Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.

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Background KCNJ2 mutations are associated with a variety of inherited arrhythmia syndromes including CPVT3. Objective Detailed cellular and mechanistic characterization of the clinically recognized KCNJ2 mutation R67Q. Methods Kir2.1 current density was measured using the whole-cell voltage clamp technique from COS-1 cells transiently transfected with WT-Kir2.1 and/or R67Q-Kir2.1. Catecholamine activity was simulated with PKA stimulating cocktail exposure. Phosphorylation deficient mutants, S425N-Kir2.1 and S425N-Kir2.1/R67Q-S425N-Kir2.1, were used in a separate set of experiments. HA- or Myc-Tag-WT-Kir2.1 or HA-Tag-R67Q-Kir2.1 were used for confocal imaging. Results A 33 year old presented with a CPVT-like clinical phenotype and was found to have KCNJ2 missense mutation R67Q. Treatment with nadolol and flecainide resulted in complete suppression of arrhythmias and symptom resolution. Under baseline conditions, R67Q-Kir2.1 expressed alone did not produce IK1 while cells co-expressing WT-Kir2.1 and R67Q-Kir2.1 showed rectification index (RI) similar to WT-Kir2.1. After PKA stimulation, R67Q-Kir2.1/WT-Kir2.1 failed to increase peak outward current density; WT-Kir2.1 increased 46% (n=5) while R67Q-Kir2.1/WT-Kir2.1 decreased 6% (n=6), p=0.002. Rectification properties in R67Q-Kir2.1/WT-Kir2.1 demonstrated sensitivity to calcium with decreased RI in high-calcium pipette solution (RI 20.3 ± 4.1%) compared to low-calcium (RI 36.5 ± 5.7%) (p< 0.05). Immunostaining of WT-Kir2.1 and R67Q-Kir2.1 individually and together showed a normal membrane expression pattern and co-localization by Pearson’s correlation coefficient. Conclusion R67Q-Kir2.1 is associated with an adrenergic-dependent clinical and cellular phenotype with rectification abnormality enhanced by increased calcium. These findings are a significant advancement of our knowledge and understanding of phenotype-genotype relationship of arrhythmia syndromes related to KCNJ2 mutations.

Background: Asthma and atrial fibrillation (AF) share an underlying inflammatory pathophysiology. We hypothesized that persistent asthmatics are at higher risk for developing AF and that this association would be attenuated by adjustment for baseline markers of systemic inflammation. Methods: The MESA (Multi-Ethnic Study of Atherosclerosis) is a prospective longitudinal study of adults free of cardiovascular disease at baseline. Presence of asthma was determined at exam 1. Persistent asthma was defined as asthma requiring use of controller medications. Intermittent asthma was defined as asthma without use of controller medications. Participants were followed for a median of 12.9 (interquartile range, 10–13.6) years for incident AF. Multivariable Cox regression models were used to assess associations of asthma subtype and AF. Results: The 6615 participants were a mean (SD) 62.0 (10.2) years old (47% male, 27% black, 12% Chinese, and 22% Hispanic). AF incidence rates were 0.11 (95% CI, 0.01–0.12) events/10 person-years for nonasthmatics, 0.11 (95% CI, 0.08–0.14) events/10 person-years for intermittent asthmatics, and 0.19 (95% CI, 0.120.49) events/10 person-years for persistent asthmatics (log-rank P =0.008). In risk-factor adjusted models, persistent asthmatics had a greater risk of incident AF (hazard ratio, 1.49 [95% CI, 1.03–2.14], P =0.03). IL (Interleukin)-6 (hazard ratio, 1.26 [95% CI, 1.13–1.42]), TNF (tumor necrosis factor)-α receptor 1 (hazard ratio, 1.09 [95% CI, 1.08–1.11]) and D-dimer (hazard ratio, 1.10 [95% CI, 1.02–1.20]) predicted incident AF, but the relationship between asthma and incident AF was not attenuated by adjustment for any inflammation marker (IL-6, CRP [C-reactive protein], TNF-α R1, D-dimer, and fibrinogen). Conclusions: In a large multiethnic cohort with nearly 13 years follow-up, persistent asthma was associated with increased risk for incident AF. This association was not attenuated by adjustment for baseline inflammatory biomarkers.