Artificial intelligence-based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram.

Aims: Smartwatch electrocardiograms (SW ECGs) have been identified as a non-invasive solution to assess abnormal heart rhythm, especially atrial arrhythmias (AAs) that are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of deep neural network (DNN) algorithms, particularly for specific populations encountered in clinical cardiology practice. Methods and results: A total of 400 patients from the electrophysiology department of one tertiary care hospital were included in two similar clinical trials (respectively, 200 patients per study). Simultaneous ECGs were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The SW ECGs were processed by using the DNN and with the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs (12L ECGs) were adjudicated by an expert electrophysiologist. The performance of the DNN was assessed vs. the expert interpretation of the 12L ECG, and inconclusive rates were reported. Overall, the DNN and the Apple app presented, respectively, a sensitivity of 91% [95% confidence interval (CI) 85-95%] and 61% (95% CI 44-75%) with a specificity of 95% (95% CI 91-97%) and 97% (95% CI 93-99%) when compared with the physician 12L ECG interpretation. The DNN was able to provide a diagnosis on 99% of ECGs, while the Apple app was able to classify only 78% of strips (22% of inconclusive diagnosis). Conclusion: In this study, by including patients from a cardiology department, a DNN-based algorithm applied to an SW ECG provided an accurate diagnosis for AA detection on virtually all tracings, outperforming the SW algorithm.

Deep neural networks detect regional wall motion abnormalities and preclinical cardiovascular disease from 12-lead ECGs.

Background: Identifying regional wall motion abnormalities (RWMAs) is critical for diagnosing and risk stratifying patients with cardiovascular disease, particularly ischemic heart disease. We hypothesized that a deep neural network could accurately identify patients with regional wall motion abnormalities from a readily available standard 12-lead electrocardiogram (ECG). Methods: This observational, retrospective study included patients who were treated at Beth Israel Deaconess Medical Center and had an ECG and echocardiogram performed within 14 days of each other between 2008 and 2019. We trained a convolutional neural network to detect the presence of RWMAs, qualitative global right ventricular (RV) hypokinesis, and varying degrees of left ventricular dysfunction (left ventricular ejection fraction [LVEF] ≤50%, LVEF ≤40%, and LVEF ≤35%) identified by echocardiography, using ECG data alone. Patients were randomly split into development (80%) and test sets (20%). Model performance was assessed using area under the receiver operating characteristic curve (AUC). Cox proportional hazard models adjusted for age and sex were performed to estimate the risk of future acute coronary events. Results: The development set consisted of 19,837 patients (mean age 66.7±16.4; 46.7% female) and the test set comprised of 4,953 patients (mean age 67.5±15.8 years; 46.5% female). On the test dataset, the model accurately identified the presence of RWMA, RV hypokinesis, LVEF ≤50%, LVEF ≤40%, and LVEF ≤35% with AUCs of 0.87 (95% CI 0.858-0.882), 0.888 (95% CI 0.878-0.899), 0.923 (95% CI 0.914-0.933), 0.93 (95% CI 0.921-0.939), and 0.876 (95% CI 0.858-0.896), respectively. Among patients with normal biventricular function at the time of the index ECG, those classified as having RMWA by the model were 3 times the risk (age- and sex-adjusted hazard ratio, 2.8; 95% CI 1.9-3.9) for future acute coronary events compared to those classified as negative. Conclusions: We demonstrate that a deep neural network can help identify regional wall motion abnormalities and reduced LV function from a 12-lead ECG and could potentially be used as a screening tool for triaging patients who need either initial or repeat echocardiographic imaging.

Safety, feasibility, and prognostic value of stress perfusion CMR in patients with MR-conditional pacemaker.

AIMS: To assess the safety, feasibility, and prognostic value of stress cardiovascular magnetic resonance (CMR) in patients with pacemaker (PM). METHODS AND RESULTS: Between 2008 and 2021, we conducted a bi-centre longitudinal study with all consecutive patients with MR-conditional PM referred for vasodilator stress CMR at 1.5 T in the Institut Cardiovasculaire Paris Sud and Lariboisiere University Hospital. They were followed for the occurrence of major adverse cardiovascular events (MACE) defined as cardiac death or non-fatal myocardial infarction. Cox regression analyses were performed to determine the prognostic value of CMR parameters. The quality of CMR was rated by two observers blinded to clinical details. Of 304 patients who completed the CMR protocol, 273 patients (70% male, mean age 71 ± 9 years) completed the follow-up (median [interquartile range], 7.1 [5.4-7.5] years). Among those, 32 experienced a MACE (11.7%). Stress CMR was well tolerated with no significant change in lead thresholds or pacing parameters. Overall, the image quality was rated good or excellent in 84.9% of segments. Ischaemia and late gadolinium enhancement (LGE) were significantly associated with the occurrence of MACE (hazard ratio, HR: 11.71 [95% CI: 4.60-28.2]; and HR: 5.62 [95% CI: 2.02-16.21], both P < 0.001). After adjustment for traditional risk factors, ischaemia and LGE were independent predictors of MACE (HR: 5.08 [95% CI: 2.58-14.0]; and HR: 2.28 [95% CI: 2.05-3.76]; both P < 0.001). CONCLUSION: Stress CMR is safe, feasible and has a good discriminative prognostic value in consecutive patients with PM.

Evaluation of an Ambulatory ECG Analysis Platform Using Deep Neural Networks in Routine Clinical Practice.

Background Holter analysis requires significant clinical resources to achieve a high-quality diagnosis. This study sought to assess whether an artificial intelligence (AI)-based Holter analysis platform using deep neural networks is noninferior to a conventional one used in clinical routine in detecting a major rhythm abnormality. Methods and Results A total of 1000 Holter (24-hour) recordings were collected from 3 tertiary hospitals. Recordings were independently analyzed by cardiologists for the AI-based platform and by electrophysiologists as part of clinical practice for the conventional platform. For each Holter, diagnostic performance was evaluated and compared through the analysis of the presence or absence of 5 predefined cardiac abnormalities: pauses, ventricular tachycardia, atrial fibrillation/flutter/tachycardia, high-grade atrioventricular block, and high burden of premature ventricular complex (>10%). Analysis duration was monitored. The deep neural network-based platform was noninferior to the conventional one in its ability to detect a major rhythm abnormality. There were no statistically significant differences between AI-based and classical platforms regarding the sensitivity and specificity to detect the predefined abnormalities except for atrial fibrillation and ventricular tachycardia (atrial fibrillation, 0.98 versus 0.91 and 0.98 versus 1.00; pause, 0.95 versus 1.00 and 1.00 versus 1. 00; premature ventricular contractions, 0.96 versus 0.87 and 1.00 versus 1.00; ventricular tachycardia, 0.97 versus 0.68 and 0.99 versus 1.00; atrioventricular block, 0.93 versus 0.57 and 0.99 versus 1.00). The AI-based analysis was >25% faster than the conventional one (4.4 versus 6.0 minutes; P<0.001). Conclusions These preliminary findings suggest that an AI-based strategy for the analysis of Holter recordings is faster and at least as accurate as a conventional analysis by electrophysiologists.

Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network.

Aims: Atrial fibrillation (AF) is associated with significant morbidity but remains underdiagnosed. A 24 h ambulatory electrocardiogram (ECG) is largely used as a tool to document AF but yield remains limited. We hypothesize that a deep learning model can identify patients at risk of AF in the 2 weeks following a 24 h ambulatory ECG with no documented AF. Methods and results: We identified a training set of Holter recordings of 7-15 days duration, in which no AF could be found in the first 24 h. We trained a neural network to predict the presence or absence of AF in the 15 following days, using only the first 24 h of the recording. We evaluated the neural network on a testing set and an external data set not used during algorithm development. In the testing data set, out of 9993 Holters with no AF on the first day, we found 361 (4%) recordings with AF within the 15 subsequent days of monitoring [5808, 218 (4%), respectively in the external data set]. The neural network could discriminate future AF with an area under the receiver operating curve, a sensitivity, and specificity of 79.4%, 76%, and 69%, respectively (75.8%, 78%, and 58% in the external data set), and outperformed ECG features previously shown to be predictive of AF. Conclusion: We show here the very first study of short-term AF prediction using 24 h Holter monitoring. This could help identify patients who would benefit the most from longer recordings and proactively initiate treatment and AF mitigation strategies in high-risk patients.

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.

BACKGROUND: QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 2019 (COVID-19). For the provision of widespread use, surrogates for 12­lead ECG QTc assessment may be useful. This prospective observational study compared QTc duration assessed by artificial intelligence (AI-QTc) (Cardiologs®, Paris, France) on smartwatch single­lead electrocardiograms (SW-ECGs) with those measured on 12­lead ECGs, in patients with early stage COVID-19 treated with a hydroxychloroquine-azithromycin regimen. METHODS: Consecutive patients with COVID-19 who needed hydroxychloroquine-azithromycin therapy, received a smartwatch (Withings Move ECG®, Withings, France). At baseline, day-6 and day-10, a 12­lead ECG was recorded, and a SW-ECG was transmitted thereafter. Throughout the drug regimen, a SW-ECG was transmitted every morning at rest. Agreement between manual QTc measurement on a 12­lead ECG and AI-QTc on the corresponding SW-ECG was assessed by the Bland-Altman method. RESULTS: 85 patients (30 men, mean age 38.3 ± 12.2 years) were included in the study. Fair agreement between manual and AI-QTc values was observed, particularly at day-10, where the delay between the 12­lead ECG and the SW-ECG was the shortest (-2.6 ± 64.7 min): 407 ± 26 ms on the 12­lead ECG vs 407 ± 22 ms on SW-ECG, bias -1 ms, limits of agreement -46 ms to +45 ms; the difference between the two measures was <50 ms in 98.2% of patients. CONCLUSION: In real-world epidemic conditions, AI-QTc duration measured by SW-ECG is in fair agreement with manual measurements on 12­lead ECGs. Following further validation, AI-assisted SW-ECGs may be suitable for QTc interval monitoring. REGISTRATION: ClinicalTrial.govNCT04371744.

A deep neural network for 12-lead electrocardiogram interpretation outperforms a conventional algorithm, and its physician overread, in the diagnosis of atrial fibrillation.

BACKGROUND: Automated electrocardiogram (ECG) interpretations may be erroneous, and lead to erroneous overreads, including for atrial fibrillation (AF). We compared the accuracy of the first version of a new deep neural network 12-Lead ECG algorithm (Cardiologs®) to the conventional Veritas algorithm in interpretation of AF. METHODS: 24,123 consecutive 12-lead ECGs recorded over 6 months were interpreted by 1) the Veritas® algorithm, 2) physicians who overread Veritas® (Veritas®â€¯+ physician), and 3) Cardiologs® algorithm. We randomly selected 500 out of 858 ECGs with a diagnosis of AF according to either algorithm, then compared the algorithms' interpretations, and Veritas®â€¯+ physician, with expert interpretation. To assess sensitivity for AF, we analyzed a separate database of 1473 randomly selected ECGs interpreted by both algorithms and by blinded experts. RESULTS: Among the 500 ECGs selected, 399 had a final classification of AF; 101 (20.2%) had ≥1 false positive automated interpretation. Accuracy of Cardiologs® (91.2%; CI: 82.4-94.4) was higher than Veritas® (80.2%; CI: 76.5-83.5) (p < 0.0001), and equal to Veritas®â€¯+ physician (90.0%, CI:87.1-92.3) (p = 0.12). When Veritas® was incorrect, accuracy of Veritas®â€¯+ physician was only 62% (CI 52-71); among those ECGs, Cardiologs® accuracy was 90% (CI: 82-94; p < 0.0001). The second database had 39 AF cases; sensitivity was 92% vs. 87% (p = 0.46) and specificity was 99.5% vs. 98.7% (p = 0.03) for Cardiologs® and Veritas® respectively. CONCLUSION: Cardiologs® 12-lead ECG algorithm improves the interpretation of atrial fibrillation.