (rS = 0.66; p = 0.029), as well as between CSF tau protein and BNP (rS = 0.78; p = 0.039). Patients experiencing severe cardiac complications exhibited notably higher levels of serum tau protein at day 1 (0.21 ± 0.23 [ng/mL]) compared to those without severe cardiac complications (0.03 ± 0.04 [ng/mL]); p = 0.009. Impaired cerebral autoregulation was noted in patients both with and without severe cardiac complications. Elevated serum NSE at day 1 was related to impaired cerebral autoregulation (rS = 0.90; p = 0.037). On the first day, a substantial, reciprocal correlation between heart rate variability low-to-high frequency ratio (HRV LF/HF) and both GFAP (rS = -0.83; p = 0.004) and S100B (rS = -0.83; p = 0.004) was observed. Cardiac and brain-specific biomarkers hold the potential to assist clinicians in providing timely insights into cardiac complications, and therefore they contribute to the prognosis of outcomes.

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.

Long-term outcomes after aneurysmal subarachnoid hemorrhage: A prospective observational cohort study

OBJECTIVES

The survival rates for patients affected by aneurysmal subarachnoid hemorrhage (aSAH) have increased in recent years; however, many patients continue to develop cognitive dysfunctions that affect their quality of life. The commonly used outcome measures often fail to identify these cognitive dysfunctions. This study aimed to evaluate the long-term outcomes at 1 and 3 years after aSAH to assess changes over time and relate outcomes to patient characteristics and events during the acute phase.

MATERIALS AND METHODS

This prospective observational study included patients that experienced aSAH. Patients were assessed according to the extended Glasgow Outcome Scale, Life Satisfaction Questionnaire, Mayo-Portland Adaptability inventory-4, and Mental Fatigue scale.

RESULTS

Patients were assessed after 1 year (n = 62) and 3 years (n = 54). At 3 years, the extended Glasgow Outcome Scale score improved in 15% and worsened in 12% of the patients. Mental fatigue was observed in 57% of the patients at 1 year. Patients <60 years of age at the time of aSAH had more self-assessed problems, including pain/headache (p < .01), than patients >60 years of age. Patients with delayed cerebral ischemia during the acute phase reported more dissatisfaction at 3 years, whereas no significant result was seen at 1 year.

CONCLUSIONS

Cognitive dysfunction, especially mental fatigue, is common in patients with aSAH, which affects quality of life and recovery. Patient outcome is a dynamic process developing throughout years after aSAH, involving both improvement and deterioration. This study indicates the importance of longer follow-up periods with broad outcome assessments.

Machine learning analysis of heart rate variability to detect delayed cerebral ischemia in subarachnoid hemorrhage

OBJECTIVES

Approximately 30% of patients with aneurysmal subarachnoid hemorrhage (aSAH) develop delayed cerebral ischemia (DCI). DCI is associated with increased mortality and persistent neurological deficits. This study aimed to analyze heart rate variability (HRV) data from patients with aSAH using machine learning to evaluate whether specific patterns could be found in patients developing DCI.

MATERIAL & METHODS

This is an extended, in-depth analysis of all HRV data from a previous study wherein HRV data were collected prospectively from a cohort of 64 patients with aSAH admitted to Sahlgrenska University Hospital, Gothenburg, Sweden, from 2015 to 2016. The method used for analyzing HRV is based on several data processing steps combined with the random forest supervised machine learning algorithm.

RESULTS

HRV data were available in 55 patients, but since data quality was significantly low in 19 patients, these were excluded. Twelve patients developed DCI. The machine learning process identified 71% of all DCI cases. However, the results also demonstrated a tendency to identify DCI in non-DCI patients, resulting in a specificity of 57%.

CONCLUSIONS

These data suggest that machine learning applied to HRV data might help identify patients with DCI in the future; however, whereas the sensitivity in the present study was acceptable, the specificity was low. Possible confounders such as severity of illness and therapy may have affected the result. Future studies should focus on developing a robust method for detecting DCI using real-time HRV data and explore the limits of this technology in terms of its reliability and accuracy.

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.

Cerebral ischemia detection using artificial intelligence (CIDAI)-A study protocol

BACKGROUND

The onset of cerebral ischemia is difficult to predict in patients with altered consciousness using the methods available. We hypothesize that changes in Heart Rate Variability (HRV), Near-Infrared Spectroscopy (NIRS), and Electroencephalography (EEG) correlated with clinical data and processed by artificial intelligence (AI) can indicate the development of imminent cerebral ischemia and reperfusion, respectively. This study aimed to develop a method that enables detection of imminent cerebral ischemia in unconscious patients, noninvasively and with the support of AI.

METHODS

This prospective observational study will include patients undergoing elective surgery for carotid endarterectomy and patients undergoing acute endovascular embolectomy for cerebral arterial embolism. HRV, NIRS, and EEG measurements and clinical information on patient status will be collected and processed using machine learning. The study will take place at Sahlgrenska University Hospital, Gothenburg, Sweden. Inclusion will start in September 2020, and patients will be included until a robust model can be constructed. By analyzing changes in HRV, EEG, and NIRS measurements in conjunction with cerebral ischemia or cerebral reperfusion, it should be possible to train artificial neural networks to detect patterns of impending cerebral ischemia. The analysis will be performed using machine learning with long short-term memory artificial neural networks combined with convolutional layers to identify patterns consistent with cerebral ischemia and reperfusion.

DISCUSSION

Early signs of cerebral ischemia could be detected more rapidly by identifying patterns in integrated, continuously collected physiological data processed by AI. Clinicians could then be alerted, and appropriate actions could be taken to improve patient outcomes.