Albert, D.J.

Background: ECG interpretation is crucial in medical practice, but professionals struggle with achieving and maintaining competency. Identifying proficiency gaps aids in designing educational interventions. Methods: Medical professionals from various disciplines interpreted 30 12-lead ECGs containing commonly taught urgent and non-urgent findings. Performance metrics evaluated were overall adjusted score (% of correctly identified findings using a point-value score based on clinical relevance), unadjusted score (% of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on an ordinal scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: Among 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FIT), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, 249 (21%) allied health professionals, 571 (47%) physicians, and 453 (38%) non-physicians. Participants had a mean adjusted score of 46.9% (± 15.9%), unadjusted score of 56.4% (± 17.2%), interpretation time of 142 seconds (± 67 seconds), and confidence of 0.83 (± 0.53) (Table 1) . Performance varied across groups; cardiology FIT had superior performance in all metrics. Physicians generally outperformed non-physicians, with higher overall adjusted score (52% vs. 43%; p<0.01), unadjusted score (62% vs. 52%; p<0.01), and confidence (0.91 vs. 0.80; p<0.01). Unadjusted score for PCPs was higher than nurses and APPs (58% vs. 47% and 51%; p<0.01) but lower than resident physicians (58% vs. 60%; p<0.01). Allied health professionals outperformed nurses and APPs and closely matched resident physicians and PCPs. Conclusions: Significant gaps in ECG interpretation proficiency exist, emphasizing the need for comprehensive, scalable, and accessible educational tools.

Abstract Background Interval duration measurements (IDMs) were compared between standard 12‐lead electrocardiograms (ECGs) and 6‐lead ECGs recorded with AliveCor's KardiaMobile 6L, a hand‐held mobile device designed for use by patients at home. Methods Electrocardiograms were recorded within, on average, 15 min from 705 patients in Mayo Clinic's Windland Smith Rice Genetic Heart Rhythm Clinic. Interpretable 12‐lead and 6‐lead recordings were available for 685 out of 705 (97%) eligible patients. The most common diagnosis was congenital long QT syndrome (LQTS, 343/685 [50%]), followed by unaffected relatives and patients (146/685 [21%]), and patients with other genetic heart diseases, including hypertrophic cardiomyopathy (36 [5.2%]), arrhythmogenic cardiomyopathy (23 [3.4%]), and idiopathic ventricular fibrillation (14 [2.0%]). IDMs were performed by a central ECG laboratory using lead II with a semi‐automated technique. Results Despite differences in patient position (supine for 12‐lead ECGs and sitting for 6‐lead ECGs), mean IDMs were comparable, with mean values for the 12‐lead and 6‐lead ECGs for QTcF, heart rate, PR, and QRS differing by 2.6 ms, −5.5 beats per minute, 1.0 and 1.2 ms, respectively. Despite a modest difference in heart rate, intervals were close enough to allow a detection of clinically meaningful abnormalities. Conclusions The 6‐lead hand‐held device is potentially useful for a clinical follow‐up of remote patients, and for a safety follow‐up of patients participating in clinical trials who cannot visit the investigational site. This technology may extend the use of 12‐lead ECG recordings during the current COVID‐19 pandemic as remote patient monitoring becomes more common in virtual or hybrid‐design clinical studies.

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.