Benditt, David G.

ABSTRACT Background The available risk prediction models are inadequate to identify a true low‐risk patient for developing new‐onset atrial fibrillation (AF) after typical atrial flutter (AFL) ablation. We aimed to determine whether adding markers of atrial myopathy to HATCH score ( h ypertension, a ge ≥75 years, t ransient ischemic attack/stroke, c hronic obstructive pulmonary disease, and h eart failure) can improve prediction of new‐onset AF after ablation of typical AFL. Methods The study included 208 consecutive patients who underwent successful ablation of typical AFL at Minneapolis VA Medical Center and University of Minnesota Medical Center. Patients with history of AF prior to ablation were excluded. Results Among the 208 patients, 76 (36.5%) developed new‐onset AF post AFL ablation. Mean follow‐up duration was 62 ± 31.8 months. HATCH score was not associated with new‐onset AF. When adding atrial myopathy (presence of at least one of the following: PTFV 1 > 5000 µV*ms, interatrial block determined by biphasic inferior p wave with duration >120 ms, left atrium (LA) diameter ≥44 mm, or LA index ≥3 cm/m 2 ) to HATCH score (HATCH‐A 2 ), the combination was independently associated with new‐onset AF. The AF incidence between HATCH‐A 2 score of 0–1 and ≥2 were 6.7% and 40.5%, respectively (Odds ratio 9.04, 95% confidence interval: 2.05–39.81, p = 0.004). Particularly, when HATCH‐A 2 score was 0, none of the patients developed AF. Conclusions Adding atrial myopathy to HATCH score improved predictability and could be used to delineate a true low‐risk patient of new‐onset AF after typical AFL ablation.

Abstract Aims Syncope remains a diagnostic challenge despite advancements in testing and treatment. Cardiac syncope is an independent predictor of mortality and can be difficult to distinguish from other causes of transient loss of consciousness (TLOC). This paper explores whether artificial intelligence (AI) can improve the evaluation and management of patients with syncope. Methods and results We conducted a literature review and incorporated the opinions of experts in the fields of syncope and AI. The cause of TLOC is often unclear, hospitalization criteria are ambiguous, diagnostic tests are frequently non-informative, and assessments are costly. Patients are left with unanswered questions and limited guidance. Artificial intelligence (AI) has the potential to optimize syncope evaluation by processing large data sets, detecting imperceptible patterns, and assisting clinicians. However, AI has limitations, including errors, lack of human empathy, and uncertain clinical utility. Liability issues further complicate its integration. We present three viewpoints: (i) AI is crucial for advancing syncope management; (ii) AI can enhance the patient experience; and (iii) AI in syncope care is inevitable. Conclusion Artificial intelligence may improve syncope diagnosis and management, particularly through machine learning–based test interpretation and wearable device data. However, it has yet to surpass human clinical judgment in complex decision-making. Current challenges include gaps in understanding syncope mechanisms, AI interpretability, generalizability, and clinical integration. Standardized diagnostic approaches, real-world validation, and curated data sets are essential for progress. Artificial intelligence may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.

Sinus tachycardia (ST) is ubiquitous, but its presence outside of normal physiological triggers in otherwise healthy individuals remains a commonly encountered phenomenon in medical practice. In many cases, ST can be readily explained by a current medical condition that precipitates an increase in the sinus rate, but ST at rest without physiological triggers may also represent a spectrum of normal. In other cases, ST may not have an easily explainable cause but may represent serious underlying pathology and can be associated with intolerable symptoms. The classification of ST, consideration of possible etiologies, as well as the decisions of when and how to intervene can be difficult. ST can be classified as secondary to a specific, usually treatable, medical condition (eg, pulmonary embolism, anemia, infection, or hyperthyroidism) or be related to several incompletely defined conditions (eg, inappropriate ST, postural tachycardia syndrome, mast cell disorder, or post-COVID syndrome). While cardiologists and cardiac electrophysiologists often evaluate patients with symptoms associated with persistent or paroxysmal ST, an optimal approach remains uncertain. Due to the many possible conditions associated with ST, and an overlap in medical specialists who see these patients, the inclusion of experts in different fields is essential for a more comprehensive understanding. This article is unique in that it was composed by international experts in Neurology, Psychology, Autonomic Medicine, Allergy and Immunology, Exercise Physiology, Pulmonology and Critical Care Medicine, Endocrinology, Cardiology, and Cardiac Electrophysiology in the hope that it will facilitate a more complete understanding and thereby result in the better care of patients with ST.