Heart Rate Variability as an Indicator of Autonomic Nervous System Disturbance in Tetanus

Wearable biosensors in cardiovascular disease

This review provides a comprehensive overview of the latest advancements in wearable biosensors, emphasizing their applications in cardiovascular disease monitoring. Initially, the key sensing signals and biomarkers crucial for cardiovascular health, such as electrocardiogram, phonocardiography, pulse wave velocity, blood pressure, and specific biomarkers, are highlighted. Following this, advanced sensing techniques for cardiovascular disease monitoring are examined, including wearable electrophysiology devices, optical fibers, electrochemical sensors, and implantable cardiac devices. The review also delves into hydrogel-based wearable electrochemical biosensors, which detect biomarkers in sweat, interstitial fluids, saliva, and tears. Further attention is given to flexible electronics-based biosensors, including resistive, capacitive, and piezoelectric force sensors, as well as resistive and pyroelectric temperature sensors, flexible biochemical sensors, and sensor arrays. Moreover, the discussion extends to polymer-based wearable sensors, focusing on innovations in contact lens, textile-type, patch-type, and tattoo-type sensors. Finally, the review addresses the challenges associated with recent wearable biosensing technologies and explores future perspectives, highlighting potential groundbreaking avenues for transforming wearable sensing devices into advanced diagnostic tools with multifunctional capabilities for cardiovascular disease monitoring and other healthcare applications.

Efficacy and Safety of Acupuncture for Restless Legs Syndrome in Patients with End-Stage Renal Disease: A Randomized-Controlled Trial at Hospital-Based Hemodialysis Center

Background: Restless legs syndrome (RLS) is frequent in patients with hemodialysis (HD) and occurs predominantly in its most severe forms. The study was conducted to evaluate the efficacy and safety of acupuncture for RLS in patients with end-stage renal disease (ESRD) at hospital-based HD center. Methods: This single-blind, randomized controlled trial was performed on patients with HD and RLS who were randomly assigned to the experimental group and control group. Data were collected using the International Restless Legs Syndrome Rating Scale (IRLSRS), Insomnia Severity Index (ISI), and heart rate variability (HRV) records at baseline, after the therapeutic course (12 times/4 weeks), and 1-week follow-up. Result: A total of 47 patients were evaluated with IRLSRS score from 11 to 30 in this study. There were 41 patients enrolled in the study based on inclusion/exclusion criteria and allocated randomly into two groups. A total of 35 participants completed the trial, including 18 subjects in the experimental group and 17 subjects in the control group. The comparison of IRLSRS and ISI showed a significant reduction between two groups after acupuncture treatment (p = 0.002, p = 0.003). The ISI after 1-week follow-up also revealed significant decrease (p = 0.003). This HRV results showed that high frequency (HF%) increased significantly (p = 0.021) and low frequency (LF%) decreased significantly in the acupuncture group (p = 0.021). The generalized estimating equation showed that the IRLSRS improved by 2.902 points (p < 0.001) in the acupuncture group compared with the control group and by 1.340 points (p = 0.003) after 1-week follow-up. There were no adverse effects observed during HD in this study. Discussion: The authors conclude that acupuncture could effectively improve the symptoms of RLS significantly. The results from this study provide clinical evidence on the efficacy and safety of acupuncture to treat the patients with RLS at the HD center.

Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease

Hand foot and mouth disease (HFMD) is caused by a variety of enteroviruses, and occurs in large outbreaks in which a small proportion of children deteriorate rapidly with cardiopulmonary failure. Determining which children are likely to deteriorate is difficult and health systems may become overloaded during outbreaks as many children require hospitalization for monitoring. Heart rate variability (HRV) may help distinguish those with more severe diseases but requires simple scalable methods to collect ECG data.We carried out a prospective observational study to examine the feasibility of using wearable devices to measure HRV in 142 children admitted with HFMD at a children's hospital in Vietnam. ECG data were collected in all children. HRV indices calculated were lower in those with enterovirus A71 associated HFMD compared to those with other viral pathogens.HRV analysis collected from wearable devices is feasible in a low and middle income country (LMIC) and may help classify disease severity in HFMD.

Risk assessment of severe adult tetanus using the NLR and AST level and construction of a nomogram prediction model

We sought to examine high-risk factors for severe tetanus, construct a nomogram model, and predict the risk probability of severe tetanus in adult patients to provide a theoretical basis for clinical intervention. Methods: A retrospective analysis was employed in this study, which enrolled 65 adult patients with tetanus diagnosed at the Second Affiliated Hospital of Hainan Medical University from January 2017 to September 2022. Study participants were divided into severe and mild groups based on the Ablett classification. The general data and laboratory markers of both groups were compared, and logistic regression analysis was used to screen for independent risk factors for severe tetanus. A nomogram prediction model was constructed, and receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were constructed and used to assess discrimination, calibration, and net benefit. Results: Of the 65 adults patients with tetanus, 28 were placed in the severe group and 37 were placed in the mild group. Univariate logistic regression analysis showed that there were statistically significant differences in the incubation period, time from disease onset to treatment, white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), platelet count (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lactate dehydrogenase level (LDH), myoglobin level (Mb), and aspartate aminotransferase (AST) level between the two groups (P < 0.05). while the differences in age; sex; and creatine kinase, creatine kinase isoenzyme, and alanine aminotransferase levels were not statistically significant (P > 0.05). Multivariate analysis showed that NLR (odds ratio [OR] = 4.998, 95 % confidence interval [CI] = 1.154-21.649, P = 0.031), AST (OR = 1.074, 95 % CI = 1.007-1.146, P = 0.031), PLT (OR = 1.055, 95 % CI = 1.006-1.106, P = 0.027), and incubation period (OR = 0.597, 95 % CI = 0.423-0.843, P = 0.003) are independent risk factor for severe tetanus. A Nomogram for predicting Severe Tetanus (N-ST) prediction model was constructed based on variables in the multivariate analysis with P < 0.05. The ROC curve showed that the optimal cutoff point was 108.044 points. At this point, the sensitivity was 86.5 %, the specificity was 89.3 %, the area under the ROC curve was 0.936, and model discrimination was good. The calibration curve overlapped with the ideal curve, and the DCA curve showed that the model can provide clinical benefits. Conclusion: NLR, AST, PLT, and incubation period are predictors of severe tetanus. The constructed N-ST model can provide a new, convenient, and rapid method to predict the risk probability of severe tetanus in adults and guide early clinical intervention.

Heart Rate Variability Measured from Wearable Devices as a Marker of Disease Severity in Tetanus

Tetanus is a disease associated with significant morbidity and mortality. Heart rate variability (HRV) is an objective clinical marker with potential value in tetanus. This study aimed to investigate the use of wearable devices to collect HRV data and the relationship between HRV and tetanus severity. Data were collected from 110 patients admitted to the intensive care unit in a tertiary hospital in Vietnam. HRV indices were calculated from 5-minute segments of 24-hour electrocardiogram recordings collected using wearable devices. HRV was found to be inversely related to disease severity. The standard deviation of NN intervals and interquartile range of RR intervals (IRRR) were significantly associated with the presence of muscle spasms; low frequency (LF) and high frequency (HF) indices were significantly associated with severe respiratory compromise; and the standard deviation of differences between adjacent NN intervals, root mean square of successive differences between normal heartbeats, LF to HF ratio, total frequency power, and IRRR, were significantly associated with autonomic nervous system dysfunction. The findings support the potential value of HRV as a marker for tetanus severity, identifying specific indices associated with clinical severity thresholds. Data were recorded using wearable devices, demonstrating this approach in resource-limited settings where most tetanus occurs.

Magnitude, Patterns, and Associated Predictors of Cardiovascular Events in Tetanus: A 2-Year, Single-Center, Ambidirectional Cohort Study Involving 572 Patients

Background

Cardiovascular events (CEs) remain the leading cause of death in patients with tetanus. We examined the incidence, patterns, and associated predictors of CEs among patients with tetanus in Vietnam.

Methods

An ambidirectional cohort study was conducted on hospitalized adult patients with tetanus at the Hospital for Tropical Diseases between 2019 and 2020. Information on demographics, tetanus disease, CEs and outcomes were collected.

Results

Among all 572 included patients, CEs accounted for 10.8% (95%CI 8.6-13.7%) and included Takotsubo cardiomyopathy (40.3%, 95%CI 29.0-52.8%), arrhythmia (19.4%, 95%CI 11.4-30.9%), sudden cardiac arrest (16.1%, 95%CI 9.0-27.2%), myocardial infarction (11.3%, 95%CI 5.6-21.5%), heart failure (6.5%, 95%CI 2.5-15.4%) and pulmonary embolism (6.5%, 95%CI 2.5-15.4%). CEs occurred from day 5 to 20 of illness. Among 62 CE patients, 21% (95%CI 12.7-32.6%) died and 61.3% (95%CI 48.9-72.4%) developed autonomic nervous system dysfunction (ANSD). Three-fourths (24/32) of patients with Takotsubo cardiomyopathy or myocardial infarction had ANSD. CEs were significantly associated with modified Ablett scores (AOR = 2.42, 95%CI 1.1-5.6, P = .04), underlying diseases (AOR = 2.7, 95%CI 1.1-6.8, P = .04) and overweight (AOR = 0.18, 95%CI .04-.8, P = .02).

Conclusions

CEs are not rare and associated with high mortality. The most common CE is Takotsubo cardiomyopathy. CEs can occur at any stage of illness, with or without ANSD. To prevent mortality, it is pivotal to screen CEs in patients with tetanus, especially those with underlying diseases, high modified Ablett scores, and a normal or low BMI. More studies are needed to fully elucidate the impact of ANSD on the cardiovascular function and the CE associated mortality in tetanus.

2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries

Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging-continuous wavelet transforms-is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00.

Recent Advances in Cardiovascular Disease Biosensors and Monitoring Technologies

Cardiovascular disease (CVD) causes significant mortality and remains the leading cause of death globally. Thus, to reduce mortality, early diagnosis by measurement of cardiac biomarkers and heartbeat signals presents fundamental importance. Traditional CVD examination requires bulky hospital instruments to conduct electrocardiography recording and immunoassay analysis, which are both time-consuming and inconvenient. Recently, development of biosensing technologies for rapid CVD marker screening attracted great attention. Thanks to the advancement in nanotechnology and bioelectronics, novel biosensor platforms are developed to achieve rapid detection, accurate quantification, and continuous monitoring throughout disease progression. A variety of sensing methodologies using chemical, electrochemical, optical, and electromechanical means are explored. This review first discusses the prevalence and common categories of CVD. Then, heartbeat signals and cardiac blood-based biomarkers that are widely employed in clinic, as well as their utilizations for disease prognosis, are summarized. Emerging CVD wearable and implantable biosensors and monitoring bioelectronics, allowing these cardiac markers to be continuously measured are introduced. Finally, comparisons of the pros and cons of these biosensing devices along with perspectives on future CVD biosensor research are presented.

Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing

Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible. Machine learning analysis of ECG has been shown of extra value in predicting tetanus severity, however any additional ECG signal analysis places a high demand on time-limited hospital staff and requires specialist equipment. Therefore, we present a novel approach to tetanus monitoring from low-cost wearable sensors combined with a deep-learning-based automatic severity detection. This approach can automatically triage tetanus patients and reduce the burden on hospital staff. In this study, we propose a two-dimensional (2D) convolutional neural network with a channel-wise attention mechanism for the binary classification of ECG signals. According to the Ablett classification of tetanus severity, we define grades 1 and 2 as mild tetanus and grades 3 and 4 as severe tetanus. The one-dimensional ECG time series signals are transformed into 2D spectrograms. The 2D attention-based network is designed to extract the features from the input spectrograms. Experiments demonstrate a promising performance for the proposed method in tetanus classification with an F1 score of 0.79 ± 0.03, precision of 0.78 ± 0.08, recall of 0.82 ± 0.05, specificity of 0.85 ± 0.08, accuracy of 0.84 ± 0.04 and AUC of 0.84 ± 0.03.

The management of tetanus in adults in an intensive care unit in Southern Vietnam

Background: Tetanus remains common in many low- and middle-income countries (LMICs) yet the evidence base guiding management of this disease is extremely limited, particularly with respect to contemporary management options. Sharing knowledge about practice may facilitate improvement in outcomes elsewhere.

Methods: We describe clinical interventions and outcomes of 180 adult patients ≥16 years-old with tetanus enrolled in prospective observational studies at a specialist infectious diseases hospital in Southern Vietnam. Patients were treated according to a holistic management protocol encompassing wound-care, antitoxin, antibiotics, symptom control, airway management, nutrition and de-escalation criteria.

Results: Mortality rate in our cohort was 2.8%, with 90 (50%) patients requiring mechanical ventilation for a median 16 [IQR 12-24] days. Median [IQR] duration of ICU stay was 15 [8-23] days.  Autonomic nervous system dysfunction occurred in 45 (25%) patients. Hospital acquired infections occurred in 77 (43%) of patients.

Conclusion: We report favourable outcomes for patients with tetanus in a single centre LMIC ICU, treated according to a holistic protocol. Nevertheless, many patients required prolonged intensive care support and hospital acquired infections were common.

The management of tetanus in adults in an intensive care unit in Southern Vietnam

Background: Tetanus remains common in many low- and middle-income countries (LMICs) yet the evidence base guiding management of this disease is extremely limited, particularly with respect to contemporary management options. Sharing knowledge about practice may facilitate improvement in outcomes elsewhere. Methods: We describe clinical interventions and outcomes of 180 adult patients ≥16 years-old with tetanus enrolled in prospective observational studies at a specialist infectious diseases hospital in Southern Vietnam. Patients were treated according to a holistic management protocol encompassing wound-care, antitoxin, antibiotics, symptom control, airway management, nutrition and de-escalation criteria. Results: Mortality rate in our cohort was 2.8%, with 90 (50%) patients requiring mechanical ventilation for a median 16 [IQR 12-24] days. Median [IQR] duration of ICU stay was 15 [8-23] days.  Autonomic nervous system dysfunction occurred in 45 (25%) patients. Hospital acquired infections occurred in 77 (43%) of patients. Conclusion: We report favourable outcomes for patients with tetanus in a single centre LMIC ICU, treated according to a holistic protocol. Nevertheless, many patients required prolonged intensive care support and hospital acquired infections were common.

Severity detection tool for patients with infectious disease

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.