Citations

BACKGROUND: Pulsed field ablation is now widely used for atrial fibrillation (AF) ablation, but atrial tachyarrhythmia recurrence remains common and postablation risk tools are limited. We assessed whether a validated sinus‑rhythm artificial intelligence— enabled ECG AF probability score, and its early postablation change, predicts recurrence after pulsed field ablation. METHODS: We studied consecutive patients undergoing index pulsed field ablation across 3 Mayo Clinic sites (February 2024–March 2025) with ≥1 sinus‑rhythm ECG within 180 days preprocedure. Baseline artificial intelligence—enabled ECG AF score (range 0–1) was the mean across baseline ECGs. Recovery indices (RI30/60/90), defined as mean postprocedure artificial intelligence—enabled ECG score within days 1–30/60/90 minus baseline, predicted post‑blanking recurrence using corresponding 30‑, 60‑, and 90‑day blanking windows. The primary outcome was time to first documented AF/atrial flutter/atrial tachycardia episode >30 seconds, ascertained via clinic ECGs, ambulatory monitoring, and device interrogations. Cox regression adjusted for prespecified clinical and echocardiographic covariates assessed associations; Harrell C‑statistic quantified discrimination. RESULTS: Among 1052 patients (67±10 years; 33% women), median baseline score was 0.35; 320 (30%) recurred over 188 days. Higher baseline score independently predicted recurrence (hazard ratio per 0.1 increase, 1.08 [95% CI, 1.03–1.14]). Scores rose on day 0 after ablation, then declined over subsequent weeks, with persistently higher, less‑improving trajectories among patients who recurred (time×recurrence interaction P <0.001). Day 0 score was not independently associated with recurrence after adjustment. Attenuated early recovery (higher recovery index) was independently associated with recurrence, most robustly for RI-90 (hazard ratio per 0.1 increase, 1.15 [95% CI, 1.07–1.24]), and improved discrimination beyond baseline and covariates (C‑statistic, 0.63–0.67; ΔC=0.04). Findings were consistent in a historical thermal ablation cohort. CONCLUSIONS: Baseline and early postablation dynamics of a sinus‑rhythm artificial intelligence—enabled ECG AF probability score are associated with pulsed field ablation recurrence and may aid multivariable risk stratification; prospective validation is warranted.

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF &lt;40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Background Artificial intelligence (AI)–enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance of AI‐enabled electrocardiography–derived diastolic dysfunction (DD) in patients undergoing transcatheter aortic valve replacement had not been assessed. Methods We analyzed 3197 patients undergoing transcatheter aortic valve replacement for severe aortic stenosis between 2010 and 2023 with baseline 12‐lead ECGs processed by a validated AI model to classify diastolic function (grades 0–3). Multivariable Cox models and nested model comparisons assessed associations with all‐cause mortality, including a prespecified analysis in patients with indeterminate echocardiographic grading. Trajectories were defined by change in AI‐enabled electrocardiography DD grade (<2 versus ≥2) from baseline to 30‐day or 1‐year follow‐up. Results At baseline, 21% had grade 3, 57% had grade 2, 8% had grade 1, and 14% had grade 0 AI‐enabled electrocardiography DD. Higher grades were associated with adverse cardiac remodeling and comorbidities. Over a median follow‐up of 3.4 years, grade 3 AI‐enabled electrocardiography DD independently predicted mortality (hazard ratio [HR], 1.80 [95% CI, 1.47–2.20]; P <0.001). AI‐enabled electrocardiography DD improved prognostic discrimination beyond clinical and echocardiographic measures (ΔHarrell concordance statistic, 0.016; Δ χ 2 =57; P <0.001). Among 1259 patients with indeterminate echocardiographic grading, AI‐enabled electrocardiography added prognostic value (ΔHarrell concordance statistic, 0.02; Δ χ 2 =13; P =0.006). Worsening or persistently high‐risk trajectories were associated with increased mortality (HRs, 1.45–1.80; all P <0.05). Conclusions AI‐enabled electrocardiography–derived DD independently predicts mortality after transcatheter aortic valve replacement, adds value beyond echocardiographic grading, and enables dynamic risk stratification through longitudinal tracking.

Background: Tricuspid regurgitation (TR) is associated with significant morbidity, but individualized treatment and timing of intervention are yet to be defined. We previously proposed and validated artificial intelligence electrocardiography (AI-ECG) scores for various diseases. Whether incorporating AI-ECG information adds to risk stratification above and beyond classical clinical parameters is unknown. Objectives: To identify AI-ECG scores independently associated with all-cause mortality in chronic TR and to create a composite AI-ECG risk score. Methods: Patients with a first echocardiographic diagnosis of ≥moderate TR between 2005-2016 and an ECG within ±15 days were included. The Tricuspid Regurgitation Impact on Outcome (TRIO) score was calculated as previously described based on 8 simple parameters (age, sex, severe TR, heart failure, lung disease, heart rate, creatinine and AST). AI-ECG probabilities of low ejection fraction, aortic stenosis, amyloid, cirrhosis, atrial fibrillation and hypertrophic cardiomyopathy were estimated by existing algorithms; the first 4 were associated with all-cause mortality and a composite AI-ECG risk score was calculated (0-4; 1 point for each AI-ECG above specific threshold). Results: A total of 12,377 pts were included (age 72±13 yrs, 55% women). Over a median follow-up of 1.3 yrs (IQR 0.1 to 4.4), 7,121 pts (58%) died. Mortality risk increased significantly with higher composite AI-ECG risk score (Fig. 1A). On multivariable analysis (proportional hazards method), the composite AI-ECG risk score remained independently associated with death, even after adjusting for age, sex, high NT-proBNP levels (>2,500 pg/mL), right ventricular systolic pressure (RVSP), diuretic use and TRIO risk score category (Fig. 1B). Conclusions: Patients with ≥moderate TR show progressively worse survival with increasing composite AI-ECG risk score. Incorporating AI-derived information from a simple and inexpensive ECG may identify patients with the highest risk of mortality beyond clinical risk scores as TRIO or classical clinical, laboratory and echocardiographic parameters. Prospective validation of this approach needs to be tested in future trials.

Introduction: Lewy body dementia (LBD) is common, yet under-recognized and under-researched. To plan studies with the highest impact, engagement of the community personally affected by these conditions is essential. Methods: A web-based survey of people living with LBD and current and former caregivers of people with LBD queried research priorities through forced ranking and exploration of burden of LBD symptoms. Specific caregiving needs in LBD and perceptions of research participation were also investigated. Results: Between April 7, 2021 and July 1, 2021, 984 responses were recorded. Top research priorities included disease-modifying therapies and improved disease detection and staging. People with LBD were interested in pathophysiology and more bothered by motor symptoms; caregivers were interested in risk factors and symptomatic therapies and more bothered by neuropsychiatric symptoms. Few available LBD treatments and resources were rated as helpful, and many valuable services were never received. Previous participation in LBD research was infrequent, but interest was high. Discussion: People with LBD and caregivers highlighted the need for research across all aspects of LBD, from pathophysiology and disease modification to prognosis, education, symptomatic treatments, and caregiver support. Funders should increase support for all aspects of LBD research to target the many needs identified by individuals and families living with LBD.