Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

Abstract

Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.

Chronic Kidney Disease with Mild and Mild to Moderate Reduction in Renal Function and Long-Term Recurrences of Atrial Fibrillation after Pulmonary Vein Cryoballoon Ablation

“…Balloon cryoablation is now a well-established technique, with some advantage in terms of procedure time and need for re-ablation as compared to radiofrequency ablation [ 14 , 27 , 28 ]. Our results suggest that, from a clinical point of view, even a mild to moderate reduction in eGFR has an impact on outcome, in line with the evidence that patient profile is a major determinant of long-term outcome in AF patients and, thus, requires individualized decision-making [ 1 , 3 , 29 , 30 , 31 , 32 , 33 ].…”

Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?

“…AI could also be useful for identifying atrial fibrillation mechanisms at genetic, cellular, organ, and patient levels, involving clinical, demographic, metabolic, and genomic aspects [ 10 ]. Future tailored approaches may integrate mechanistic markers at all these biological levels, using machine learning and the AI approach, to develop individualized models of AF onset, progression, and response to therapy [ 10 ]. This is in order to achieve a real precision and personalized medicine in atrial fibrillation patients [ 10 ].…”

“…Future tailored approaches may integrate mechanistic markers at all these biological levels, using machine learning and the AI approach, to develop individualized models of AF onset, progression, and response to therapy [ 10 ]. This is in order to achieve a real precision and personalized medicine in atrial fibrillation patients [ 10 ].…”

New Perspectives on Risk Stratification and Treatment in Patients with Atrial Fibrillation: An Analysis of Recent Contributions on the Journal of Cardiovascular Disease and Development

“…The medical approach to atrial fibrillation (AF) underwent a paradigm shift over time, evolving from considering AF as a simple arrhythmic phenomenon to a complex nosological entity [ 1 , 2 ].…”