Cohen‐Shelly, Michal

Abstract Aims Mitral and tricuspid regurgitation (MR and TR) are common in older adults and associated with substantial morbidity and mortality. While transthoracic echocardiography (TTE) is the diagnostic gold standard, access remains limited in many care settings. Artificial intelligence (AI)–based echocardiographic analysis may help address this diagnostic gap. Methods and results We externally validated a deep learning algorithm developed by Aisap.ai using TTE studies from the Mayo Clinic Health System (2013–23). The model analyses echocardiographic images to classify atrioventricular regurgitation severity and was evaluated against cardiologist interpretations. Performance was assessed using binary (normal–mild vs. moderate–severe) and ordinal (normal, mild, moderate, severe) classification schemes. Among 1541 eligible TTEs, the model returned predictions for 578 studies (38%). Performance analysis was limited to these cases. The MR cohort included 280 studies and the TR cohort 298. For MR, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 [95% confidence interval (CI): 0.97–0.99], with 91% accuracy, 95% sensitivity, and 89% specificity. For TR, the AUC was 0.96 (95% CI: 0.94–0.98), with 84% accuracy, 91% sensitivity, and 80% specificity. Conclusion In cases where a prediction was generated, the model demonstrated high diagnostic performance in identifying clinically significant atrioventricular regurgitation. These findings support the feasibility of AI-assisted echocardiography in diverse populations, while underscoring the need for technical alignment between model requirements and local acquisition practices to ensure real-world applicability.

Introduction: LMNA encodes the nuclear envelope proteins lamin A/C that are involved in ageing processes. Mutations in LMNA cause a spectrum of phenotypes which include muscular dystrophy, arrhythmogenic dilated cardiomyopathy, peripheral neuropathy, progeroid syndrome and overlap syndrome. Early diagnosis is crucial to prevent sudden cardiac death. We hypothesized that LMNA mutations result in an ECG biological age older than chronological age. Methods: In this study, we used AI-enabled ECG network to investigate standard digital 12-lead ECGs in genetically characterized LMNA patients. Results: We identified 33 LMNA patients with 331, 12-lead ECG available for analysis. Median age at symptoms onset was 23.5 (range: <1 to 61 years;15 females). Mean follow up time was 86 months (range: <1 to 367 months). Symptoms at onset included muscle weakness (n=10) or cardiac symptoms (n=18) and asymptomatic (n=5). We included a total of 1532 age sex matched controls. We found significant differences in positive age-gap (biologically older than chronological age) comparing LMNA patients (median of 17.5) versus controls (4.9 years; p<0.001, Figure 1 ). AI-ECG age with 10 years or higher age-gap was observed in 73% of LMNA patients compared to 27% in the controls (p<0.001). Consecutive serial analysis for all ECG’s in the study versus predicted age showed faster aging when comparing the rates of age-gap overtime in the LMNA group compared to controls (p<0.0001, Figure 2 ). Moreover, the subset analysis of 23 ECG in the 5 asymptomatic patients versus controls also showed significantly higher AI-ECG age-gap (p=0.004) Conclusions: An AI-ECG algorithm to detect biological age effectively demonstrates accelerated aging in patients with LMNA mutations related to physiological aging. Our findings further validate the role of AI-ECG to detect accelerated aging, expanding previous studies demonstrating higher mortality among individuals with in increased positive age-gap.

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 &lt; 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.

AbstractAimsAn artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect ‘physiological age’. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).Methods and resultsA total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, R = 0.63, P &lt; 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, R = 0.45, P &lt; 0.0001) and the recipient ages (R = 0.38, P &lt; 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), P = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002].ConclusionElectrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.

Abstract Background An artificial intelligence (AI) algorithm detecting age from 12-lead ECG has been suggested to signal “physiological age” of the individual. Importantly, increased physiological age gauged by an increased difference between ECG-age and chronological age has been associated with higher risk of cardiac events in non-transplant population. Purpose We sought to investigate the validity of the AI-derived ECG-age algorithm in patients who underwent heart transplantation and its relationship to major adverse cardiovascular events (MACE). Methods A total of 489 consecutive patients who had undergone heart transplantation in our institution between 1994 and 2018 were studied. AI-ECG age was calculated by a previously-trained artificial intelligence (AI) algorithm using a 12-lead ECG per patient. ECGs used in the training process of the algorithm were excluded. The average of the ECG-ages within one year before and one year after heart transplantation was used to represent pre- and post-transplant ECG-ages. MACE was defined as any incidence of revascularization, re-transplantation, and death. Results Pre-transplant ECG-age (mean 63±10 years) correlated significantly with recipient chronological age (mean 50±13 years, r=0.57, p&lt;0.0001), but this correlation between recipient and ECG-ages was weakened after transplantation (mean post-transplant ECG age of 55±10 years, r=0.34, p&lt;0.0001). Interestingly, post-transplant ECG-age correlated significantly with donor age (mean ECG age of 55±10 years vs. mean donor age of 32±13 years, r=0.42, p&lt;0.0001). During a median (IQR) follow-up of 9 (5, 14) years, 251 patients had MACE. Mean change in ECG age after transplantation compared to before was −8.8±12.7 years. Patients who had an increase in ECG-age after compared to before transplantation showed increased risk of MACE (HR: 1.53 [1.16, 2.01], p=0.002), independent of recipient and donor ages (adjusted HR: 1.68 [1.26, 2.25], p=0.001); whereas there were no significant differences in risk of MACE in patients who were transplanted with an older donor heart (HR: 1.07 [0.77, 1.50], p=0.66). In a Kaplan Meier survival analysis, those with increased ECG-age after transplantation had significantly lower MACE-free survival compared to those with decreased ECG-age. (Log-rank P=0.002; Wilcoxon P=0.001) (Figure) Conclusion Post-transplant ECG-age correlates more faithfully with the donor's than the recipient's chronological age, suggesting that ECG-age more closely reflects cardiac age than the patient age. Furthermore, ECG-age derived cardiac aging after transplantation is associated with higher risk of MACE. Funding Acknowledgement Type of funding sources: None.

Abstract Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50). Conclusion An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

Background An artificial intelligence algorithm that detects age using the 12‐lead ECG has been suggested to signal “physiologic age.” This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index of vascular aging, with accelerated physiologic aging gauged by ECG‐derived artificial intelligence–estimated age. Methods and Results This study included 531 patients who underwent ECG and a noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry. Abnormal PMEF was defined as reactive hyperemia peripheral arterial tonometry index ≤2.0. Accelerated or delayed physiologic aging was calculated by the Δ age (ECG‐derived artificial intelligence–estimated age minus chronological age), and the association between Δ age and PMEF as well as its impact on composite major adverse cardiovascular events were investigated. Δ age was higher in patients with abnormal PMEF than in patients with normal PMEF (2.3±7.8 versus 0.5±7.7 years; P =0.01). Reactive hyperemia peripheral arterial tonometry index was negatively associated with Δ age after adjustment for cardiovascular risk factors (standardized β coefficient, –0.08; P =0.048). The highest quartile of Δ age was associated with an increased risk of major adverse cardiovascular events compared with the first quartile of Δ age in patients with abnormal PMEF, even after adjustment for cardiovascular risk factors (hazard ratio, 4.72; 95% CI, 1.24–17.91; P =0.02). Conclusions Vascular aging detected by endothelial function is associated with accelerated physiologic aging, as assessed by the artificial intelligence–ECG Δ age. Patients with endothelial dysfunction and the highest quartile of accelerated physiologic aging have a marked increase in risk for cardiovascular events.