Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities.

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Cardiac contractility modulation in patients with heart failure - A review of the literature.

Experimental in vivo and in vitro studies showed that electric currents applied during the absolute refractory period can modulate cardiac contractility. In preclinical studies, cardiac contractility modulation (CCM) was found to improve calcium handling, reverse the foetal myocyte gene programming associated with heart failure (HF), and facilitate reverse remodeling. Randomized control trials and observational studies have provided evidence about the safety and efficacy of CCM in patients with HF. Clinically, CCM therapy is indicated to improve the 6-min hall walk, quality of life, and functional status of HF patients who remain symptomatic despite guideline-directed medical treatment without an indication for cardiac resynchronization therapy (CRT) and have a left ventricular ejection fraction (LVEF) ranging from 25 to 45%. Although there are promising results about the role of CCM in HF patients with preserved LVEF (HFpEF), further studies are needed to elucidate the role of CCM therapy in this population. Late gadolinium enhancement (LGE) assessment before CCM implantation has been proposed for guiding the lead placement. Furthermore, the optimal duration of CCM application needs further investigation. This review aims to present the existing evidence regarding the role of CCM therapy in HF patients and identify gaps and challenges that require further studies.

Vagus Nerve Stimulation and Inflammation in Cardiovascular Disease: A State-of-the-Art Review.

Vagus nerve stimulation (VNS) has been found to exert anti-inflammatory effects in different clinical settings and has been associated with improvement of clinical outcomes. However, evidence on the mechanistic link between the potential association of inflammatory status with clinical outcomes following VNS is scarce. This review aims to summarize the existing knowledge linking VNS with inflammation and its potential link with major outcomes in cardiovascular diseases, in both preclinical and clinical studies. Existing data show that in the setting of myocardial ischemia and reperfusion, VNS seems to reduce inflammation resulting in reduced infarct size and reduced incidence of ventricular arrhythmias during reperfusion. Furthermore, VNS has a protective role in vascular function following myocardial ischemia and reperfusion. Atrial fibrillation burden has also been reduced by VNS, whereas suppression of inflammation may be a potential mechanism for this effect. In the setting of heart failure, VNS was found to improve systolic function and reverse cardiac remodeling. In summary, existing experimental data show a reduction in inflammatory markers by VNS, which may cause improved clinical outcomes in cardiovascular diseases. However, more data are needed to evaluate the association between the inflammatory status with the clinical outcomes following VNS.

Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association.

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.

Autonomic Nervous System and Cardiac Metabolism: Links Between Autonomic and Metabolic Remodeling in Atrial Fibrillation.

Simultaneous activation of the sympathetic and parasympathetic nervous systems is crucial for the initiation of paroxysmal atrial fibrillation (AF). However, unbalanced activation of the sympathetic system is characteristic of autonomic remodeling in long-standing persistent AF. Moreover, the adrenergic activation-induced metabolic derangements provide a milieu for acute AF and promote the transition from the paroxysmal to the persistent phase of AF. On the other hand, cholinergic activation ameliorates the maladaptive metabolic remodeling in the face of metabolic challenges. Selective inhibition of the sympathetic system and restoration of the balance of the cholinergic system by neuromodulation is emerging as a novel nonpharmacologic strategy for managing AF. This review explores the link between cardiac autonomic and metabolic remodeling and the potential roles of different autonomic modulation strategies on atrial metabolic aberrations in AF.

Application of artificial intelligence in the diagnosis of sleep apnea.

STUDY OBJECTIVES: Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS: A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS: Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS: The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION: Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

Fractal dynamics of individual mitochondrial oscillators measure local inter-mitochondrial coupling.

Mitochondrial inner membrane potentials in cardiomyocytes may oscillate in cycles of depolarization/repolarization when the mitochondrial network is exposed to metabolic or oxidative stress. The frequencies of such oscillations are dynamically changing while clusters of weakly coupled mitochondrial oscillators adjust to a common phase and frequency. Across the cardiac myocyte, the averaged signal of the mitochondrial population follows self-similar or fractal dynamics; however, fractal properties of individual mitochondrial oscillators have not yet been examined. We show that the largest synchronously oscillating cluster exhibits a fractal dimension, D, that is indicative of self-similar behavior with D=1.27±0.11, in contrast to the remaining network mitochondria whose fractal dimension is close to that of Brownian noise, D=1.58±0.10. We further demonstrate that fractal behavior is correlated with local coupling mechanisms, whereas it is only weakly linked to measures of functional connections between mitochondria. Our findings suggest that individual mitochondrial fractal dimensions may serve as a simple measure of local mitochondrial coupling.

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis.

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

Investigating Electrophysiological Markers of Arrhythmogenesis in a Chronic Myocardial Infarction Ovine Model.

Cardiac alternans has been associated with an increased propensity to lethal tachyarrhythmias such as ventricular tachycardia and fibrillation (VT/VF). Myocardial infarction (MI), resulting from restricted oxygen supply to the heart, is a known substrate for VT/VF. Here, we investigate the utility of cardiac alternans as a predictor of tachyarrhythmias in a chronic MI ovine model. In-vivo electrophysiological studies were performed to assess the change in microvolt T-wave alternans (TWA) with induction of acute ischemia following coronary artery occlusion. 24-hour telemetry was performed in an ambulatory animal for 6 weeks to monitor the progression of TWA with chronic MI. At 6 weeks, ex-vivo optical mapping experiments were performed to assess the spatiotemporal evolution of alternans in sham (n=5) and chronic MI hearts (n=8). Our results demonstrate that chronic MI leads to significant electrophysiological changes in the cardiac substrate. Significant increase in TWA is observed post occlusion and a steady rise in alternans is seen with progression of chronic MI. Compared to sham, chronic MI hearts show significant presence of localized action potential amplitude alternans, which spatially evolve with an increase in pacing frequency. Clinical Relevance - Our results demonstrate that localized alternans underlie arrhythmogenesis in chronic MI hearts and microvolt TWA can serve as a biomarker of disease progression during chronic MI.

RespiCo: A novel, flexible, and stand-alone electronic respiratory coaching device.

Conscious respiratory pattern and rate control is desired by patients with some forms of pulmonary disease that are undergoing respiratory muscle conditioning and rehabilitation, by practitioners of meditation hoping to improve mindfulness and wellbeing, by athletes striving to obtain breathing control in order to increase competitiveness, and by engineers and scientists that wish to use the data from breathing subjects to test hypotheses and develop physiological monitoring systems. Although prerecorded audio sources and computer applications are available that guide breathing exercises, they often suffer from being inflexible and allow only limited customization of the breathing cues. Here we describe a small, lightweight, battery-powered, microprocessor-based respiratory coaching device (RespiCo), which through wireless or wired connections, can be easily customized to precisely guide subjects to breathe at desired respiratory rates using specific breathing patterns through visual, auditory, or haptic cues. Digital signals can also be captured from the device to document the breathing cues provided by the device for research purposes. It is anticipated that this device will have important utility for those who wish to be guided to breathe in a precise manner or in research and development of physiologic monitoring systems.

Machine learning techniques for arrhythmic risk stratification: a review of the literature.

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians' unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit.

Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963-0.972), heart failure AUC of 0.838 (CI: 0.825-0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821-0.842), pulmonary embolism AUC of 0.802 (CI: 0.764-0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499-2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

The inclusion of augmented intelligence in medicine: A framework for successful implementation.

Artificial intelligence (AI) algorithms are being applied across a large spectrum of everyday life activities. The implementation of AI algorithms in clinical practice has been met with some skepticism and concern, mainly because of the uneasiness that stems, in part, from a lack of understanding of how AI operates, together with the role of physicians and patients in the decision-making process; uncertainties regarding the reliability of the data and the outcomes; as well as concerns regarding the transparency, accountability, liability, handling of personal data, and monitoring and system upgrades. In this viewpoint, we take these issues into consideration and offer an integrated regulatory framework to AI developers, clinicians, researchers, and regulators, aiming to facilitate the adoption of AI that rests within the FDA's pathway, in research, development, and clinical medicine.

Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks.

Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life-threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid- convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5-fold cross-validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.

Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm.

AIMS: This work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. METHODS AND RESULTS: We use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%. CONCLUSIONS: In conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.

Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients.

The pandemic has brought to everybody's attention the apparent need of remote monitoring, highlighting hitherto unseen challenges in healthcare. Today, mobile monitoring and real-time data collection, processing and decision-making, can drastically improve the cardiorespiratory-haemodynamic health diagnosis and care, not only in the rural communities, but urban ones with limited healthcare access as well. Disparities in socioeconomic status and geographic variances resulting in regional inequity in access to healthcare delivery, and significant differences in mortality rates between rural and urban communities have been a growing concern. Evolution of wireless devices and smartphones has initiated a new era in medicine. Mobile health technologies have a promising role in equitable delivery of personalized medicine and are becoming essential components in the delivery of healthcare to patients with limited access to in-hospital services. Yet, the utility of portable health monitoring devices has been suboptimal due to the lack of user-friendly and computationally efficient physiological data collection and analysis platforms. We present a comprehensive review of the current cardiac, pulmonary, and haemodynamic telemonitoring technologies. We also propose a novel low-cost smartphone-based system capable of providing complete cardiorespiratory assessment using a single platform for arrhythmia prediction along with detection of underlying ischaemia and sleep apnoea; we believe this system holds significant potential in aiding the diagnosis and treatment of cardiorespiratory diseases, particularly in underserved populations.

Microvolt T-Wave Alternans Is Modulated by Acute Low-Level Tragus Stimulation in Patients With Ischemic Cardiomyopathy and Heart Failure.

Aims: Microvolt T-wave alternans (TWA), an oscillation in T-wave morphology of the electrocardiogram (ECG), has been associated with increased susceptibility to ventricular tachy-arrhythmias, while vagus nerve stimulation has shown promising anti-arrhythmic effects in in vivo and ex vivo animal studies. We aimed to examine the effect of non-invasive, acute low-level tragus stimulation (LLTS) on TWA in patients with ischemic cardiomyopathy and heart failure. Methods: 26 patients with ischemic cardiomyopathy (left ventricular ejection fraction <35%) and chronic stable heart failure, previously implanted with an automatic implantable cardioverter defibrillator (ICD) device with an atrial lead (dual chamber ICD or cardiac resynchronization therapy defibrillator), were enrolled in the study. Each patient sequentially received, (1) Sham LLTS (electrode on tragus, but no stimulation delivered) for 5 min; (2) Active LLTS at two different frequencies (5 and 20 Hz, 15 min each); and (3) Active LLTS, during concomitant atrial pacing at 100 bpm at two different frequencies (5 and 20 Hz, 15 min each). LLTS was delivered through a transcutaneous electrical nerve stimulation device (pulse width 200 µs, frequency 5/20 Hz, amplitude 1 mA lower than the discomfort threshold). TWA burden was assessed using continuous ECG monitoring during sham and active LLTS in sinus rhythm, as well as during atrial pacing. Results: Right atrial pacing at 100 bpm led to significantly heightened TWA burden compared to sinus rhythm, with or without LLTS. Acute LLTS at both 5 and 20 Hz, during sinus rhythm led to a significant rise in TWA burden in the precordial leads (p < 0.05). Conclusion: Acute LLTS results in a heart-rate dependent increase in TWA burden.

Low-Level Tragus Stimulation Modulates Atrial Alternans and Fibrillation Burden in Patients With Paroxysmal Atrial Fibrillation.

Background Low-level tragus stimulation (LLTS) has been shown to significantly reduce atrial fibrillation (AF) burden in patients with paroxysmal AF. P-wave alternans (PWA) is believed to be generated by the same substrate responsible for AF. Hence, PWA may serve as a marker in guiding LLTS therapy. We investigated the utility of PWA in guiding LLTS therapy in patients with AF. Methods and Results Twenty-eight patients with AF were randomized to either active LLTS or sham (earlobe stimulation). LLTS was delivered through a transcutaneous electrical nerve stimulation device (pulse width 200 µs, frequency 20 Hz, amplitude 10-50 mA), for 1 hour daily over a 6-month period. AF burden over 2-week periods was assessed by noninvasive continuous ECG monitoring at baseline, 3 months, and 6 months. A 5-minute control ECG for PWA analysis was recorded during all 3 follow-up visits. Following the control ECG, an additional 5-minute ECG was recorded during active LLTS in all patients. At baseline, acute LLTS led to a significant rise in PWA burden. However, active patients receiving chronic LLTS demonstrated a significant reduction in both PWA and AF burden after 6 months (P<0.05). Active patients who demonstrated an increase in PWA burden with acute LLTS showed a significant drop in AF burden after 6 months of chronic LLTS. Conclusions Chronic, intermittent LLTS resulted in lower PWA and AF burden than did sham control stimulation. Our results support the use of PWA as a potential marker for guiding LLTS treatment of paroxysmal AF.