Noseworthy, Peter A.

The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

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

Background: In patients with heart failure and atrial fibrillation (AF), several clinical trials have reported improved outcomes, including freedom from AF recurrence, quality of life, and survival, with catheter ablation. This article describes the treatment-related outcomes of the AF patients with heart failure enrolled in the CABANA trial (Catheter Ablation Versus Antiarrhythmic Drug Therapy for Atrial Fibrillation). Methods: The CABANA trial randomized 2204 patients with AF who were ≥65 years old or <65 years old with ≥1 risk factor for stroke at 126 sites to ablation with pulmonary vein isolation or drug therapy including rate or rhythm control drugs. Of these, 778 (35%) had New York Heart Association class >II at baseline and form the subject of this article. The CABANA trial’s primary end point was a composite of death, disabling stroke, serious bleeding, or cardiac arrest. Results: Of the 778 patients with heart failure enrolled in CABANA, 378 were assigned to ablation and 400 to drug therapy. Ejection fraction at baseline was available for 571 patients (73.0%), and 9.3% of these had an ejection fraction <40%, whereas 11.7% had ejection fractions between 40% and 50%. In the intention-to-treat analysis, the ablation arm had a 36% relative reduction in the primary composite end point (hazard ratio, 0.64 [95% CI, 0.41–0.99]) and a 43% relative reduction in all-cause mortality (hazard ratio, 0.57 [95% CI, 0.33–0.96]) compared with drug therapy alone over a median follow-up of 48.5 months. AF recurrence was decreased with ablation (hazard ratio, 0.56 [95% CI, 0.42–0.74]). The adjusted mean difference for the AFEQT (Atrial Fibrillation Effect on Quality of Life) summary score averaged over the entire 60-month follow-up was 5.0 points, favoring the ablation arm (95% CI, 2.5–7.4 points), and the MAFSI (Mayo Atrial Fibrillation-Specific Symptom Inventory) frequency score difference was –2.0 points, favoring ablation (95% CI, –2.9 to –1.2). Conclusions: In patients with AF enrolled in the CABANA trial who had clinically diagnosed stable heart failure at trial entry, catheter ablation produced clinically important improvements in survival, freedom from AF recurrence, and quality of life relative to drug therapy. These results, obtained in a cohort most of whom had preserved left ventricular function, require independent trial verification. Registration: URL: https://www.clinicaltrials.gov/ct2/show/NCT00911508 ; Unique identifier: NCT0091150.

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute’s virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.

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.

Abstract 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.

AbstractBackgroundA subset of patients with hypertrophic cardiomyopathy (HCM) is at high risk of sudden cardiac death (SCD). Practice guidelines endorse use of a risk calculator, which requires entry of left atrial (LA) diameter. However, American Society of Echocardiography (ASE) guidelines recommend the use of LA volume index (LAVI) for routine quantification of LA size. The aims of this study were to (a) develop a model to estimate LA diameter from LAVI and (b) evaluate whether substitution of measured LA diameter by estimated LA diameter derived from LAVI reclassifies HCM‐SCD risk.MethodsThe study cohort was comprised of 500 randomly selected HCM patients who underwent transthoracic echocardiography (TTE). LA diameter and LAVI were measured offline using digital clips from TTE. Linear regression models were developed to estimate LA diameter from LAVI. A European Society of Cardiology endorsed equation estimated SCD risk, which was measured using LA diameter and estimated LA diameter derived from LAVI.ResultsThe mean LAVI was 48.5 ± 18.8 mL/m2. The derived LA diameter was 45.1 mm (SD: 5.5 mm), similar to the measured LA diameter (45.1 mm, SD: 7.1 mm). Median SCD risk at 5 years estimated by measured LA diameter was 2.22% (interquartile range (IQR): 1.39, 3.56), while median risk calculated by estimated LA diameter was 2.18% (IQR: 1.44, 3.52). 476/500 (95%) patients maintained the same risk classification regardless of whether the measured or estimated LA diameter was used.ConclusionsSubstitution of measured LA diameter by estimated LA diameter in the HCM‐SCD calculator did not reclassify risk.