Admission Heart Rate Variability is Associated with Fever Development in Patients with Intracerebral Hemorrhage

Autonomic dysfunction as a predictor of infection in neurocritical care unit: a prospective cohort study

PURPOSE

Infection in the neurocritical care unit ( NCCU) can cause significant mortality and morbidity. Autonomic nervous system plays an important role in defense against infection. Autonomic dysfunction causing inflammatory dysregulation can potentiate infection. We aimed to study the relationship between autonomic dysfunction and occurrence of infection in neurologically ill patients.

METHODS

Fifty one patients who were on mechanical ventilation were prospectively enrolled in this study. Autonomic dysfunction was measured for three consecutive days on admission to NCCU using Ansiscope. Patients were followed up for seven days to see the occurrence of infection. Infection was defined as per centre of disease control definition.

RESULTS

A total of 386 patients were screened for eligibility. 68 patients satisfied the eligibility criteria and 51 patients were finally included in the study. The incidence of infection was 74.5%. The commonest infection was pulmonary infection (38.8%) followed by urinary tract infection (33.3%), blood stream infection(14.8%), central nervous system infection (11.1%) and wound site infection (3.7%). The degree of autonomic dysfunction (AD) percentage was more in infection group (37.7% (25.2-49.7)) compared to non infection group (23.5% (18-33.5)) and maximal on day 3 (P = 0.02). Patients with increasing trend of AD% from day 1 to day 3 had the highest infection rates. The length of NCCU stay (20(10-23) days and mortality (42.1%) was higher in infection group (p < 0.001).

CONCLUSION

AD assessment can be used as a tool to predict development of infection in NCCU. This can help triage and institute early investigation and treatment.

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.

Heart Rate Variability in Patients with Spontaneous Intracerebral Hemorrhage and its Relationship with Clinical Outcomes

BACKGROUND

Although abnormal heart rate variability (HRV) is frequently observed in patients with spontaneous intracerebral hemorrhage (ICH), its time course and presentation of different indices remain unclear, and few studies have focused on its association with clinical outcomes.

METHODS

We prospectively recruited consecutive patients with spontaneous ICH between June 2014 and June 2021. HRV was evaluated twice during hospitalization (within 7 days and 10-14 days after stroke). Time and frequency domain indices were calculated. A modified Rankin Scale score ≥ 3 at 3 months was defined as a poor outcome.

RESULTS

Finally, 122 patients with ICH and 122 age- and sex-matched volunteers were included. Compared with controls, time domain and absolute frequency domain HRV parameters (total power, low frequency [LF], and high frequency [HF]) in the ICH group were significantly decreased within 7 days and 10-14 days. For relative values, normalized LF (LF%) and LF/HF were significantly higher, whereas normalized HF (HF%) was significantly lower, in the patient group than in the control group. Furthermore, LF% and HF% measured at 10-14 days were independently associated with 3-month outcomes.

CONCLUSIONS

HRV values were impaired significantly within 14 days after ICH. Furthermore, HRV indices measured 10-14 days after ICH were independently associated with 3-month outcomes.

Autonomic dysfunction and treatment strategies in intracerebral hemorrhage

AIMS

Autonomic dysfunction with central autonomic network (CAN) damage occurs frequently after intracerebral hemorrhage (ICH) and contributes to a series of adverse outcomes. This review aims to provide insight and convenience for future clinical practice and research on autonomic dysfunction in ICH patients.

DISCUSSION

We summarize the autonomic dysfunction in ICH from the aspects of potential mechanisms, clinical significance, assessment, and treatment strategies. The CAN structures mainly include insular cortex, anterior cingulate cortex, amygdala, hypothalamus, nucleus of the solitary tract, ventrolateral medulla, dorsal motor nucleus of the vagus, nucleus ambiguus, parabrachial nucleus, and periaqueductal gray. Autonomic dysfunction after ICH is closely associated with neurological functional outcomes, cardiac complications, blood pressure fluctuation, immunosuppression and infection, thermoregulatory dysfunction, hyperglycemia, digestive dysfunction, and urogenital disturbances. Heart rate variability, baroreflex sensitivity, skin sympathetic nerve activity, sympathetic skin response, and plasma catecholamine concentration can be used to assess the autonomic functional activities after ICH. Risk stratification of patients according to autonomic functional activities, and development of intervention approaches based on the restoration of sympathetic-parasympathetic balance, would potentially improve clinical outcomes in ICH patients.

CONCLUSION

The review systematically summarizes the evidence of autonomic dysfunction and its association with clinical outcomes in ICH patients, proposing that targeting autonomic dysfunction could be potentially investigated to improve the clinical outcomes.

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.

Evaluation of skin sympathetic nervous activity for classification of intracerebral hemorrhage and outcome prediction

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals. A customized measurement device presented in our previous papers was used to collect data. 117 subjects (50 healthy control subjects and 67 ICH patients) were recruited for this study to obtain their 5-min electrocardiogram (ECG) and SKNA signals. We extracted the signal's time-domain, frequency-domain, and nonlinear features and analyzed their differences between healthy control subjects and ICH patients. Subsequently, we established the ICH classification and outcome evaluation model based on the eXtreme Gradient Boosting (XGBoost). In addition, heart rate variability (HRV) as an ANS assessment method was also included as a comparison method in this study. The results showed significant differences in most features of the SKNA signal between healthy control subjects and ICH patients. The ICH patients with good outcomes have a higher change rate and complexity of SKNA signal than those with bad outcomes. In addition, the accuracy of the model for ICH classification and outcome prediction based on the SKNA signal was more than 91% and 83%, respectively. The ICH classification and outcome prediction based on the SKNA signal proved to be a feasible method in this study. Furthermore, the features of change rate and complexity, such as entropy measures, can be used to characterize the difference in SKNA signals of different groups. The method can potentially provide a new tool for rapid classification and outcome prediction of ICH patients. Index Terms-intracerebral hemorrhage (ICH), skin sympathetic nervous activity (SKNA), classification, outcome prediction, cardiovascular and cerebrovascular diseases.

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.

Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability

Objectives

Neonatal early onset sepsis (EOS), bacterial infection during the first seven days of life, is difficult to diagnose because presenting signs are non-specific, but early diagnosis before birth can direct life-saving treatment for mother and baby. Specifically, maternal fever during labor from placental infection is the strongest predictor of EOS. Alterations in maternal heart rate variability (HRV) may precede development of intrapartum fever, enabling incipient EOS detection. The objective of this work was to build a predictive model for intrapartum fever.

Methods

Continuously measured temperature, heart rate, and beat-to-beat RR intervals were obtained from wireless sensors on women (n = 141) in labor; traditional manual vital signs were taken every 3-6 hours. Validated measures of HRV were calculated in moving 5-minute windows of RR intervals: standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD) between normal heartbeats.

Results

Fever (>38.0 °C) was detected by manual or continuous measurements in 48 women. Compared to afebrile mothers, average SDNN and RMSSD in febrile mothers decreased significantly (p < 0.001) at 2 and 3 hours before fever onset, respectively. This observed HRV divergence and raw recorded vitals were applied to a logistic regression model at various time horizons, up to 4-5 hours before fever onset. Model performance increased with decreasing time horizons, and a model built using continuous vital signs as input variables consistently outperformed a model built from episodic vital signs.

Conclusions

HRV-based predictive models could identify mothers at risk for fever and infants at risk for EOS, guiding maternal antibiotic prophylaxis and neonatal monitoring.

Metoprolol attenuates intracerebral hemorrhage-induced cardiac damage by suppression of sympathetic overactivity in mice

The high rates of mortality and disability resulting from intracerebral hemorrhage (ICH) are closely related to subsequent cardiac complications. The mechanisms underlying ICH-induced cardiac dysfunction are not fully understood. In this study, we investigated the role of sympathetic overactivity in mediating cardiac dysfunction post ICH in mice. Collagenase-injection ICH model was established in adult male C57BL/6J mice. Neurological function was subsequently evaluated at multiple time points after ICH and cardiac function was measured by echocardiography on 3 and 14 days after ICH. Plasma adrenaline, noradrenaline, cortisol and heart β1 adrenergic receptor (β1-AR) levels were assessed to evaluate sympathetic activity. Picro Sirius Red (PSR) staining was performed to evaluate cardiomyocyte hypertrophy and interstitial fibrosis. Monocyte chemotactic protein 1 (MCP-1), tumor necrosis factor-alpha (TNF-α), interleukin-6(IL-6), nuclear factor kappa-B(NF-κB), NADPH oxidase-2 (NOX2), matrix metalloprotein (MMP-9) and transforming growth factor-beta (TGF-β) levels were assessed to evaluate inflammation, fibrosis and oxidative stress levels in heart after ICH. Macrophages and neutrophils were assessed to evaluate inflammatory cell infiltration in heart after ICH. ICH induced sympathetic excitability, as identified by increased circulating adrenaline, noradrenaline, cortisol levels and β1-AR expression in heart tissue. Metoprolol-treated ICH mice had improved cardiac and neurological function. The suppression of sympathetic overactivity by metoprolol attenuates cardiac inflammation, fibrosis and oxidative stress after ICH. In conclusion, ICH-induced secondary sympathetic overactivity which mediated inflammatory response may play an important role in post-ICH cardiac dysfunction.

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.

Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care

Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners. In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.