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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.

Background: Atrial fibrillation (AF) burden and duration appear to be related to stroke risk. A wearable consumer electronic device could provide long-term assessment of these measures inexpensively and noninvasively. This study compares the accuracy of an AF-sensing watch (AFSW; Apple Watch with KardiaBand) with simultaneous recordings from an insertable cardiac monitor (ICM; Reveal LINQ). Methods: SmartRhythm 2.0, a convolutional neural network, was trained on anonymized data of heart rate, activity level, and ECGs from 7500 AliveCor users. The network was validated on data collected in 24 patients with ICMs and a history of paroxysmal AF who simultaneously wore the AFSW with SmartRhythm 0.1 software. The primary outcome was sensitivity of the AFSW for AF episodes ≥1 hour. Secondary end points included sensitivity of the AFSW for detection of AF by subject and sensitivity for total AF duration across all subjects. Subjects with >50% false-positive AF episodes on ICM were excluded. Results: We analyzed 31 348.9 hours (mean (SD), 11.3 (4.4) hours/day) of simultaneous AFSW and ICM recordings in 24 patients. The ICM detected 82 episodes of AF ≥1 hour while the AFSW was worn, with a total duration of 1127.1 hours. Of these, the SmartRhythm 2.0 neural network detected 80 episodes (episode sensitivity, 97.5%) with a total duration of 1101.1 hours (duration sensitivity, 97.7%). Three of the 18 subjects with AF ≥1 hour had AF only when the watch was not being worn (patient sensitivity, 83.3%; or 100% during time worn). Positive predictive value for AF episodes was 39.9%. Conclusions: An AFSW is highly sensitive for detection of AF and assessment of AF duration in an ambulatory population when compared with an ICM. Such devices may represent an inexpensive, noninvasive approach to long-term AF surveillance and management.

Peak aortic blood acceleration is recognized to be a sensitive index of global left ventricular performance. In the present study peak acceleration was assessed noninvasively in patients with a continuous-wave Doppler velocity meter. Peak aortic blood velocity and peak blood acceleration were measured by placing the ultrasonic transducer at the suprasternal notch. Measurements were obtained in 36 patients undergoing diagnostic cardiac catheterization. Peak velocity and acceleration were measured at rest just before left ventriculography. In patients with ejection fractions greater than 60%, peak acceleration was 19 +/- 5 m/sec/sec. In patients with ejection fractions of 41% to 60%, peak acceleration was lower, at 12 +/- 2 m/sec/sec (p less than .001). In patients with ejection fractions of 40% or less, peak acceleration (8 +/- 2 m/sec/sec) was markedly lower than in patients with ejection fractions greater than 60% (p less than .001). Peak acceleration showed a good linear correlation with ejection fraction (r = .90), and a better power fit (r = .93). These results indicate that peak acceleration, measured noninvasively with a continuous-wave Doppler velocity meter, is a useful indicator of global left ventricular performance.