Key result
Advanced pacing, AI, and non-linear dynamics show promise for predicting, preventing, and controlling arrhythmias.
Why the study?
Predicting arrhythmia onset and preventing transitions to unstable rhythms remains challenging, with ICD patients facing inappropriate shocks, highlighting the need to review advances in cardiac pacing and arrhythmia control strategies.
Advanced machine learning models and novel pacing strategies offer promising alternatives to traditional clinical methods for predicting and suppressing cardiac arrhythmias.
Requires prospective trials before adoption in arrhythmia care; extends review evidence without confirming clinical efficacy.
Cardiac arrhythmias constitute a tremendous burden on healthcare and are the leading cause of mortality worldwide. An alarming number of people have been reported to manifest sudden cardiac death as the first symptom of cardiac arrhythmias, accounting for about 20% of all deaths annually. Furthermore, patients prone to atrial tachyarrhythmias such as atrial flutter and fibrillation often have associated comorbidities including hypertension, ischemic heart disease, valvular cardiomyopathy and increased risk of stroke. Technological advances in electrical stimulation and sensing modalities have led to the proliferation of medical devices including pacemakers and implantable defibrillators, aiming to restore normal cardiac rhythm. However, given the complex spatiotemporal dynamics and non-linearity of the human heart, predicting the onset of arrhythmias and preventing the transition from steady state to unstable rhythms has been an extremely challenging task. Defibrillatory shocks still remain the primary clinical intervention for lethal ventricular arrhythmias, yet patients with implantable cardioverter defibrillators often suffer from inappropriate shocks due to false positives and reduced quality of life. Here, we aim to present a comprehensive review of the current advances in cardiac arrhythmia prediction, prevention and control strategies. We provide an overview of traditional clinical arrhythmia management methods and describe promising potential pacing techniques for predicting the onset of abnormal rhythms and effectively suppressing cardiac arrhythmias. We also offer a clinical perspective on bridging the gap between basic and clinical science that would aid in the assimilation of promising anti-arrhythmic pacing strategies.
No takes yet. Share an insight, caveat, or question.
Patel et al. (2021) conducted a review in Cardiac arrhythmias. Cardiac pacing, artificial intelligence, and non-linear dynamical methods vs. Traditional clinical methods was evaluated. Advanced pacing strategies, artificial intelligence models, and non-linear dynamical methods offer promising approaches for the prediction, prevention, and control of cardiac arrhythmias.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: