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Background While ventricular tachycardia (VT) in the setting of postmyocardial infarction left ventricular aneurysms (LVA) is not uncommonly encountered, there is a scarcity of data regarding the safety, efficacy, and outcomes of ablation of VT in this subset of patients. Methods Our study included consecutive patients aged 18 years or older with postmyocardial infarction LVA who presented to Mayo Clinic for catheter ablation of VT between 2002 and 2018. Results Of 34 patients, the mean age was 70.4 ± 9.1 years; 91% were male. Mean LVEF was 29 ± 9.7% and left ventricular end-diastolic dimension was 64.9 ± 6.6 mm. The site of the LVA was apical in 21 patients (62%). Fifteen patients (44%) presented with electrical storm or incessant VT. Nine patients (26%) had a history of intracardiac thrombus. All except for one patient had at least one VT originating from the aneurysm. The mean number of VTs was 2.9 ± 1.7. All patients underwent ablation at the site of the aneurysm. Ablation outside the aneurysm was performed in 13 patients (38%). Low-voltage fractionated potentials and/or late potentials at the aneurysmal site were present in all cases. Complete elimination of all VTs was achieved in 18 (53%), while the elimination of the clinical VT with continued inducibility of nonclinical VTs was achieved in a further 11 patients (32%). Two patients developed cardiac tamponade requiring pericardiocentesis. During a mean follow-up period of 2.3 ± 2.4 years, 11 patients (32%) experienced VT recurrence. Freedom from all-cause mortality at 1-year follow-up was 94%. Conclusion Radiofrequency catheter ablation targeting the aneurysmal site is a feasible and reasonably effective management strategy for clinical VTs in patients with postinfarction LVA.

Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, therefore pre-emptively identifying and treating patients may be beneficial. Screening for DC with echocardiography is expensive and labor intensive and standard electrocardiography (ECG) is insensitive and non-specific. The performance and applicability of artificial intelligence-enabled electrocardiography (AI-ECG) for detection of DC is unknown. Diagnostic performance of an AI algorithm in determining reduced left ventricular ejection fraction (LVEF) was evaluated in a cohort that comprised of DC and normal LVEF control patients. DC patients and controls with 12-lead ECGs and a reference LVEF measured by echocardiography performed within 30 and 180 days of the ECG respectively were enrolled. The model was tested for its sensitivity, specificity, negative predictive (NPV) and positive predictive values (PPV) based on the prevalence of DC at 1% and 5%. The cohort consisted of 421 DC cases (60% males, 57±15 years, LVEF 28±11%) and 16,025 controls (49% males, age 69 ±16 years, LVEF 62±5%). For detection of LVEF≤45%, the area under the curve (AUC) was 0.955 with a sensitivity of 98.8% and specificity 44.8%. The NPV and PPV were 100% and 1.8% at a DC prevalence of 1% and 99.9% and 8.6% at a prevalence of 5%, respectively. In conclusion AI-ECG demonstrated high sensitivity and negative predictive value for detection of DC and could be used as a simple and cost-effective screening tool with implications for screening first degree relatives of DC patients.

Objective Endomyocardial biopsy (EMB) is a useful diagnostic tool though the yield may be limited in many myocardial diseases. Data on the diagnostic yield and prognostic significance of EMB guided by abnormal electrograms (EGM-Bx) in suspected cardiac sarcoidosis (CS) are scarce. Methods Seventy-nine patients (mean age: 56 ± 12 years; 61% men) with suspected CS based on clinical and imaging features underwent right or left ventricular EGM-Bx guided by electroanatomic mapping. Tissue samples were obtained from sites with abnormal EGMs and/or abnormal cardiac imaging. The diagnostic yield of EGM-Bx was evaluated in reference to histopathologic analysis. Left ventricular assist device (LVAD) and transplantation-free survival were compared between patients with positive and negative EGM-Bx for CS. Results A total of 254 samples were obtained from abnormal EGM sites, and 126 samples from normal EGM sites guided by pre-procedure imaging findings. Abnormal histopathology was noted in 65 (26%) and 10 (8%) samples from abnormal and normal EGM sites, respectively. Histopathology confirmed CS in 16 (20%) patients, while an alternative tissue diagnosis emerged in 10 (13%) patients. Abnormal EGMs at the biopsy site had sensitivity 89% and specificity 33% for a histopathologic diagnosis of CS. LVAD and transplantation-free survival were not significantly associated with the EGM-Bx result (log-rank p = .91). Conclusion In patients with suspected CS, abnormal EGM-Bx has high sensitivity and low specificity for establishing a definite CS diagnosis. Consideration of substrate abnormalities apparent on preprocedural imaging as an adjunct for selection of biopsy sites may further improve EGM-Bx yield.

BACKGROUND Local impedance (LI) drop measured with microfidelity electrodes embedded in the tip of an ablation catheter accurately reflects tissue heating during radiofrequency (RF) ablation. Previous studies found 15-30 Ω LI drops created successful lesions, while more than 40 Ω drops were associated with steam pops. The objective of this study was to evaluate the safety and efficacy of LI-guided ablation using standard (30 W) and high-power (50 W) in a preclinical model. METHODS RF lesions were created in explanted swine hearts (n = 6) to assess the feasibility of LI-guided ablation by targeting 10, 20, or 30 Ω (n = 20/group) drops. Subsequently, LI-guided ablation was evaluated in a chronic animal model (n = 8 Canines, 25-29 kg, 30/50 W). During the index procedure point-by-point intercaval line ablation and left inferior pulmonary vein (PV) isolation were performed. RF duration was at the operators' discretion but discontinued early if a 15-30 Ω drop was achieved. Operators attempted to avoid LI drops of more than 40 Ω. At 1-month, durable conduction block was evaluated with electroanatomic mapping followed by necropsy and histopathology. RESULTS In explanted tissue, terminating ablation at 10, 20, or 30 Ω LI drops created statistically larger lesions (p

Background: Ablation of ventricular tachycardia (VT) is limited by the inability to create penetrating lesions to reach intramyocardial origins. Intramural needle ablation using in-catheter, heated saline-enhanced radio frequency (SERF) energy uses convective heating to increase heat transfer and produce deeper, controllable lesions at intramural targets. This first-in-human trial was designed to evaluate the safety and efficacy of SERF needle ablation in patients with refractory VT. Methods: Thirty-two subjects from 6 centers underwent needle electrode ablation. Each had recurrent drug-refractory monomorphic VT after implantable cardioverter defibrillator implantation and prior standard ablation. During the SERF study procedure, one or more VTs were induced and mapped. The SERF needle catheter was used to create intramural lesions at targeted VT site(s). Acute procedural success was defined as noninducibility of the clinical VT after the procedure. Patients underwent follow-up at 30 days, and 3 and 6 months, with implantable cardioverter defibrillator interrogation at follow-up to determine VT recurrence. Results: These refractory VT patients (91% male, 66±10 years, ejection fraction 35±11%; 56% ischemic, and 44% nonischemic) had a median of 45 device therapies (shock/antitachycardia pacing) for VT in the 3 to 6 months pre-SERF ablation. The study catheter was used to deliver an average of 10±5 lesions per case, with an average of 430±295 seconds of radiofrequency time, 122±65 minute of catheter use time, and a procedural duration of 4.3±1.3 hours. Acute procedural success was 97% for eliminating the clinical VT. At average follow-up of 5 months (n=32), device therapies were reduced by 89%. Complications included 2 periprocedural deaths: an embolic mesenteric infarct and cardiogenic shock, 2 mild strokes, and a pericardial effusion treated with pericardiocentesis (n=1). Conclusions: Intramural heated saline needle ablation showed complete acute and satisfactory mid-term control of difficult VTs failing 1 to 5 prior ablations and drug therapy. Further study is warranted to define safety and longer-term efficacy. Registration: URL: https://www.clinicaltrials.gov ; Unique Identifier: NCT03628534 and NCT02994446.

Background The combination of contact force (CF) and local impedance (LI) may improve tissue characterization and lesion prediction during radiofrequency (RF) ablation. Objective The purpose of this study was to evaluate the utility of LI combined with CF in assessing RF ablation efficacy. Methods An LI catheter with CF sensing was evaluated in swine (n = 11) and in vitro (n = 14). The relationship between LI and CF in different tissue types was evaluated in vivo. Discrete lesions were created in vitro and in vivo at a range of forces, powers, and durations. Finally, an intercaval line was created in 3 groups at 30 W: 30s, Δ20Ω, and Δ30Ω. In the Δ20Ω and Δ30Ω groups, the user ablated until a 20 or 30 Ω LI drop. In the 30s group, the user was blinded to LI. Results In vivo, distinction in LI was found between the blood pool and the myocardium (blood pool: 122 ± 7.02 Ω; perpendicular contact: 220 ± 29 Ω; parallel contact: 207 ± 31 Ω). LI drop correlated with lesion depth both in vitro (R = 0.84) and in vivo (R = 0.79), informing sufficient lesion creation (LI drop >20 Ω) and warning of excessive heating (LI drop >65 Ω). When creating an intercaval line, the total RF time was significantly reduced when using LI guidance (6.4 ± 2 minutes in Δ20Ω and 8.1 ± 1 minutes in Δ30Ω) compared with a standard 30-second workflow (18 ± 7 minutes). Acute conduction block was achieved in all Δ30Ω and 30s lines. Conclusion The addition of LI to CF provides feedback on both electrical and mechanical loads. This provides information on tissue type and catheter-tissue coupling; provides feedback on whether volumetric tissue heating is inadequate, sufficient, or excessive; and reduces ablation time.

Background: Voltage mapping is critical to define substrate during ablation. In ventricular tachycardia, abnormal potentials may be targets. However, wavefront of activation could impact local signal characteristics. This may be particularly true when comparing sinus rhythm versus paced rhythms. We sought to determine how activation wavefront impacts electrogram characteristics. Methods: Patients with ischemic cardiomyopathy, ventricular tachycardia, and without fascicular or bundle branch block were included. Point by point mapping was done and at each point, one was obtained during an atrial paced rhythm and one during a right ventricular paced rhythm. Signals were adjudicated after ablation to define late potentials, fractionated potentials, and quantify local voltage. Areas of abnormal voltage (defined as

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.

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.

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.

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.

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 The prognosis of left ventricular noncompaction (LVNC) remains elusive despite its recognition as a clinical entity for >30 years. We sought to identify clinical and imaging characteristics and risk factors for mortality in patients with LVNC. Methods and Results 339 adults with LVNC seen between 2000 and 2016 were identified. LVNC was defined as end‐systolic noncompacted to compacted myocardial ratio >2 (Jenni criteria) and end‐diastolic trough of trabeculation‐to‐epicardium (X):peak of trabeculation‐to‐epicardium (Y) ratio 2.3 (Petersen criteria) by magnetic resonance imaging. Median age was 47.4 years, and 46% of patients were female. Left ventricular ejection fraction P Conclusions Overall survival is reduced in patients with LVNC compared with the expected survival of age‐ and sex‐matched US population. However, survival rate in those with preserved left ventricular ejection fraction and isolated apical noncompaction was comparable with that of the general population.

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.

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.

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.

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) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

Introduction Therapies for substrate-related arrhythmias include ablation or drugs targeted at altering conductive properties or disruption of slow zones in heterogeneous myocardium. Conductive compounds such as carbon nanotubes may provide a novel personalizable therapy for arrhythmia treatment by allowing tissue homogenization. Methods A nanocellulose carbon nanotube-conductive hydrogel was developed to have conduction properties similar to normal myocardium. Ex vivo perfused canine hearts were studied. Electroanatomic activation mapping of the epicardial surface was performed at baseline, after radiofrequency ablation, and after uniform needle injections of the conductive hydrogel through the injured tissue. Gross histology was used to assess distribution of conductive hydrogel in the tissue. Results The conductive hydrogel viscosity was optimized to decrease with increasing shear rate to allow expression through a syringe. The direct current conductivity under aqueous conduction was 4.3 x 10(-1) S/cm. In four canine hearts, when compared with the homogeneous baseline conduction, isochronal maps demonstrated sequential myocardial activation with a shift in direction of activation to surround the edges of the ablated region. After injection of the conductive hydrogel, isochrones demonstrated conduction through the ablated tissue with activation restored through the ablated tissue. Gross specimen examination demonstrated retention of the hydrogel within the tissue. Conclusions This proof-of-concept study demonstrates that conductive hydrogel can be injected into acutely disrupted myocardium to restore conduction. Future experiments should focus on evaluating long-term retention and biocompatibility of the hydrogel through in vivo experimentation.

In the past year, there have been numerous advances in our understanding of arrhythmia mechanisms, diagnosis, and new therapies. We have seen advances in basic cardiac electrophysiology with data suggesting that secretoneurin may be a biomarker for patients at risk of ventricular arrhythmias, and we have learned of the potential role of an NPR-C (natriuretic peptide receptor-C) in atrial fibrosis and the role of an atrial specific 2-pore potassium channel TASK-1 as a therapeutic target for atrial fibrillation. We have seen studies demonstrating the role of sensory neurons in sleep apnea–related atrial fibrillation and the association between bariatric surgery and atrial fibrillation ablation outcomes. Artificial intelligence applied to electrocardiography has yielded estimates of age, sex, and overall health. We have seen new tools for collection of patient-centered outcomes following catheter ablation. There have been significant advances in the ability to identify ventricular tachycardia termination sites through high-density mapping of deceleration zones. We have learned that right ventricular dysfunction may be a predictor of survival benefit after implantable cardioverter-defibrillator implantation in patients with nonischemic cardiomyopathy. We have seen further insights into the role of His bundle pacing on improving outcomes. As our understanding of cardiac laminopathies advances, we may have new tools to predict arrhythmic event rates in gene carriers. Finally, we have seen numerous advances in the treatment of arrhythmias in patients with congenital heart disease.

Background Magnetic resonance imaging (MRI) in patients with legacy cardiovascular implantable electronic devices (CIEDs) in situ is likely underutilized. We hypothesized the clinical benefit of MRI would outweigh the risks in legacy CIED patients. Methods This is a single-center retrospective study that evaluated and classified the utility of MRI using a prospectively maintained database. The outcomes were classified as aiding in diagnosis, treatment, or both for the patients attributable to the MRI. We then assessed the incidence of adverse effects (AE) when the MRI was performed. Results In 668, MRIs performed on 479 patients, only 13 (1.9%) MRIs did not aid in the diagnosis or treatment of the patient. Power-on reset events without clinical sequelae in three scans (0.45%) were the only AE. The probability of an adverse event happening without any benefit from the MRI scan was 1.1 × 10-4 . A maximum benefit in diagnosis using MRI was obtained in ruling out space-occupying lesions (121/185 scans, 65.4%). Scans performed in patients for elucidating answers to queries in treatment were most frequently done for disease staging at long term follow-up (167/470 scans, 35.5%). Conservative treatment (184/470 scans, 39%) followed by medication changes (153/470 scans, 28.7%) were the most common treatment decisions made. Conclusions The utility of MRI in patients with non-MRI-conditional CIEDs far outweighs the risk of adverse events when imaging is done in the context of a multidisciplinary program that oversees patient safety.