Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found
. An inexpensive, ...noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
Radiofrequency ablation in the outflow tract and great arteries is increasingly performed to treat a variety of symptomatic cardiac arrhythmias. The regional anatomy of these structures is among the ...most complex of those encountered by cardiac electrophysiologists. An exact appreciation of the relationships between these overlapping structures and their proximity to the coronary arterial and conduction system is essential for rational, safe, and effective ablation for these arrhythmias. A supravalvar portion of the aorta is a unique site for arrhythmia origin where the arrhythmogenic substrate for atrial arrhythmias, ventricular arrhythmias, and accessory pathways may all be located. Discussed in this review are the main principles of outflow tract and supravalvar arrhythmia, and these are correlated with fluoroscopy, electrograms, and electrocardiography that help guide the invasive electrophysiologist.
Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.
This study sought to develop an artificial intelligence approach for the detection of HCM based on ...12-lead electrocardiography (ECG).
A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects.
In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval CI: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN’s AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively.
ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening.
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Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited ...by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.
We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs.
We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86–0·88), sensitivity of 79·0% (77·5–80·4), specificity of 79·5% (79·0–79·9), F1 score of 39·2% (38·1–40·3), and overall accuracy of 79·4% (79·0–79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90–0·91), sensitivity to 82·3% (80·9–83·6), specificity to 83·4% (83·0–83·8), F1 score to 45·4% (44·2–46·5), and overall accuracy to 83·3% (83·0–83·7).
An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation.
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Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional ...neural network (CNN) could be trained through a process called deep learning to predict a person's age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health.
We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age. The networks were tested on a separate cohort of 275 056 patients. Subsequently, 100 randomly selected patients with multiple ECGs over the course of decades were identified to assess within-individual accuracy of CNN age estimation.
Of 275 056 patients tested, 52% were males and mean age was 58.6±16.2 years. For sex classification, the model obtained 90.4% classification accuracy with an area under the curve of 0.97 in the independent test data. Age was estimated as a continuous variable with an average error of 6.9±5.6 years (R-squared =0.7). Among 100 patients with multiple ECGs over the course of at least 2 decades of life, most patients (51%) had an average error between real age and CNN-predicted age of <7 years. Major factors seen among patients with a CNN-predicted age that exceeded chronologic age by >7 years included: low ejection fraction, hypertension, and coronary disease (P<0.01). In the 27% of patients where correlation was >0.8 between CNN-predicted and chronologic age, no incident events occurred over follow-up (33±12 years).
Applying artificial intelligence to the ECG allows prediction of patient sex and estimation of age. The ability of an artificial intelligence algorithm to determine physiological age, with further validation, may serve as a measure of overall health.
The application of brief high voltage electrical pulses to tissue can lead to an irreversible or reversible electroporation effect in a cell-specific manner. In the management of ventricular ...arrhythmias, the ability to target different tissue types, specifically cardiac conduction tissue (His-Purkinje System) vs. cardiac myocardium would be advantageous. We hypothesize that pulsed electric fields (PEFs) can be applied safely to the beating heart through a catheter-based approach, and we tested whether the superficial Purkinje cells can be targeted with PEFs without injury to underlying myocardial tissue.
In an acute (n = 5) and chronic canine model (n = 6), detailed electroanatomical mapping of the left ventricle identified electrical signals from myocardial and overlying Purkinje tissue. Electroporation was effected via percutaneous catheter-based Intracardiac bipolar current delivery in the anesthetized animal. Repeat Intracardiac electrical mapping of the heart was performed at acute and chronic time points; followed by histological analysis to assess effects.
PEF demonstrated an acute dose-dependent functional effect on Purkinje, with titration of pulse duration and/or voltage associated with successful acute Purkinje damage. Electrical conduction in the insulated bundle of His (n = 2) and anterior fascicle bundle (n = 2), was not affected. At 30 days repeat cardiac mapping demonstrated resilient, normal electrical conduction throughout the targeted area with no significant change in myocardial amplitude (pre 5.9 ± 1.8 mV, 30 days 5.4 ± 1.2 mV, p = 0.92). Histopathological analysis confirmed acute Purkinje fiber targeting, with chronic studies showing normal Purkinje fibers, with minimal subendocardial myocardial fibrosis.
PEF provides a novel, safe method for non-thermal acute modulation of the Purkinje fibers without significant injury to the underlying myocardium. Future optimization of this energy delivery is required to optimize conditions so that selective electroporation can be utilized in humans the treatment of cardiac disease.
Objectives The aim of this study was to investigate the prevalence of mitral valve prolapse (MVP) and its association with ventricular arrhythmias in a cohort with “unexplained” out-of-hospital ...cardiac arrest. Background Ventricular arrhythmias are an important cause of sudden unexpected death in the young. The role of MVP in sudden unexpected death remains controversial. Methods Of 1,200 patients evaluated between July 2000 and December 2009 in the Mayo Clinic’s Long QT Syndrome/Genetic Heart Rhythm Clinic, all 24 (16 women, median age 33.5 years) with idiopathic out-of-hospital cardiac arrest (i.e., negative for ischemia, cardiomyopathy, and channelopathy) were reviewed. Results All 24 patients had implantable cardioverter-defibrillators (ICDs). Out-of-hospital cardiac arrest was the sentinel event in 22 (92%). Bileaflet MVP was found in 10 (42%). Compared with patients with normal mitral valves, patients with bileaflet MVP: 1) were over-represented by women (9 of 10 90% vs. 7 of 14 50%, p = 0.04); 2) had a higher prevalence of biphasic or inverted T waves (7 of 9 77.8% vs. 4 of 14 29%, p = 0.04); and 3) on Holter interrogation had higher prevalence of ventricular bigeminy (9 of 9 100% vs. 1 of 10 10%, p < 0.0001), ventricular tachycardia (7 of 9 78% vs. 1 of 10 10%, p = 0.006), and premature ventricular contractions originating from the outflow tract alternating with the papillary muscle or fascicular region (7 of 9 78% vs. 2 of 10 20%, p = 0.02). Over a median 1.8 years (range: 0.1 to 11.9 years) from ICD placement, 13 of 24 patients (54%) received appropriate ventricular fibrillation–terminating ICD shocks. Only bileaflet MVP was associated with ventricular fibrillation recurrences requiring ICD therapy on follow-up (logistic regression odds ratio: 7.2; 95% confidence interval: 1.1 to 48; p = 0.028). Conclusions The authors describe a “malignant” subset of patients with MVP who experienced life-threatening ventricular arrhythmias. This phenotype is characterized by bileaflet MVP, female sex, and frequent complex ventricular ectopic activity, including premature ventricular contractions of the outflow tract alternating with papillary muscle or fascicular origin.
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