Citations

Introduction: Left ventricular diastolic function and filling pressure (FP) can be assessed by echocardiography and cardiac catheterization. Recently, a novel artificial intelligence (AI)-enabled ECG has proven to be effective in identifying increased FP determined by echocardiographic assessment of diastolic function. Aims: We aimed to compare a prognostic value of AI-ECG determination of FP compared to echocardiography and catheterization. Methods: This study included 4,213 patients who had a transthoracic echocardiogram and ECG within 30 days before hemodynamic cardiac catheterization. Increased FP was defined as pulmonary artery wedge pressure >15 mmHg by catheterization and grade 2 or 3 diastolic dysfunction by echocardiography based on diastolic function guidelines. Increased FP by ECG was identified using previously developed AI-ECG. AI-ECG performance was evaluated using the area under the curve (AUC) of the receiver operating curve. We assessed the risk of all-cause mortality using Kaplan-Meier estimate and multivariable Cox proportional-hazards models. Results: AI-ECG had AUCs of 0.8 and 0.7 for predicting increased FP determined by echocardiography and catheterization, respectively. Death was observed in 2,776 (34%) patients over a median follow-up of 7.7 years (IQR 7.1, 8.3). Survival of patients with normal and increased FP by AI-ECG, Echocardiography, and catheterization are shown in Figure 1a. Concordant diagnoses of normal and increased FP by AI-ECG and catheterization or echocardiography had the highest and lowest survival rates, respectively. When FP by AI-ECG was discordant, survival was better predicted by AI-ECG compared to echocardiography and catheterization (Figure 1b). Conclusion: AI-ECG for FP has a potential to discriminate prognosis further compared to FP determined by echocardiography or catheterization.

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.

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

Single-cell diversity in the brain The cells that make up an organism may all start from one genome, but somatic mutations mean that somewhere along the line of development, an organism's individual cellular genomes diverge. McConnell et al. review the implications and causes of single-cell genomic diversity for brain function. Somatic mutations caused by mobile genetic elements or errors in DNA repair may underlie certain neuropsychiatric disorders. Science , this issue p. eaal1641