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.