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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 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.

Background Analysis of a 12-lead electrocardiogram (ECG) in sinus rhythm by artificial intelligence (AI) has demonstrated to identify the risk of underlying paroxysmal atrial fibrillation (AF) with reasonable accuracy in the overall population. Purpose This study investigates whether AI based ECG analysis can predict new AF development in patients with heart failure and preserved ejection fraction (HFpEF) without previous AF history, as well as predict AF burden in patients with AF history. Methods This retrospective cohort study includes 424 patients with HFpEF, consecutively admitted to receive treatment with intravenous diuretics for congestion. A previously validated AI algorithm that provides an AF risk score based on ECG analysis was applied in all subjects. Patients were stratified according to AF history: (1) no history; (2) paroxysmal AF; (3) persistent AF; or (4) permanent AF. In patients without AF history, the impact of AI-predicted AF risk on new AF development was assessed. In patients with previous AF episodes, the relationship between AI-predicted AF risk and AF burden as well as underlying echocardiography substrate was evaluated. Results Eighty-three patients had paroxysmal AF (19.5%), 48 persistent AF (11%), and 121 permanent AF (28.5%). AF patients were older, with lower body mass index and higher heart rate. Lower systolic blood pressure, larger left atrial volume index (LAVI), worse diastolic function, more tricuspid valve regurgitation, and more need for pacing were observed with increasing AF severity. In 172 patients without AF history followed for 979±875 days, 61 developed AF (35%) after a median time of 2,118 days. AI-predicted AF risk was associated with new-onset AF after adjustments for age, gender, systolic blood pressure, body mass index and LAVI [HR (95% CI) = 1.15 (1.04–1.28); P-value=0.007; figure]. Only 5/61 patients who developed AF during follow-up (8.2%) were anticoagulated at baseline. In patients with previous AF, the AI-predicted risk score increased significantly with AF burden (46±25% in paroxysmal AF, 59±22% in persistent AF, and 69±17% in permanent AF; P-value Conclusions An AI-predicted AF risk score based on ECG analysis was associated with AF burden in HFpEF patients and did independently predict new-onset AF in those without previous history. Time to new onset of atrial fibrillation Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Belgian American Educational Foundation (B.A.E.F.); Special Research Fund (BOF) of Hasselt University (Hasselt, Belgium)

Background Diastolic reserve decreases with aging. A recently developed artificial intelligence (AI) algorithm can predict age based on 12-lead electrocardiogram (ECG) analysis. Purpose This study aims to use a validated AI algorithm to assess cardiac senescence and investigate the impact of obesity on cardiac aging in heart failure with preserved ejection fraction (HFpEF). Methods This retrospective cohort study includes 403 patients with HFpEF, admitted for treatment with intravenous diuretics. ECG age was assessed by a convolutional neural network as previously validated. Patients were stratified according to the presence of obesity (body mass index >30 kg/m2) and ECG age was compared between groups. The relationship between ECG versus calendar age and structural/functional alterations on echocardiography, as well as the risk of atrial fibrillation (AF) development, was evaluated. Results In 253 (63%) obese patients with HFpEF, calendar age was 8 years younger compared with their non-obese counterparts, but ECG age was only 3 years younger. ECG minus calendar age was higher in obese patients (P-value Conclusions Obesity accelerates cardiac senescence in HFpEF as reflected by more pronounced diastolic dysfunction and a higher AF risk, which was identified from ECG analysis by a validated AI algorithm. Figure 1 Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Belgian American Educational Foundation (B.A.E.F.); Special Research Fund (BOF) of Hasselt University (Hasselt, Belgium).

Background Artificial Intelligence (AI) algorithms enabled the detection of patients with paroxysmal atrial fibrillation (PAF) from a single normal sinus rhythm (NSR) ECG. Coronary microvascular dysfunction (CMD) is a precursor for coronary artery disease, which is a known risk factor for AF. Purpose The aim of this study is to examine the probability of PAF, according to AI-enabled algorithm estimation, in patients with CMD. Methods 1858 patients without persistent atrial fibrillation with signs and/or symptoms of ischemia and with non-obstructive CAD (9%; which is a pre-set cut-off found to have the highest accuracy of identifying patients with PAF (Area Under the Curve = 0.87). Results Mean age for patients was 51.2±12.4 and 66.3% were females. 835 (45%) were in G1, 39 (2%) in G2, 911 (49%) in G3, and 73 (4%) in G4. Compared to G1 and G2, G3 and G4 were older, had more diabetes and higher smoking rates (p Conclusion Patients with combined CMD have a significantly higher probability of having PAF based on an AI-enabled algorithm. Further research is warranted to know if patients with CMD would benefit from formal AF screening at the time of diagnosis. Funding Acknowledgement Type of funding source: None

Background An Artificial intelligence (AI)-enabled algorithm can provide an age estimate from a single standard resting ECG. The gap between AI-estimated age (ECG age) and chronological age (Age-Gap) has been associated with total and cardiovascular mortality. We hypothesized that coronary endothelial dysfunction (CED), an early feature of coronary atherosclerosis, is associated with physiological aging, as measured by ECG age. Purpose The aim of this study is to investigate the association of CED, a potential index of cardiac aging, and AI-estimated physiological aging. Methods 1902 patients with signs and/or symptoms of ischemia and with non-obstructive coronary artery disease ( Results Average chronological age was 50.9±12.5, ECG age was 54.1±11.6, 1261 (66%) were females. Compared to CED−, the CED+ group had more males and hyperlipidaemia (p Conclusion Abnormal coronary endothelial function is associated with an increased AI-estimated age which could indicate a higher physiological age. Funding Acknowledgement Type of funding source: None

Background We have demonstrated that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased mortality. We hypothesized that Age-Gap would predict long-term atherosclerotic cardiovascular disease (ASCVD) and that Age-Gap would refine the ACC/AHA Pooled Cohort Equations' (PCE) predictive abilities. Methods Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients seeking primary care between 1998–2000 and followed through March 2016. Inclusion criteria were age 40–79 and complete data to calculate PCE. We excluded those with known ASCVD, AF, HF or an event within 30 days of baseline.A neural network, trained, validated, and tested in an independent cohort of ∼ 500,000 independent patients, using 10-second digital samples of raw, 12 lead ECGs. PCE was categorized as low Results We included 24,793 patients (54% women, 95% Caucasian) with mean follow up of 12.6±5.1 years. 2,366 (9.5%) developed ASCVD events and 3,401 (13.7%) the expanded ASCVD. Mean chronologic age was 53.6±11.6 years and the AI-ECG age was 54.5±10.9 years, R2=0.7865, p20%) age and sex adjusted HR 6.90, 95% CI 1.98–24.08, p-value=0.0006, when comparing those older to younger by ECG respectively (Figure 1B). Conclusion The difference between physiologic AI-ECG age and chronologic age is associated with long-term ASCVD, and enhances current risk calculators (PCE) ability to identify high and low risk individuals. This may help identify individuals who should or should not be treated with newer, expensive risk-reducing therapies. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Mayo Clinic

Background Cardiac sarcoidosis (CS) is an infiltrative inflammatory condition defined by infiltration of noncaseating granulomas into the heart. Based on the location of sarcoid lesion involvement, patients can present with symptoms of congestive heart failure, arrhythmias, and even sudden cardiac death. Purpose Diagnosis of CS has been somewhat challenging, with the Heart Rhythm Society (HRS) and Japanese Ministry of Health and Welfare (JMHW) being the 2 widely accepted diagnostic guidelines. Endomyocardial biopsy is the gold standard to prove definite CS but has a low sensitivity. Imaging studies have been helpful as non-invasive methods to diagnose probable CS but these can be logistically difficult and expensive. Thus, investigating for laboratory biomarkers that can act as both diagnostic and prognostic can be crucial in how we diagnose and manage CS in the future. Methods Patients meeting HRS for CS were evaluated at a single institution (n=217). Biomarkers of interest included angiotensin-converting enzyme (ACE), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), N-terminal pro B-type natriuretic peptide (NT-proBNP), troponin-T, 1,25 dihydroxyvitamin D (1,25-OHVit-D), and creatinine. Biomarkers were stratified by clinical variables of interest and their association with prognosis was examined. The primary endpoint was LVAD implantation, transplantation, or death. Results Mean values on presentation were: ACE 32.9±28, ESR 12±13, CRP 7.4±19, NT-proBNP 1630±2923, Troponin-T 0.03±0.1, 1,25-OHVit-D 55.5±20, and creatinine 1.12±0.3. None of the biomarkers differed by sex, definite or probable CS, or a history of immunosuppression. ACE levels were associated with the presence of cardiac fibrosis on cardiac MRI (mean difference 14.7, p=0.032). Troponin-T (p=0.006; HR 1.06 per 0.01 ng/mL), NT-proBNP (p=0.0003; HR 1.31 per 1,000 pg/mL), and creatinine (p=0.01; HR 4.02 per mg/dL) were each associated with the primary endpoint (52/217 patients). Biomarkers associated with long term outcomes in patients with cardiac sarcoidosis Biomarker Hazard ratio P value Troponin-T 1.06 (1.02–1.11)* 0.006 NT-pro BNP 1.31 (1.15–1.48)** 0.0003 Creatinine 4.02 (1.41–9.94)*** 0.01 *Per 0.01 ng/mL change; 99th percentile upper reference limit Conclusion Troponin-T, NT-proBNP, and creatinine at presentation predict outcomes in patients with CS. Further investigation on the utility of biomarkers for assessment of disease activity and treatment response is warranted.

Catheter-tissue coupling is crucial for effective delivery of radiofrequency (RF) energy during catheter ablation. Force sensing catheters provide a metric of mechanical tissue contact and catheter stability, while local impedance has been shown to provide sensitive information on real-time tissue heating. The complementary use of force and local impedance during RF ablation procedures could provide an advantage over the use of one metric alone. This study evaluates a prototype ablation catheter that measures both contact force (CF) using inductive sensors and local catheter impedance (LI) using only catheter electrodes. The complementary nature was assessed with discrete lesions in vitro and an intercaval line in vivo. A force-sensing catheter with LI was evaluated in explanted swine hearts (n=14) in an in vivo swine model (n=9, 50–70kg) using investigational electroanatomical mapping software. In vitro, discrete lesions were created in ventricular tissue at a range of forces (0–40g) controlled externally. RF energy was applied at a range of powers (20W, 30W, and 40W), durations (10s-60s), and catheter orientations (0°, 45°, and 90°). Lesions were stained with TTC and measured. LI drop relative to baseline during RF in the bench studies was used to inform the in vivo study. In a separate subset of animals in vivo, an intercaval line was created in three experimental groups: LI blinded, 20Ω ΔLI, and 30Ω ΔLI. CF was maintained between 15 and 25g in all groups. All ablations were performed with a power of 30W. In the LI blinded group, all lesions were delivered for 30s. In the 20Ω ΔLI group, the investigator ablated until a 20Ω drop or 30 seconds was achieved. Likewise, in the 30Ω ΔLI, the investigator ablated until a 30Ω drop or 30 seconds was achieved. In vitro, 137 discrete ventricular lesions were created. LI drop during ablation correlated strongly with lesion depth using a monoexponential fit (R=0.84) while force time integral (FTI) did not correlate as strongly (R=0.56). In the intercaval LI blinded group, starting LI ranged from 126–163Ω with a median of 138Ω. LI drops ranged from 13Ω-44Ω, with a median of 26Ω. In the 20Ω ΔLI group, starting LI ranged from 137–211Ω with a median of 161Ω and LI drop ranged from 7Ω-35Ω, with a median of 22Ω. In the 30Ω ΔLI group, starting LI ranged from 130–256Ω with a median of 171Ω and LI drop ranged from 20Ω-52Ω, with a median of 31Ω. Notably, RF time for the LI blinded group was 13±0.1 minutes while RF time in the 20Ω ΔLI group was 6.4±1.9 minutes and 7.5±0.7 minutes in the 30Ω ΔLI group. A catheter incorporating CF-sensing and LI capabilities provides a powerful tool for RF ablation. Bench studies demonstrate a strong correlation between LI drop and lesion dimensions, which guided the use of LI in vivo. In vivo, the confirmation of stable mechanical contact and viewing of real-time LI drops enabled a significant reduction in RF time while creating a continuous intercaval line. Acknowledgement/Funding This study was funded by Boston Scientific.