Dominic, Paari

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.

Abstract Background Catheter Ablation (CA) is an effective treatment for atrial fibrillation (AF). However, frail elderly patients have been understudied due to their exclusion from landmark trials. Objectives Our study aims to evaluate outcomes in this population. Methods The national readmission database (2016–2020) was queried, and frailty categories were defined based on hospital risk frailty scores ≦5 as low while >5 as intermediate/high frailty (IHF). We used multivariate regression and propensity‐matched analysis to compare outcomes in patients undergoing CA for atrial fibrillation based on frailty index. Results Among 55 936 CAs for AF, 33,248 patients had low frailty, while 22 688 had intermediate/high frailty (IHF). After propensity matching (N 12 448), IHF patients were found to have higher adverse events, including mortality (3% vs. 0.3%, p < .001), stroke (1.9% vs. 0.2%, p < .001), acute heart failure (53.8% vs. 42.2%, p < .001), AKI (42.5% vs. 6.8%, p < .001), pericardial complications (2.8 vs. 1.6%, p < .001), respiratory complications (27.8 vs. 7.2%, p < .001), major adverse cardiovascular events (21.2 vs. 9.4%, p < .001) and net adverse events (76.7 vs. 55%, p < .001). IHF patients had higher readmissions at 30 (15.5 vs. 12.6%, p < .001), 90 (31.9 vs. 25.1%, p < .001), and 180‐day (41 vs. 34.7%, p < .001) intervals. A higher median length of stay (LOS) (7 vs. 3 days, p < .001) and cost ($44 287 vs. $27 517, p < .001) at index admission and subsequent readmissions were also observed ( p < .001). Conclusion Intermediate/high frailty patients undergoing catheter ablation had worse clinical outcomes, higher healthcare burden, and readmission rates. LOS has decreased in both groups from 2016 to 2020; however, total cost has increased.