Atrial fibrillation (AF) is the most common sustained arrhythmia, with radiofrequency ablation (RFA) being a key treatment strategy. Despite its success, AF recurrence postablation is significant, and the ablation index (AI) was introduced to improve lesion quality and clinical outcomes. The aim of this study was to provide updated safety and efficacy data regarding AI-guided ablation strategy. A systematic literature review was performed utilizing the PubMed, Embase, Cochrane, and Web-of-Science databases to retrieve double-arm retrospective and prospective studies comparing AI-guided ablation to non-AI guided ablation from inception until January-2024. The initial literature search yielded 700 studies, with 18 double-arm full articles included in the analysis after screening and exclusions. The cohort consisted of 2030 patients undergoing AI-guided ablation and 1580 undergoing non-AI-guided ablation. The average age of the cohort was 64.09 ± 9.86 years, with a median follow-up period of 12 months. AI-guided ablation was associated with lower AF recurrence (22% vs 32%, OR = 0.67, 95% CI = 0.59-0.77, p < 0.001). It also resulted in shorter total procedure time (145 min vs 159 min, SMD = -0.47, 95% CI = -0.78--0.16, p = 0.005) and fluoroscopy time (8.8 min vs 11.3 min, SMD = -0.35, 95% CI = -0.55 to -0.20, p < 0.001). No significant difference was observed in ablation time. First pass isolation was more likely with AI-guided ablation (80% vs 60%, OR = 1.44, 95% CI = 1.04-2.01, p = 0.037). Complication rates were similar between groups (OR = 0.79, 95% CI = 0.47-1.32, p = 0.33). In conclusion, AI-guided ablation significantly reduces AF recurrence and procedure times compared to non-AI-guided methods, without increasing complication rates, indicating its efficacy and safety in clinical practice.

BACKGROUND After a cryptogenic stroke, patients often will require prolonged cardiac monitoring; however, the subset of patients who would benefit from long-term rhythm monitoring is not clearly defined. OBJECTIVE The purpose of this study was to create a risk score by identifying significant predictors of atrial fi brillation (AF) using age, sex, comorbidities, baseline 12-lead electrocardiogram, short-term rhythm monitoring, and echocardiographic data and to compare it to previously published risk scores. METHODS Patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or transient ischemic attack who underwent long-term rhythm monitoring with an implantable cardiac monitor were retrospectively analyzed. RESULTS Variables positively associated with a diagnosis of clinically significant AF include age (P <.001), race (P = .022), diabetes status (P = .026), chronic obstructive pulmonary disease status (P = .012), presence of atrial runs (P = .003), number of atrial runs per 24 hours (P <.001), total number of atrial run beats per 24 hours (P <.001), number of beats in the longest atrial run (P <.001), left atrial enlargement (P = .007), and at least mild mitral regurgitation (P = .009). We created a risk stratification score for our population, termed the ACL score. The ACL score demonstrated superiority to the CHA(2)DS(2)-VASc score and comparability to the C2HEST score for predicting device-detected AF. CONCLUSION The ACL score enables clinicians to better predict which patients are more likely to be diagnosed with device detected AF after a cryptogenic stroke.