Sehrawat, Ojasav

AbstractIntroductionCardiac sarcoidosis (CS) is a nonischemic cardiomyopathy (NICM) characterized by infiltration of noncaseating granulomas involving the heart with highly variable clinical manifestations that can include conduction abnormalities and systolic heart failure. Cardiac resynchronization therapy (CRT) has shown significant promise in NICM, though little is known about its efficacy in patients with CS.ObjectiveTo determine if CRT improved cardiac remodeling in patients with CS.MethodsWe retrospectively reviewed all patients with a clinical or histological diagnosis of CS who underwent CRT implantation at the Mayo Clinic enterprise from 2000 to 2021. Baseline characteristics, imaging parameters, heart failure hospitalizations and need for advanced therapies, and major adverse cardiac events (MACE) were assessed.ResultsOur cohort was comprised of 55 patients with 61.8% male and a mean age of 58.7 ± 10.9 years. Eighteen (32.7%) patients had definite CS, 21 (38.2%) had probable CS, while 16 (29.1%) had presumed CS, and 26 (47.3%) with extracardiac sarcoidosis. The majority underwent CRT‐D implantation (n = 52, 94.5%) and 3 (5.5%) underwent CRT‐P implantation with 67.3% of implanted devices being upgrades from prior pacemakers or implantable cardioverter defibrillators. At 6 months postimplantation there was no significant improvement in ejection fraction (34.8 ± 10.9% vs. 37.7 ± 14.2%, p = .331) or left ventricular end‐diastolic diameter (58.5 ± 10.2 vs. 57.5 ± 8.1 mm, p = .236), though mild improvement in left ventricular end systolic diameter (49.1 ± 9.9 vs. 45.7± 9.9 mm, p < .0001). Within the first 6 months postimplantation, 5 (9.1%) patients sustained a heart failure hospitalization. At a mean follow‐up of 4.1± 3.7 years, 14 (25.5%) patients experienced a heart failure hospitalization, 11 (20.0%) underwent cardiac transplantation, 1 (1.8%) underwent left ventricular assist device implantation and 7 (12.7%) patients died.ConclusionsOur findings suggest variable response to CRT in patients with CS with no overall improvement in ventricular function within 6 months and a substantial proportion of patients progressing to advanced heart failure therapies.

Abstract Background In the context of atrial fibrillation (AF), traditional clinical practices have thus fallen short in several domains, such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. Aims To discuss the roles of artificial intelligence (AI)‐enabled electrocardiogram (ECG) pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. Materials & Methods An extensive search and review of the currently available literature on the topics. Results One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Challenges with regards to the benefits and harms of AF screening remain. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm. Discussion Knowledge gaps remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and identifying those who would benefit most from oral anticoagulation. The AI‐enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high‐risk ESUS patients likely to benefit from empirical oral anticoagulation. The role of DL models assessing AF burden from long‐duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer‐grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, the role of adequate external validation of the models and clinical trials to study true performance is discussed. Conclusion Algorithms using AI to interpret ECGs in various new ways have been developed. While still, much work needs to be done, these technologies have shown enormous potential in a short span of time. With further advancements and continuous research, these novel ways of interpretation may well become part of everyday clinical workflow.

In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with ESUS and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed.