Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Arrhythmic Manifestations and Outcomes of Definite and Probable Cardiac Sarcoidosis.

BACKGROUND: The 2014 HRS consensus statement defines histological (definite) and clinical (probable) diagnostic categories of cardiac sarcoidosis (CS), but few studies have compared their arrhythmic phenotypes and outcomes. OBJECTIVE: Evaluate the electrophysiologic/arrhythmic phenotype and outcomes of patients with definite and probable CS. METHODS: We analyzed the arrhythmic/electrophysiologic phenotype in a single-center North American cohort of 388 patients (median age 56 years, 39% female) diagnosed with definite (n=58) or probable (n=330) CS (2000-2022). The primary composite outcome was survival to first ventricular tachycardia/fibrillation (VT/VF) or sudden cardiac death (SCD). Key secondary outcomes were also assessed. RESULTS: At index evaluation, in situ cardiac implantable electronic devices and antiarrhythmic drug use were more common in definite CS. At median follow-up 3.1 years, the primary outcome occurred in 22 (38%) definite and 127 (38%) probable CS patients (log-rank p=0.55). In multivariable analysis, only higher 18F-FDG SUVmax myocardium:SUVmax blood pool ratio (HR 1.09 [95% CI 1.03, 1.15], p=0.003, per 1 unit increase) was associated with the primary outcome. During follow-up, definite CS patients had higher burden of device-treated VT/VF events (mean 2.86 vs 1.56 per patient-year) and higher rate of progression to heart transplant/left ventricular assist device implantation, but no difference in all-cause mortality compared to probable CS patients. CONCLUSION: Definite and probable CS patients had similarly high risks of first sustained VT/VF/SCD and all-cause death though definite CS patients had a higher overall arrhythmic burden. Both CS diagnostic groups as defined by the 2014 HRS criteria require an aggressive approach to prevent arrhythmic complications.

2023 HRS expert consensus statement on the management of arrhythmias during pregnancy.

This international multidisciplinary expert consensus statement is intended to provide comprehensive guidance that can be referenced at the point of care to cardiac electrophysiologists, cardiologists, and other health care professionals, on the management of cardiac arrhythmias in pregnant patients and in fetuses. This document covers general concepts related to arrhythmias, including both brady- and tachyarrhythmias, in both the patient and the fetus during pregnancy. Recommendations are provided for optimal approaches to diagnosis and evaluation of arrhythmias; selection of invasive and noninvasive options for treatment of arrhythmias; and disease- and patient-specific considerations when risk stratifying, diagnosing, and treating arrhythmias in pregnant patients and fetuses. Gaps in knowledge and new directions for future research are also identified.

Artificial Intelligence-Derived Electrocardiogram Assessment of Cardiac Age and Molecular Markers of Senescence in Heart Failure.

OBJECTIVE: To ascertain whether heart failure (HF) itself is a senescent phenomenon independent of age, and how this is reflected at a molecular level in the circulating progenitor cell niche, and at a substrate level using a novel electrocardiogram (ECG)-based artificial intelligence platform. PATIENTS AND METHODS: Between October 14, 2016, and October 29, 2020, CD34+ progenitor cells were analyzed by flow cytometry and isolated by magnetic-activated cell sorting from patients of similar age with New York Heart Association functional classes IV (n = 17) and I-II (n = 10) heart failure with reduced ejection fraction and healthy controls (n = 10). CD34+ cellular senescence was quantitated by human telomerase reverse transcriptase expression and telomerase expression by quantitative polymerase chain reaction, and senescence-associated secretory phenotype (SASP) protein expression assayed in plasma. An ECG-based artificial intelligence (AI) algorithm was used to determine cardiac age and difference from chronological age (AI ECG age gap). RESULTS: CD34+ counts and telomerase expression were significantly reduced and AI ECG age gap and SASP expression increased in all HF groups compared with healthy controls. Expression of SASP protein was closely associated with telomerase activity and severity of HF phenotype and inflammation. Telomerase activity was more closely associated with CD34+ cell counts and AI ECG age gap. CONCLUSION: We conclude from this pilot study that HF may promote a senescent phenotype independent of chronological age. We show for the first time that the AI ECG in HF shows a phenotype of cardiac aging beyond chronological age, and appears to be associated with cellular and molecular evidence of senescence.

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age.

OBJECTIVE: To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care. PATIENT AND METHODS: We applied a previously trained convolutional neural network model to predict biological age by electrocardiogram (ECG) [Artificial Intelligence (AI)-ECG age] to LMNA patients evaluated by multiple ECGs from January 1, 2003, to December 31, 2019. The age gap was the difference between chronological age and AI-ECG age. Findings were compared with age-/sex-matched controls. RESULTS: Thirty-one LMNA patients who had a total of 271 ECGs were studied. The median age at symptom onset was 22 years (range, <1-53 years; n=23 patients); eight patients were asymptomatic family members carrying the LMNA mutation. Cardiac involvement was detected by ECG and echocardiogram in 16 patients and consisted of ventricular arrhythmias (13), atrial fibrillation (12), and cardiomyopathy (6). Four patients required cardiac transplantation. Fourteen patients had neurological manifestations, mainly muscular dystrophy. LMNA mutation carriers, including asymptomatic carriers, were 16 years older by AI-ECG than non-LMNA carriers, suggesting accelerated biological age. Most LMNA patients had an age gap of more than 10 years, compared with controls (P<.001). Consecutive AI-ECG analysis showed accelerated aging in the LMNA group compared with controls (P<.0001). There were no significant differences in age-gap among LMNA patients based on phenotype. CONCLUSION: AI-ECG predicted that LMNA patients have a biological age older than chronological age and accelerated aging even in the absence of cardiac abnormalities by traditional methods. Such a finding could translate into early medical intervention and serve as a disease biomarker.

Artificial Intelligence-Augmented Electrocardiogram in Determining Sex: Correlation with Sex Hormone Levels.

OBJECTIVE: To study the relationship between the sex probability derived from the artificial intelligence (AI)-augmented electrocardiogram (ECG) and sex hormone levels. PATIENTS AND METHODS: Adult patients with total testosterone (TT; ng/dL) or estradiol (E2; pg/mL) levels (January 1, 2000, to December 31, 2020) with ECGs obtained within 6 months of the blood sample were identified. The closest ECG to the blood test was used. The AI-ECG model output ranges from 0.0 to 1.0, with higher numbers indicating high probability of being male. Low male probability was defined as ≤0.3, intermediate as 0.31 to 0.69, and high as ≥0.7. Continuous variables are expressed as median (interquartile range). RESULTS: Paired TT-ECGs were available in 58,084 male subjects and 11,190 female subjects. Paired E2-ECGs were available in 2835 male patients and 18,228 female patients. TT levels had moderate positive correlation with AI-ECG male sex probability (r=0.46, P<.001). Male subjects with low AI-ECG male sex probability had lower TT and higher E2 levels compared with men with high probability (TT: 303 [129-474] vs 381 [264-523], P <.001; E2: 35 [21-49] vs 32 [22-38], P=.05). Female subjects with high AI-ECG male sex probability had higher TT and lower E2 levels compared with those who had low male probability (TT: ≤50 years of age: 31 [18-55] vs 26 [16-39], P<.001; >50 years of age: 27 [12-68] vs 20 [12-34], P<.001; E2: ≤50 years of age: 58 [30-124] vs 47 [25-87], P=.001; >50 years of age: 30 [10-55] vs 21 [10-41], P=.006). CONCLUSION: In this study, TT levels were lower and E2 levels higher with decreasing AI-ECG male probability in both sexes. Male and female patients with discordant AI-ECG sex probability had significantly different TT or E2 levels. This suggests that the ECG could be used as a biomarker of hormone status.

18F-FDG/13N-ammonia cardiac PET findings in ATTR cardiac amyloidosis.

18F-flurodeoxyglycose (FDG)/13N-ammonia positron emission tomography/computed tomography (PET/CT) is frequently utilized to evaluate cardiac sarcoidosis (CS) but findings can reflect other forms of myocardial inflammation or altered myocardial metabolic activity. Herein, we present five cases where cardiac PET findings suggested CS, but right ventricular endomyocardial biopsy samples revealed ATTR-type cardiac amyloidosis.

Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction.

Aims: Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm's long-term efficacy and potential bias in the absence of retraining. Methods and results: Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90-0.92) with minimal performance difference between sexes. Patients with a 'normal sinus rhythm' electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion: The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Ablation of Refractory Ventricular Tachycardia Using Intramyocardial Needle Delivered Heated Saline-Enhanced Radiofrequency Energy: A First-in-Man Feasibility Trial.

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.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European heart rhythm association and European society of cardiology working group on ecardiology in collaboration with the heart rhythm society, Asia pacific heart rhythm society, Latin American Heart rhythm society and computing in cardiology

Abstract 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. catheter­electrode 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.

Routine Laboratory Biomarkers As Prognostic Indicators of Cardiac Sarcoidosis Outcomes.

Background: Biomarkers to monitor disease activity and predict major adverse cardiac events (MACE) in CS have not been described previously. We aimed to identify biomarkers to predict MACE in cardiac sarcoidosis (CS). Methods: Patients (N=232) diagnosed with CS were retrospectively enrolled. Biomarkers including angiotensin-converting enzyme (ACE), N-terminal brain natriuretic peptide (NT-proBNP), troponin T, and creatinine levels were evaluated against a primary end point of left ventricular assist device implantation, heart transplantation, or death, and a secondary end point of cardiac hospitalization-free survival. Results: Troponin T (hazard ratio [HR], 1.06 per 0.01 ng/mL; P=.006), NT-proBNP (HR, 1.31 per 1,000 pg/mL; P<.001), and creatinine (HR, 4.02 per mg/dL; P=.01) were associated with the primary end point, even after adjusting for ejection fraction. NT-proBNP, B-type natriuretic peptide (BNP), creatinine, albumin, and calcium were associated with the secondary end point (P<.05). ACE levels were associated with presence of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging (mean difference, 14.7; P=.03); 1,25 dihydroxyvitamin D (1,25-OHVit-D) was associated with uptake on cardiac 18F-flurodeoxyglucose position emission tomography (FDG-PET, P=.03). Conclusions: Troponin T, NT-proBNP, and creatinine predict clinically significant outcomes in CS. ACE levels correlated with LGE on CMR, and 1,25-OHVit-D levels correlated with FDG-PET activity.

Artificial intelligence-derived cardiac ageing is associated with cardiac events post-heart transplantation.

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

Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope.

Aims: Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results: One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, <40%, or <50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ± 13.8; 61% male, BMI: 30.0 ± 5.4), eight had EF≤40%, and six had EF 40-50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80-0.97) for EF≤35%, 0.85 (CI: 0.75-0.95) for EF≤40%, and 0.81 (CI: 0.71-0.90) for EF < 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84-0.97) for EF≤35%, 0.89 (CI: 0.83-0.96) for EF≤40%, and 0.84 (CI: 0.73-0.94) for EF < 50%. Conclusion: An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.

Artificial intelligence-electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode.

Aims: Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results: We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2-5 years prior to AF, 23.6% 1-2 years prior to AF, 34.0% 0-3 months prior to AF, 40.9% 0-3 months after AF, 35.2% 1-2 years after AF, and 42.2% 2-5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA2DS2-VASc score ≥4. Conclusion: The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology.

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.

Characteristics and outcomes of ventricular tachycardia and premature ventricular contractions ablation in patients with prior mitral valve surgery.

BACKGROUND: 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. OBJECTIVE: To investigate the characteristics, safety, and outcomes of radiofrequency CA in patients with prior MVS and ventricular arrhythmias (VA). METHODS: 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. RESULTS: 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. CONCLUSION: CA of VAs can be performed safely and effectively in patients with MVS.

Detection of Left Atrial Myopathy Using Artificial Intelligence-Enabled Electrocardiography.

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<0.001). In the population as a whole, each 10% increase in AI-probability was associated with a 12% greater risk of death (hazard ratio, 1.12 [95% CI, 1.08-1.17]; P<0.001) during long-term follow-up, which was no longer significant after adjustments for baseline characteristics. 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.

Outcomes Associated With Catheter Ablation of Ventricular Tachycardia in Patients With Cardiac Sarcoidosis.

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] <50%). Exposures: Catheter ablation of CS-associated VT and, as appropriate, medical treatment. Main Outcomes and Measures: Immediate and short-term outcomes included procedural success, elimination of VT storm, and reduction in defibrillator shocks. The primary long-term outcome was the composite of VT recurrence, heart transplant (HT), or death. Results: Complete procedural success (no inducible VT postablation) was achieved in 85 patients (54%). Sixty-five patients (41%) had preablation VT storm that did not recur postablation in 53 (82%). Defibrillator shocks were significantly reduced from a median (IQR) of 2 (1-5) to 0 (0-0) in the 30 days before and after ablation (P < .001). During median (IQR) follow-up of 2.5 (1.1-4.9) years, 73 patients (46%) experienced VT recurrence and 81 (51%) experienced the composite primary outcome. One- and 2-year rates of survival free of VT recurrence, HT, or death were 60% and 52%, respectively. EF less than 50% and myocardial inflammation on preprocedural 18F-fluorodeoxyglucose positron emission tomography were significantly associated with adverse prognosis in multivariable analysis for the primary outcome (HR, 2.24; 95% CI, 1.37-3.64; P = .001 and HR, 2.93; 95% CI, 1.31-6.55; P = .009, respectively). History of hypertension was associated with a favorable long-term outcome (adjusted HR, 0.51; 95% CI, 0.28-0.92; P = .02). Conclusions and Relevance: In this observational study of selected patients with CS and VT, catheter ablation was associated with reductions in defibrillator shocks and recurrent VT storm. Preablation LV dysfunction and myocardial inflammation were associated with adverse long-term prognosis. These data support the role of catheter ablation in conjunction with medical therapy in the management of CS-associated VT.