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Energy sources used for catheter ablation of atrial fibrillation (AF) ablation have undergone an exceptional journey over the past 50 years. Traditional energy sources, such as radiofrequency and cryoablation, have been the mainstay of AF ablation. Novel investigations have led to inclusion of other techniques, such as laser, high-frequency ultrasound, and microwave energy, in the armamentarium of electrophysiologists. Despite these modalities, AF has remained one of the most challenging arrhythmias. Advances in the understanding of electroporation promise to overcome the shortcomings of conventional energy sources. A thorough understanding of the biophysics and practical implications of the existing energy sources is paramount.

Background: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered hemodynamics, and risk for incident AF could be identified from a single 12-lead ECG using a novel artificial intelligence (AI)-enabled ECG analysis. Methods: Patients with heart failure and preserved ejection fraction (n=613) underwent AI-enabled ECG analysis, echocardiography, and cardiac catheterization. Individuals were grouped by AI-enabled ECG probability of contemporaneous AF, taken as an indicator of underlying LA myopathy. Results: Structural heart disease was more severe in patients with higher AI-probability of AF, with more left ventricular hypertrophy, larger LA volumes, and lower LA reservoir and booster strain. Cardiac filling pressures and pulmonary artery pressures were higher in patients with higher AI-probability, while cardiac output reserve was more impaired during exercise. Among patients with sinus rhythm and no prior AF, each 10% increase in AI-probability was associated with a 31% greater risk of developing new-onset AF (hazard ratio, 1.31 [95% CI, 1.20–1.42]; P P Conclusions: A novel AI-enabled score derived from a single 12-lead ECG identifies the presence of underlying LA myopathy in patients with heart failure and preserved ejection fraction as evidenced by structural, functional, and hemodynamic abnormalities, as well as long-term risk for incident AF. Further research is required to determine the role of the AI-enabled ECG in the evaluation and care of patients with heart failure and preserved ejection fraction.

IMPORTANCE: Ventricular tachycardia (VT) is associated with high mortality in patients with cardiac sarcoidosis (CS), and medical management of CS-associated VT is limited by high failure rates. The role of catheter ablation has been investigated in small, single-center studies. OBJECTIVE: To investigate outcomes associated with VT ablation in patients with CS. DESIGN, SETTING, AND PARTICIPANTS: This cohort study from the Cardiac Sarcoidosis Consortium registry (2003-2019) included 16 tertiary referral centers in the US, Europe, and Asia. A total of 158 consecutive patients with CS and VT were included (33% female; mean [SD] age, 52 [11] years; 53% with ejection fraction [EF]

Introduction: Transthyretin cardiac amyloidosis (TTR-CA) can cause heart failure with preserved EF (HFpEF). We previously reported a clinical/echo-based score (TCAS) and defined a cut-off that predicted risk of TTR-CA in HFpEF. Herein, we assess the comparative performance of an artificial-intelligence ECG-based screening tool (AI-ECG). Hypothesis: AI-ECG successfully identifies TTR-CA in HFpEF cohorts across a range of TTR-CA prevalence Methods: Referral HFpEF cohort was constructed from a referral population of HFpEF patients (EF ≥ 40%) undergoing 99m Tc-pyrophosphate scintigraphy (PYP) at Mayo Clinic (2013-2020, n=484, 38% with TTR-CA). Community HFpEF cohort was prospectively screened with PYP (n=275; age ≥ 60, LV wall thickness (WT) ≥ 12 mm, 4% with TTR-CA). TCAS was built using a multivariable logistic regression model and converted to a simple point score comprised of age (60-69, 70-79, ≥ 80: +2, +3, +4 points respectively), male sex (+2), relative WT > 0.57 (+2), posterior WT ≥ 12 mm (+1), EF < 60% (+1), and hypertension diagnosis (-1). TCAS was validated in the community cohort. A deep neural network trained to predict cardiac amyloidosis from a single 12-lead ECG, was applied to both referral and community cohorts. Patients were excluded if ECG was not available ≤ 12 months before PYP or if used in development of the AI-ECG. Standard ROC methods were applied. Youden’s index was used to define optimal cut-off. Results: Predictive characteristics of AI-ECG were strong and compared favorably with TCAS across a range of true TTR-CA prevalence (Table). Conclusions: Using a single ECG, AI-ECG effectively predicts TTR-CA with an excellent negative predictive value to rule out TTR-CA and a positive predictive value sufficient to warrant specific TTR-CA testing. This was particularly evident in the community. AI-ECG holds promise for simple, cost-effective global screening of at-risk populations, especially those with limited access to advanced cardiac imaging.

Introduction: Cardiac Sarcoidosis (CS) is a non-ischemic cardiomyopathy (NICM) characterized by infiltration of non-caseating granulomas involving in the heart with highly variable clinical manifestations. Cardiac resynchronization therapy (CRT) has shown significant promise in NICM, though little is known about its effectiveness in patients with CS. Hypothesis: We sought to determine if CRT improved cardiac remodeling and reduced hospitalizations in patients with CS. Methods: We retrospectively reviewed all patients with a clinical or histological diagnosis of CS who underwent CRT implantation at a Mayo Clinic site from 2000-2021. Baseline characteristics, echocardiographic parameters, heart failure hospitalization, and major adverse cardiac events (MACE) were assessed. Results: Our cohort was comprised of 55 patients with 61.8% male and a mean age of 58.7 ± 10.9 years. At baseline, 38 (69.1%) patients had isolated CS, while 17 (30.9%) had evidence of CS with extra-cardiac sarcoidosis. The overwhelming majority underwent CRT-D implantation (n=52, 94.5%) with 67.3% of implanted devices upgrades from prior pacemaker or implantable cardioverter defibrillator. At 6 months post-implantation there was no significant different in ejection fraction (34.8 ± 10.9 % vs. 37.7 ± 14.2 %, p = 0.237), left ventricular end systolic diameter (49.1 ± 9.9 mm vs. 45.7 ± 9.9 mm, p = 0.077) or left ventricular end-diastolic diameter (58.5 ± 10.2 mm vs. 57.5 ± 8.1 mm, p = 0.574). Within the first 6 months post-implantation, 5 (9.1%) patients sustained a heart failure hospitalization. At a mean follow up of 4.1 ± 3.7 years, 14 (25.5%) patients experienced a heart failure hospitalization, 11 (20.0%) underwent cardiac transplantation, 1 (1.8%) underwent LVAD implantation and 7 (12.7%) patients died. Conclusions: Our findings suggest a poor response to CRT in patients with CS with no significant evidence of reverse remodeling within 6 months and a substantial proportion of patients progressing to advanced heart failure therapies.

Data regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation following mitral valve surgery (MVS) is limited. Catheter ablation (CA) can be challenging given perivalvular substrate in the setting of mitral annuloplasty or prosthetic valves.To investigate the characteristics, safety, and outcomes of radiofrequency CA in patients with prior MVS and ventricular arrhythmias (VA).We identified consecutive patients with prior MVS who underwent CA for VT or PVC between January 2013 and December 2018. We investigated the mechanism of arrhythmia, ablation approach, peri-operative complications, and outcomes.In our cohort, 31 patients (77% men, mean age 62.3 ± 10.8 years, left ventricular ejection fraction 39.2 ± 13.9%) with prior MVS underwent CA (16 VT; 15 PVC). Access to the left ventricle was via transseptal approach in 17 patients, and a retrograde aortic approach was used in 13 patients. A combined transseptal and retrograde aortic approach was used in one patient, and a percutaneous epicardial approach was combined with trans-septal approach in one patient. Heterogenous scar regions were present in 94% of VT patients and scar-related reentry was the dominant mechanism of VT. Forty-seven percent of PVC patients had abnormal substrate at the site targeted for ablation. Clinical VA substrates involved the peri-mitral area in six patients with VT and five patients with PVC ablation. No procedure-related complications were reported. The overall recurrence-free rate at 1-year was 72.2%; 67% in the VT group and 78% in the PVC group. No arrhythmia-related death was documented on long-term follow-up.CA of VAs can be performed safely and effectively in patients with MVS.

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.

Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence electrocardiogram (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19. We examined all patients in the Mayo Clinic system who underwent clinically indicated electrocardiography and echocardiography within 2 weeks following a positive COVID-19 test and had permitted use of their data for research were included. Of the 27 patients who met the inclusion criteria, one had a history of normal ventricular function who developed COVID-19 myocarditis with rapid clinical decline. The initial AI ECG in this patient indicated normal ventricular function. Repeat AI ECG showed a probability of ejection fraction (EF) less than or equal to 40% of 90.2%, corroborated with an echocardiographic EF of 35%. One other patient had a pre-existing EF less than or equal to 40%, accurately detected by the algorithm before and after COVID-19 diagnosis, and another was found to have a low EF by AI ECG and echocardiography with the COVID-19 diagnosis. The area under the curve for detection of EF less than or equal to 40% was 0.95. This case series suggests that the AI ECG, previously shown to detect ventricular dysfunction in a large general population, may be useful as a screening tool for the detection of cardiac dysfunction in patients with COVID-19.

Objective This study sought to investigate incidence of left atrial appendage (LAA) triggers of atrial fibrillation (AF) and/or organized atrial tachycardias (OAT) in patients undergoing AF ablation and to evaluate outcomes after ablation. Background Although LAA isolation is being increasingly performed during AF ablation, the true incidence of LAA triggers for AF remains unclear. Methods All patients with LAA triggers of AF and/or OAT during AF ablation from 2001 to 2017 were included. LAA triggers were defined as atrial premature depolarizations from the LAA, which initiated sustained AF and/or OAT. Results Out of 7,129 patients undergoing AF ablation over 16 years, LAA triggers were observed in 21 (0.3%) subjects (age 60 ± 9 years; 57% males; 52% persistent AF). Twenty (95%) patients were undergoing repeat ablation. The LAA was the only nonpulmonary vein trigger in 3 patients; the remaining 18 patients had both LAA and other nonpulmonary vein triggers. LAA triggers were eliminated in all patients (focal ablation in 19 patients; LAA isolation in 2 patients). Twelve months after ablation, 47.6% remained free from recurrent arrhythmia. After overall follow-up of 5.0 ± 3.6 years (median: 3.7 years; interquartile range: 1.4 to 8.9 years), 38.1% were arrhythmia-free. All 3 patients with triggers limited to the LAA remained free of AF recurrence. One patient undergoing LAA isolation developed LAA thrombus during follow-up. Conclusions The incidence of true LAA triggers is very low (0.3%). Most patients with LAA triggers have additional nonpulmonary vein triggers, and despite elimination of LAA triggers, long-term arrhythmia recurrence rates remain high. Potential risks of empiric LAA isolation during AF ablation (especially first-time AF ablation) may outweigh benefits.

Atrial arrhythmias, including atrial tachycardia and atrial flutter, are not uncommon after prior ablation. Mechanisms for arrhythmogenesis may vary and include recurrent conduction through sites of ablation, leading to recurrence of prior ablated arrhythmias and creation of new substrate. Incidence of postablation atrial arrhythmias varies across studies and may relate to the approach to ablation, including extent of ablation performed, or to extent of substrate identified at the time of prior ablation and how that relates to the lesion set. In addition, postablation atrial arrhythmias may be more common in certain types of cardiomyopathy, including hypertrophic cardiomyopathy.

Background Inappropriate sinus tachycardia (IST) remains a clinical challenge because patients often are highly symptomatic and not responsive to medical therapy. Objective To study the safety and efficacy of stellate ganglion (SG) block and cardiac sympathetic denervation (CSD) in patients with IST. Methods Twelve consecutive patients who had drug-refractory IST (10 women) were studied. According to a prospectively initiated protocol, five patients underwent an electrophysiologic study before and after SG block (electrophysiology study group). The subsequent seven patients had ambulatory Holter monitoring before and after SG block (ambulatory group). All patients underwent SG block on the right side first, and then on the left side. Selected patients who had heart rate reduction ≥15 beats per minute (bpm) were recommended to consider CSD. Results The mean (SD) baseline heart rate (HR) was 106 (21) bpm. The HR significantly decreased to 93 (20) bpm (P = .02) at 10 minutes after right SG block and remained significantly slower at 97(19) bpm at 60 minutes. Left SG block reduced HR from 99 (21) to 87(16) bpm (P = .02) at 60 minutes. SG block had no significant effect on blood pressure or HR response to isoproterenol or exercise (all P > .05). Five patients underwent right (n = 4) or bilateral (n = 1) CSD. The clinical outcomes were heterogeneous: one patient had complete and two had partial symptomatic relief, and two did not have improvement. Conclusion SG blockade modestly reduces resting HR but has no significant effect on HR during exercise. Permanent CSD may have a modest role in alleviating symptoms in selected patients with IST.

Summary Background Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning. Methods We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs. Findings We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86–0·88), sensitivity of 79·0% (77·5–80·4), specificity of 79·5% (79·0–79·9), F1 score of 39·2% (38·1–40·3), and overall accuracy of 79·4% (79·0–79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90–0·91), sensitivity to 82·3% (80·9–83·6), specificity to 83·4% (83·0–83·8), F1 score to 45·4% (44·2–46·5), and overall accuracy to 83·3% (83·0–83·7). Interpretation An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation. Funding None.

Introduction Autonomic modulation has been used as a therapy to control recurrent ventricular arrhythmia (VA). This study was to explore stellate ganglion block (SGB) effect on cardiac electrophysiologic properties and evaluate the long-term outcome of cardiac sympathetic denervation (CSD) for patients with recurrent VA and structural heart disease (SHD). Materials and methods Patients who had recurrent VA due to SHD were enrolled prospectively. Electrophysiologic study and ventricular tachycardia (VT) induction were performed before and after left and right SGB. VA burden and long-term outcomes were assessed for a separate patient group who underwent left or bilateral CSD for drug-refractory VA due to SHD. Results Electrophysiologic study of nine patients showed that baseline mean (SD) corrected sinus node recovery time (cSNRT) increased from 320.4 (73.3) ms to 402.9 (114.2) ms after left and 482.4 (95.7) ms after bilateral SGB (P = .03). SGB did not significantly change P-R, QRS, and Q-T intervals and ventricular effective refractory period, nor did the inducibility of VA. Nineteen patients underwent left (n = 14) or bilateral (n = 5) CSD. CSD reduced VA burden and appropriate ICD therapies from a median (interquartile range) of 2.5 (0.4-11.6) episodes weekly to 0.1 (0.0-2.4) episodes weekly at 6-month follow-up (P = .002). Three-year freedom from orthotopic heart transplant (OHT) and death was 52.6%. New York Heart Association functional class III/IV and VT rate less than 160 beats per minute were predictors of recurrent VA, OHT, and death. Conclusion SGB increased cSNRT without changing heart rate. CSD was more beneficial for patients with mild-to-moderate heart failure and faster VA.

Introduction It is unknown if ANO‐1 is expressed in the heart, though the presence of a calcium‐activated chloride current has been proposed to mediate some cardiac dysrhythmias. Furthermore, a specific cell type termed telocytes, morphologically mimicking Cajal cells which use ANO‐1 to modulate their pacemaker activity in the gut, have been described in the heart. We therefore sought to determine whether this channel is expressed in the canine heart. Methods Myocardium was sampled from the ventricles of five canines. Sections were labeled with anti‐Kit and anti‐ANO‐1 antibodies. Slides were reviewed by four investigators looking at cell morphology, distribution, and co‐localization. Identification of telocytes was based on criteria including morphology, Kit positivity (+), and ANO‐1 positivity (+). Results Clusters of cells meeting criteria for telocytes were seen in the epicardium, sub‐epicardium, and mid‐myocardium. A small subset of cells that were morphologically similar to myocytes was ANO‐1 (+) but Kit (−). In total, three different cell classes were found: (i) Kit (+), ANO‐1 (+) cells with the appearance of telocytes; (ii) Kit (+), ANO‐1 (−) cells; and (iii) Kit (−), ANO‐1 (+) cells with the morphologic appearance of cardiac myocytes. Conclusions Telocytes are present in the canine ventricle and express ANO‐1. These data merit further study to elucidate the functional expression of these channels in the heart and whether they may be targets for cardiac arrhythmias.

The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software.In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance.The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p 0.001), frontal percent amplitude change (%) (p 0.001), and horizontal percent amplitude change (%) (p 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT.The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.

Objectives This study hypothesized that atrial fibrillation was associated with heart failure (HF) hospitalization, and that patients who received rhythm control therapy had a lower incidence of HF hospitalization and mortality. Background Atrial fibrillation is a known risk factor for HF hospitalization and mortality in patients with acquired heart disease. Although atrial arrhythmias are common in adults with tetralogy of Fallot (TOF), data about prevalence and outcomes of therapy for atrial fibrillation are very limited. Methods The MACHD (Mayo Adult Congenital Heart Disease) database was queried for adults with repaired TOF and documented atrial fibrillation from 1990 to 2017. Primary endpoint was HF hospitalization defined as admission for volume overload (pulmonary congestion and/or peripheral edema) requiring intravenous diuretics. Secondary endpoint was the effect of rhythm control therapy on HF hospitalization and all-cause mortality. Patients were divided into rhythm control and rate control groups based on the therapy initiated at the time of arrhythmia diagnosis. Results Of 415 patients, 27 (7%) had 42 HF hospitalizations. Of these 415 patients, 88 (21%) had atrial fibrillation at age 49 ± 13 years. Atrial fibrillation was an independent risk factor for HF hospitalization (adjusted hazard ratio: 2.67; 95% confidence interval: 1.04 to 7.34; p = 0.045). The 88 patients were divided into the rhythm control group (n = 61, 69%) and the rate control group (n = 27, 31%). The rate control group had higher unadjusted annual incidence of HF hospitalization (13% vs. 3%; p = 0.001) and all-cause mortality (11% vs. 4%; p = 0.002). Conclusions Atrial fibrillation was a risk factor for HF hospitalization and mortality in TOF patients, and rhythm control therapy was protective against these adverse events.

Aims: Previous data suggest ventricular high rate episodes (VHREs) on pacemakers are frequent and not associated with overall mortality on short term follow up. We sought to determine whether VHREs are associated with mortality, device upgrade, or change in ejection fraction on long term follow up. Methods: A single center, retrospective study was performed on 542 patients with permanent pacemakers followed between 2011 and 2013. Follow-up was extended to 2017 for determination of long term outcomes. “True” VHREs were defined as episodes adjudicated to be due to non-sustained ventricular tachycardia on review of electrograms and “false” VHREs were defined as supraventricular arrhythmias or noise. Results: VHRE occurred in 202(37.2%)/542 included patients. True VHRE was detected in 148(27.3%) while 54(10%) had false VHRE. The mean age of the population was 72 ± 15 years and 46% were women. Mean follow-up was 3.3 ± 1.4 years. The baseline characteristics of the true, false and no VHRE patients were similar. There was no difference in all-cause mortality between groups (27% mortality in true VHRE, 33% in false VHRE and 29% in no VHRE). Furthermore, there was no difference between groups with regards to any device upgrade (5% any upgrades in the VHRE, 9% in false VHRE and 5% in no VHRE.) On follow up, EF declined in all groups: −4% vs −2.4% vs −3.5% for true, false and no VHRE. Conclusion: VHRE are frequently encountered on remote monitoring of pacemakers and not associated with increased risk of mortality or need for downstream device upgrade. Keywords: Non-sustained ventricular tachycardia, Pacemakers, Remote monitoring, Mortality

Background Endocardial radiofrequency ablation of epicardial ganglionic plexus (GP) for atrial fibrillation (AF) is complicated by myocardial damage. Objectives We hypothesized that an epicardial approach with a novel nitinol catheter system capable of causing irreversible electroporation (IRE) with direct current (DC) could selectively and permanently destroy GP without collateral myocardial injury. Methods Acute studies and medium-term terminal studies (mean survival, 1137 days) were performed with seven dogs. In the acute studies, DC was used to target epicardial GP within the transverse sinus, oblique sinus, vein of Marshall, and right periaortic space. Successful electroporation was defined as the presence of ablative lesions in the GP without collateral myocardial damage. A four-point integer system was used to classify histologic changes in tissue harvested from the ablation sites. Atrial effective refractory period (AERP) was measured during the acute and medium-term studies. Results For six dogs in the medium-term studies, the postablation period was uneventful without complications. Lesions were successfully created at 20 of 21 sites (95.2%) with more than minimal myocardial damage in one dog. An increase in AERP occurred in both atria during the acute studies but was maintained only in the right atrium at medium-term follow-up (5032 milliseconds). No dog had damage to the esophagus, adjacent great arteries, or pulmonary veins. Conclusions This proof-of-concept study suggests that safe, effective, and selective epicardial ablation of GP can be performed with DC by IRE with minimal collateral myocardial damage.

Objectives We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort. Background Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging. Methods We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September 2018. The performance of the algorithm was tested among patients with recent echocardiographic assessments of LV function. We also applied the algorithm in patients with no recent echocardiographic assessments of LV function to determine the rate of new "positive screens." Results Among 16 056 adult patients who underwent routine ECG, 8600 (age 67.1 ± 15.2 years, 45.6% male), had a transthoracic echocardiogram (TTE) and 3874 patients had a TTE and ECG less than 1 month apart. Among these patients, the algorithm was able to detect an EF less than or equal to 35% with 86.8% specificity, 82.5% sensitivity, and 86.5% accuracy, (area under the curve, 0.918). Among 474 "false-positives screens," 189 (39.8%) had an EF of 36% to 50%. Among patients with no prior TTE, the algorithm identified 3.5% of the patients with suspected EF less than or equal to 35%. Exploratory analysis suggests false positives could be reduced by assessing NT-pro-BNP after the initial "positive screen." Conclusions A deep learning algorithm detected depressed LV function with good accuracy in routine practice. Further studies are needed to validate the algorithm in patients with no prior echocardiogram and to assess the impact on echocardiography utilization, cost, and clinical outcomes.

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter–electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

Background: Data regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation following MVS is limited.) CA can be challenging given perivalvular substrate in the setting of mitral annuloplasty or prosthetic valves. Objective: To investigate the characteristics, safety, and outcomes of radiofrequency catheter ablation (CA) in patients with prior mitral valve surgery (MVS) and ventricular arrhythmias (VA). Methods: We identified consecutive patients with prior MVS who underwent CA for VT or PVC between January 2013- December 2018. We investigated the mechanism of arrhythmia, ablation approach, peri-operative complications, and outcomes. Results: In our cohort of 31 patients (77% men, mean age 62.3±10.8 years, left ventricular ejection fraction 39.2±13.9%) with prior MVS underwent CA (16 VT; 15 PVC). Access to the left ventricle was via transseptal approach in 17 patients, and a retrograde aortic approach was used in 13 patients. A combined transseptal and retrograde aortic approach was used in one patient, and a percutaneous epicardial approach was combined with trans-septal approach in 1patient. Heterogenous scar regions were present in 94% of VT patients and scar-related reentry was the dominant mechanism of VT. Clinical VA substrates involved the peri-mitral area in 6 patients with VT and 5 patients with PVC ablation. No procedure-related complications were reported. The overall recurrence-free rate at 1-year was 72.2%; 67% in the VT group and 78% in the PVC group. No arrhythmia-related death was documented on long-term follow-up. Conclusion: CA of VAs can be performed safely and effectively in patients with MVS

To our knowledge, natural history has not been reported for cardiac sarcoidosis (CS) diagnosed by pathologic evaluation of the apical core at left ventricular assist device (LVAD) implantation or cardiac transplantation. We retrospectively identified 232 consecutive patients meeting CS criteria. Of these patients, 54 were diagnosed by pathologic confirmation of CS, 10 after evaluation of the apical core (LVAD implant) or explanted heart (transplant). We compared clinical characteristics at initial evaluation and outcomes for these 10 patients with those of 10 patients with known CS before LVAD implant/transplant. In the study group, five patients (50%) had confirmed extracardiac sarcoidosis before LVAD implant/transplant; five had not been diagnosed with sarcoidosis. Mean (standard deviation) left ventricular ejection fraction at initial evaluation was 23% (16%), and left ventricular end-diastolic dimension was 61 (10) mm. Four patients died during follow-up; however, no survival difference was found for the 10 patients diagnosed incidentally and the group with a previous diagnosis or institutional LVAD/transplant cohorts. Patients diagnosed with CS on pathological examination of the apical core/explanted heart may have severe dilated cardiomyopathy as the initial presentation. Outcomes for patients with CS after advanced heart failure therapies may be comparable with those of non-CS patients.

Background An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. Method We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. Results We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82–0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged Conclusions The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditions. While transthoracic echocardiography (TTE) remains the most common diagnostic tool to screen for LVSD, it is operator-dependent, time-consuming, effort-intensive, and relatively expensive. Recent work has demonstrated the ability of an artificial intelligence-augment ECG (AI-ECG) model to accurately predict LVSD in critical intensive care unit (CICU) patients. We demonstrate that the AI-ECG algorithm can maintain its performance in these patients with and without AF despite their clinical differences. An AI-ECG algorithm can serve as a non-invasive, inexpensive, and rapid screening tool for early detection of LVSD in resource-limited settings, and potentially expedite clinical decision making and guideline-directed therapies in the acute care setting.

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.

Sarcoidosis is a multisystem inflammatory condition with occasional cardiac involvement (CS), which may be associated with risk of venous thromboembolism (VTE). As data on VTE in CS are sparse and corticosteroid therapy has not been previously examined, we aim to determine the association between CS, corticosteroid treatment for CS, and VTE. Patients referred to our institution with concern for sarcoidosis and underwent a positron emission tomography (PET) scan were retrospectively assessed. Chi-squared and multivariate regression analyses were conducted to determine the association between a diagnosis of sarcoidosis, CS, corticosteroid use, and VTE events. Six hundred and forty nine patients were split into 3 categories: 235 with no sarcoidosis (NS), 91 with extra-cardiac sarcoidosis only (ECS), and 323 with CS (isolated CS and/or CS with extra cardiac sarcoid). Thirty nine CS, 7 ECS, and 9 NS patients developed PE while 44 CS, 3 ECS, and 18 NS patients developed DVT. On multivariate regression, neither CS nor ECS was an independent risk factor for VTE (p0.05) but corticosteroid use was independently associated with VTE (HR 3.06, p = 0.007 for PE, HR 6.21, p0.0001 for DVT). On logistic regression analysis, corticosteroid dose was found to be independently associated with both PE (p = 0.001) and DVT (p = 0.007). Optimal threshold for defining VTE risk with corticosteroid therapy was a prednisone-equivalent dose of 17.5 mg. In conclusion, contrary to previous studies, this current study found that neither sarcoidosis nor CS is an independent risk factor for VTE. Rather, corticosteroid therapy was associated with an increased risk of VTE.

Background: A non-invasive, easy-to-access marker of accelerated cardiac ageing would provide novel insights into the mechanisms and aetiology of cardiovascular disease (CVD) as well as contribute to risk stratification of those who have not had a heart or circulatory event. Our hypothesis is that differences between an ECG-predicted and chronologic age of participants (δage) would reflect accelerated or decelerated cardiovascular ageing Methods: A convolutional neural network model trained on over 700,000 ECGs from the Mayo Clinic in the U.S.A was used to predict the age of 4,542 participants in the Know Your Heart study conducted in two cities in Russia (2015-2018). Thereafter, δage was used in linear regression models to assess associations with known CVD risk factors and markers of cardiac abnormalities. Results: The biomarker δage (mean: +5.32 years) was strongly and positively associated with established risk factors for CVD: blood pressure, body mass index (BMI), total cholesterol and smoking. Additionally, δage had strong independent positive associations with markers of structural cardiac abnormalities: N-terminal pro b-type natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT) and pulse wave velocity, a valid marker of vascular ageing. Conclusion: The difference between the ECG-age obtained from a convolutional neural network and chronologic age (δage) contains information about the level of exposure of an individual to established CVD risk factors and to markers of cardiac damage in a way that is consistent with it being a biomarker of accelerated cardiovascular (vascular) ageing. Further research is needed to explore whether these associations are seen in populations with different risks of CVD events, and to better understand the underlying mechanisms involved.

Objective - To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. Background - LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. Methods - We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35–69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. Results - Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. Conclusions - The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance.

In this last decade, with further maturity of catheter ablation for AF, the multicenter CABANA trial demonstrated AF ablation to be safe, more effective compared to drugs, and likely having an early role in the CHF patient. Newer energy sources for accomplishing ablation with enhanced precision and safety are in their formative stages: particle beam radiation and electroporation advancing rapidly.

Background: High-sensitivity cardiac troponin (hs-cTn) assays quantify cTn in patients at very low concentrations. Myocyte injury due to ischemia or other pathologies cause blood levels to increase, which is prognostic. A noninvasive, rapid, broadly available, home-based test to detect hs-cTn increases would facilitate risk-stratification. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could non-invasively predict current or impending hs-cTnT elevations. Objective: To develop an AI-ECG convolutional neural network (CNN) to detect an abnormal hs-cTnT (5 th Gen cTnT, Roche Diagnostics) concentration using a 12-lead ECG, and a single lead ECG (lead I), which would enable smartphone, home-based detection. Methods: We developed single lead and 12-lead ECG CNNs to detect a) hs-cTnT concentrations that were at or above the 6ng/L limit that can be reported b) above the 99 th percentile upper limits of >15ng/L for men and >10ng/L for women. All ECGs were recorded within one hour of the hs-cTnT measurements. We used 73,012 ECG and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 18,276 ECG and hs-cTnT pairs from 11,904 different patients as a holdout test set to determine the area under the receiver-operator curve (AUC). Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. Of the 91,288 hs-cTnT pairs 73,271 (80.2%) were above 6ng/L and 50,799 (55.6%) are above the 99 th percentile. In the test set, the AUC for the detection of a hs-cTnT level higher than 6ng/L was 0.88 using the 12 lead ECG and 0.834 with the single lead. For the detection of hs-cTnT level above of 99 th percentile, the 12 lead ECG AUC was 0.853 and the single lead was 0.806. Conclusion: The AI-ECG permits detection of levels of hs-cTnT consistent with myocardial injury. This may allow a home-based, non-invasive test that would be massively scalable and could further enhance rapid-risk stratification and patient triage with potentially significant cost reductions and enable novel triage strategies at sites without hs-cTn assays.

Background: Myocardial injury results in release of cardiac troponin (cTn) into the bloodstream, readily detected by high-sensitivity cTn (hs-cTn) assays. A noninvasive, rapid, home-based test to exclude acute myocardial injury at the time of the test and for the next 7 hours would help manage patients with suspected myocardial infarction. Since myocyte injury is associated with ECG changes, we hypothesized an artificial intelligence ECG (AI-ECG) could identify absence of injury. Objective: To train and test an AI-ECG convolutional neural network (CNN) to identify patients using a single ECG who are suspected of myocardial infarction who are low risk, with hs-cTn levels below the 99 th percentile at test time and for the subsequent 7 hours. Methods: A CNN tuned to identify the absence of a hs-cTnT (5 th Gen cTnT Roche Diagnostics) >15ng/L for men and >10ng/L for women was developed. All ECGs were recorded within one hour of the hs-cTnT assay. We used 73,012 ECGs and hs-cTnT pairs from 47,542 unique patients to train the network, 9031 ECGs from 5,811 patients for internal validation to optimize hyperparameters, and 11,904 ECGs with 21,191 hs-cTnT measurements up to 7 hours after the ECG, from 11,904 different patients as a holdout test set. Results: The mean age was 63.9±17.5 years, and 30,348 of the 59,446 patients (51%) were male. 5,852 patients (49.1%) had no elevation of hs-cTnT and 6,052 (50.9%) had an hs-cTnT above the 99 th percentile at baseline or within 7 hours of the test. Of the 11,904 patients in the test set, using a sensitive threshold, the 12 lead AI ECG identified 1037 patients (8.7%) likely to have a low risk for subsequent hs-cTnT increases > 99 th % (AUC 0.86), and the single lead ECG identified 685 patients. Of the 1037 low risk pts, 59 had an hs-cTnT > 99 th percentile within 7 hours. The mean maximum hs-cTnT among the 59 low-risk patients was 53 ng/L ± 92 vs 184 ng/L ± 1474 in the others. None of these low risk patients died within 14 days of the test. Conclusion: Use of the AI ECG to identify patients without cardiac injury at the time of the test and for 7 subsequent hours is feasible. This may permit rapid, point of care, home-based, bloodless, painless testing to exclude cardiac injury, facilitating care and management of patients with suspected ischemia.

Objectives: We sought to evaluate the procedural safety and outcomes of high power (HP) versus conventional power (CP) radiofrequency (RF) ablation during pulmonary vein isolation (PVI). Methods: A systematic search was used via MEDLINE, EMBASE, and Cochrane databases between 01/1998 and 05/2020 for studies comparing the safety and efficacy between HP and CP during RF ablation for PVI using contact-force catheters (ThermocoolSmartTouch or TactiCath). The search terms used were “AF” AND “catheter ablation” OR “radiofrequency ablation”. Results: From eight comparative observational studies, we developed a cohort of 992 patients (478 in the HP and 514 in the CP group). The median maximum energy output used in the HP group was 50W vs 35W in the CP group. There was a statistically significant difference noted in acute pulmonary vein reconnection (APVR) in the HP group compared to the CP groups at16% vs 32.6%, respectively (RR: 0.53, 95% CI: 0.33 - 0.86, P = 0.01). Freedom from arrhythmia at 12 months follow-up was also significant and found to be 87.8% of cases in the HP group versus75.2% of cases in the CP group (RR: 0.46, 95%CI: 0.32 - 0.65, P Conclusion: This study suggests that HP PVI ablation can be associated with better ablation outcomes by reducing the rate of acute and late PV reconnection and atrial arrhythmia recurrence, with similar safety profiles, and shorter procedural times. These findings merit evaluation in a randomized controlled trial.

UNSTRUCTURED Manual contact tracing is a top-down solution that starts with contact tracers at the public health level, who identify the contacts of infected individuals, interview them to get additional context about the exposure, and also monitor their symptoms and support them until the incubation period is past. On the other hand, digital contact tracing is a bottom-up solution that starts with citizens who on obtaining a notification about possible exposure to an infected individual may choose to ignore the notification, get tested to determine if they were actually exposed or self-isolate and monitor their symptoms over the next two weeks. Most experts recommend a combination of manual and digital contact tracing, though they are limited published studies to guide implementation. One possible hybrid solution digital and manual contact tracing could involve a smartphone based alert that requests the possible contact of an infected individual to call Public Health (PH) for next steps, or that suggests ways to self-assess in order to reduce the burden on PH by only advising the most critical cases to call. In this paper, we aim to compare manual and digital approaches to contact tracing and provide suggestions for potential hybrid solutions.

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 Histologic evidence is required for a definitive diagnosis of cardiac sarcoidosis (CS) by published guidelines; however, the sporadic nature of the disease may produce false negative biopsy results, causing CS to be underdiagnosed. We sought to establish a clinical category of CS absent histologic findings. Methods Patients evaluated for CS were stratified into 3 groups: probable CS and definite CS based on Heart Rhythm Society (HRS) criteria and presumed CS, ie, patients without any histologic evidence of sarcoidosis, but with unexplained high-grade atrioventricular block or ventricular arrhythmia and findings suggestive of CS on either cardiac magnetic resonance imaging or positron emission tomography. The primary end point was hospitalization-free and overall survival at 10 years. Results A total of 383 patients were included in the study: 59, definite CS; 223, probable CS; and 101, presumed CS (62, isolated CS and 39, systemic CS). Compared with patients meeting HRS criteria for CS, patients with presumed CS had lower odds of New York Heart Association class III or IV symptoms (odds ratio [OR], 0.44 [95% CI, 0.23–0.83]; P = .01) but greater odds of previous ventricular tachycardia (OR, 2.4 [95% CI, 1.4–4.0]; P = .001) or history of resuscitated sudden cardiac arrest (OR, 2.9 [95% CI, 1.0–8.6]; P = .05). Hospitalization-free and overall survival were similar among groups (P = .51 and P = .71, respectively). Conclusions Clinical categorization of patients with presumed CS identified a high-risk cohort comparable to patients with histologic evidence of disease, although caution should be exercised in reaching this diagnosis without paying due diligence to the differential diagnosis.

Introduction An artificial intelligence (AI) algorithm that utilizes data contained within a single 12-lead ECG during normal sinus rhythm has recently shown to identify patients with occult AF. Patients with HFpEF frequently display AF in association with left atrial (LA) dysfunction. Hypothesis Patients with HFpEF and a higher probability of AF according to their AI-enabled ECG display more advanced LA remodeling and dysfunction, putting them at greater risk for AF development. Methods This retrospective cohort study includes 613 patients with definite HFpEF: 184 ambulatory patients diagnosed through invasive hemodynamic testing and 429 inpatients admitted because of decompensated HFpEF. A previously validated AI algorithm that predicts current AF risk based on ECG analysis was applied to estimate future AF risk during long-term follow-up, with patients grouped into quartiles of risk. Cardiac structure and function, as well as incident AF rates were compared. Results AI-predicted AF risk was 42% (14−69%) overall. Patients with higher risk were older, with more comorbidities, lower blood pressure, higher natriuretic peptide levels, greater conduction delays, a higher prevalence of pacing, and more frequent use of diuretics. In addition, underlying structural heart disease was more severe, with more pronounced left ventricular hypertrophy, larger LA volumes, lower LA reservoir and booster strain, higher cardiac filling pressures (confirmed through invasive measurements ambulatory patients), and more tricuspid regurgitation. Echocardiography images from a representative patient within each AF risk quartile are presented in the Figure. Invasive hemodynamic measurements in the ambulatory cohort showed additionally that patients with higher AI-predicted AF risk had lower cardiac output (10.01±2.85L/min and 6.94±2.49L/min in the lowest and highest risk quartile, respectively) and a higher pulmonary vascular resistance at peak effort (121±102dynes/s/cm−5 and220±133 dynes/s/cm−5 in the lowest and highest risk quartile, respectively). Over a median follow-up of 41months (11-71months), AI-predicted AF risk was associated with new-onset AF [HR(95%CI) = 1.31(1.20-1.41); p Conclusions An AI-risk score based upon a single 12-lead ECG reflects the severity of underlying LA myopathy in HFpEF and predicts new-onset AF. These data suggest that application of an AI-enabled ECG may be useful to identify patients with the AF/LA myopathy phenotype in HFpEF.

Objective To validate an artificial intelligence–augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort. Methods We identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment. Results For detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004). Conclusion Artificial intelligence–augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.

Background Right bundle‐branch block (RBBB) occurs in 0.2% to 1.3% of people and is considered a benign finding. However, some studies have suggested increased risk of cardiovascular morbidity and mortality. We sought to evaluate risk attributable to incidental RBBB in patients without prior diagnosis of cardiovascular disease (CVD). Methods and Results We reviewed the Mayo Clinic Integrated Stress Center database for exercise stress tests performed from 1993 to 2010. Patients with no known CVD—defined as absence of coronary disease, structural heart disease, heart failure, or cerebrovascular disease—were selected. Only Minnesota residents were included, all of whom had full mortality and outcomes data. There were 22 806 patients without CVD identified; 220 of whom (0.96%) had RBBB, followed for 6 to 23 years (mean 12.4±5.1). There were 8256 women (36.2%), mean age was 52±11 years; and 1837 deaths (8.05%), including 645 cardiovascular‐related deaths (2.83%), occurred over follow‐up. RBBB was predictive of all‐cause (hazard ratio [HR], 1.5; 95% CI, 1.1–2.0; P =0.0058) and cardiovascular‐related mortality (HR,1.7; 95% CI, 1.1–2.8; P =0.0178) after adjusting for age, sex, diabetes mellitus, hypertension, obesity, current and past history of smoking, and use of a heart rate‐lowering drug. Patients with RBBB exhibited more hypertension (34.1% versus 23.7%, P P P P Conclusions Patients with RBBB without CVD have increased risk of all‐cause mortality, cardiovascular‐related mortality, and lower exercise tolerance. These data suggest RBBB may be a marker of early CVD and merit further prospective evaluation.

Aims An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). Methods and results We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02–1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93–0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. Conclusion The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.

Artificial intelligence (AI) is an aspect of computer technology that imitates the ability of the human mind to analyze data. Over the last few years, there has been a paradigm shift in the utilization of AI in clinical practice. It is imperative for the clinical electrophysiologist to understand the basics of AI, and its potential applications in the field as new applications are developed and implemented. Multiple investigators have demonstrated various AI algorithms that can be utilized in clinical care. These include applications such as electronic stethoscopes and electrocardiographic prediction of atrial fibrillation or congestive heart failure. AI may also be used in cardiovascular imaging, to identify disease patterns and even compose preliminary reports. Herein, we seek to familiarize readers with terms associated with AI, such as machine learning and neural networks. Further, we review the applications of AI in bedside clinical calculators, electrocardiography, and the field of cardiovascular imaging. A critical appraisal of AI is provided with specific review of hurdles in the integration of AI in clinical practice.