Lee, Eunjung

Abstract Background and Aims Atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF) are intimately associated disorders; HFpEF may be overlooked in AF when symptoms are simply attributed to dysrhythmia, and incident AF may identify patients at risk for developing diastolic dysfunction (DD). This study aimed to investigate the prevalence and incidence of DD in patients with new-onset AF compared with sinus rhythm (SR). Methods Adults with new-onset AF (n = 1747) or SR (n = 29 623) and no structural heart disease were identified. Propensity score matching was performed (1:3 ratio) between AF and SR based on age, sex, body mass index, and comorbidities. Severe DD (SDD) was defined by ≥3/four abnormal parameters (medial e’, medial E/e’, tricuspid regurgitation velocity, and left atrial volume index) and ≥moderate DD (>MDD) by ≥2/4. Annualized changes in DD indices were determined. Results New-onset AF was independently associated with SDD (8% vs. 3%) and ≥MDD (25% vs. 16%); 62% of patients with AF had high-risk H2FPEF scores, and 5% had clinically recognized HFpEF. Over a median follow-up of 3.2 (interquartile range 1.6–5.8) years, DD progressed two–four-fold more rapidly in those with new-onset AF (P < .001 for all). The risk for incident DD was increased in new-onset AF [hazard ratio (95% confidence interval) 2.69 (2.19–3.32) for SDD and 1.73 (1.49–2.02) for ≥MDD]. Conclusions Patients with new-onset AF display high-risk features for HFpEF at diagnosis, emphasizing the importance of evaluating for HFpEF among symptomatic patients with AF. Patients with new-onset AF have accelerated progression in DD over time, which may identify patients with preclinical HFpEF, where preventive therapies may be tested.

Abstract Aims Atrial functional mitral regurgitation (AFMR) has been associated with atrial fibrillation (AF) and heart failure with preserved ejection fraction. However, data on incident AFMR are scarce. We aimed to study the incidence, risk factors, and clinical significance of AFMR in AF or sinus rhythm (SR). Methods and results Adults with new diagnosis of AF and adults in SR were identified. Patients with >mild MR at baseline, primary mitral disease, cardiomyopathy, left-sided valve disease, previous cardiac surgery, or with no follow-up echocardiogram were excluded. Diastolic dysfunction (DD) was indicated by ≥2/4 abnormal diastolic function parameters [mitral medial e′, mitral medial E/e′, tricuspid regurgitation velocity, left atrial volume index (LAVI)]. Overall, 1747 patients with AF and 29 623 in SR were included. Incidence rate of >mild AFMR was 2.6 per 100 person-year in new-onset AF and 0.7 per 100 person-year in SR, P < 0.001. AF remained associated with AFMR in a propensity score-matched analysis based on age, sex, and comorbidities between AF and SR [hazard ratio: 3.80 (95% confidence interval 3.04–4.76)]. Independent risk factors associated with incident AFMR were age ≥65 years, female sex, LAVI, and DD in both AF and SR, in addition to rate (vs. rhythm) control in AF. Incident AFMR was independently associated with all-cause death in both groups (both P < 0.001). Conclusions AF conferred a three-fold increase in the risk of incident AFMR. DD, older age, left atrial size, and female sex were independent risk factors in both SR and AF, while rhythm control was protective. AFMR was universally associated with worse mortality.

Abstract Aims An artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown. Methods and results The AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate–severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 ± 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group (P < 0.001). The AI-ECG was correlated with aortic valve area (ρ = −0.48, R2 = 0.20), peak velocity (ρ = 0.22, R2 = 0.08), and mean pressure gradient (ρ = 0.35, R2 = 0.08). The AI-ECG also correlated with left ventricular (LV) mass index (ρ = 0.36, R2 = 0.13), E/e′ (ρ = 0.36, R2 = 0.12), and left atrium volume index (ρ = 0.42, R2 = 0.12). Neither LV ejection fraction nor stroke volume index had a significant correlation with the AI-ECG. Age correlated with the AI-ECG (ρ = 0.46, R2 = 0.22) and its correlation with echocardiography parameters was similar to that of the AI-ECG. Conclusion A combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical/functional features in the model and its identification process of AS is multifactorial.