Device-measured physical activity data for classification of patients with ventricular arrhythmia events: A pilot investigation

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Effect of angiotensin receptor neprilysin inhibitor on physical activity in patients with heart failure with reduced ejection fraction, monitored by implantable electronic device home monitoring

AIMS

Angiotensin receptor neprilysin inhibitor (ARNI) therapy is a cornerstone in the treatment of heart failure with reduced ejection fraction (HFrEF), with significant improvement in mortality as well as morbidity and quality of life. However, maximal ARNI doses often result in hypotension. Recent studies with 'real world' experience suggest that lower doses of ARNI are as effective as higher doses.In order to evaluate the symptomatic effect of low-dose ARNI in HFrEF patients, we analyzed physical activity data obtained via home monitoring of patients with cardiac implantable electronic devices (CIEDs).

METHODS

We retrospectively analyzed physical activity data obtained from HFrEF patients with CIED-active home monitoring during the years 2021-2022. Patients with ARNI therapy were further divided into subgroups according to the administered dose. Low-dose ARNI included doses of up to 24/26 mg sacubitril/valsartan daily. Intermediate dose and high dose included doses of 72/78-120/130 mg/day, and 144/156-194/206 mg/day, respectively.

RESULTS

A total of 122 patients had home monitoring-compatible CIEDs and HFrEF during the study period. Sixty-four of these patients were treated with ARNI. Administration of low-dose ARNI resulted in a 20% increase in daily activity when compared with patients without ARNI treatment ( P  = 0.038). Change in physical activity of patients in the intermediate-dose and high-dose groups was not significant. Younger patients, patients with cardiac resynchronization therapy, and patients without diabetes mellitus were more physically active.

CONCLUSION

Low-dose ARNI had a beneficial effect on physical activity in HFrEF patients. MH via CIED provided real-life objective data for patients' follow-up.

Role of artificial intelligence in defibrillators: a narrative review

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.

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.

Big Data in electrophysiology

The quantity of data produced and captured in medicine today is unprecedented. Technological improvements and automation have expanded the traditional statistical methods and enabled the analysis of Big Data. This has permitted the discovery of new associations with a granularity that was previously hidden to human eyes. In the first part of this review, the authors would like to provide an overview of basic Machine Learning (ML) principles and techniques in order to better understand their application in recent publications about cardiac arrhythmias. In the second part, ML-enabled advances in disease detection and diagnosis, outcome prediction, and novel disease characterization in topics like electrocardiography, atrial fibrillation, ventricular arrhythmias, and cardiac devices are presented. Finally, the limitations and challenges of applying ML in clinical practice, such as validation, replication, generalizability, and regulatory issues, are discussed. More carefully designed studies and collaborations are needed for ML to become feasible, trustworthy, accurate, and reproducible and to reach its full potential for patient-oriented precision medicine.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

Multicenter Analysis of Dosing Protocols for Sotalol Initiation

Sotalol, a Vaughan-Williams Class III antiarrhythmic medication, is used to manage atrial arrhythmias. Due to its QT-prolonging effect and subsequent increased risk of torsade de pointes, many centers admit patients during the initial dosing period. Despite its widespread use, little information is available regarding dosing protocols during this period. In this multicenter investigation, dosing protocols in patients initiating sotalol therapy were examined to identify predictors of successful sotalol initiation. Over a 4-year period, patients admitted to 5 hospitals in the United States for inpatient telemetry monitoring during initiation for nonresearch purposes were enrolled. A 3-day course of 5 of 6 doses of sotalol was considered successful completion of the loading protocol. Of the 213 enrolled patients, over 90% were successfully discharged on sotalol. Significant bradycardia, ineffectiveness, and excessive QT prolongation were reasons for failed completion. Absence of a dose adjustment was a strong predictor of successful initiation (odds ratio: 6.6, 95% confidence interval: 1.3-32.7, P = .02). Hypertension, use of a calcium channel blocker, use of a separate β-blocker, and presence of a pacemaker were predictors of dose adjustments. Marginal structural models (ie, inverse probability weighting based on probability of a dose adjustment) verified that these factors also predicted successful initiation via preventing any dose adjustment and suggests that considering these factors may result in a higher likelihood of successful initiation in future investigations. In conclusion, we found that the majority of patients admitted for sotalol initiation are successfully discharged on the medication. The study findings suggest that factors predicting need for dose adjustment can be used to identify patients who could undergo outpatient initiation. Prospective studies are needed to verify this approach.