Artificial intelligence for detection of ventricular oversensing: Machine learning approaches for noise detection within nonsustained ventricular tachycardia episodes remotely transmitted by pacemakers and implantable cardioverter-defibrillators.

BACKGROUND: Pacemakers (PMs) and implantable cardioverter-defibrillators (ICDs) increasingly automatically record and remotely transmit nonsustained ventricular tachycardia (NSVT) episodes, which may reveal ventricular oversensing. OBJECTIVES: We aimed to develop and validate a machine learning algorithm that accurately classifies NSVT episodes transmitted by PMs and ICDs in order to lighten health care workload burden and improve patient safety. METHODS: PMs or ICDs (Boston Scientific, St Paul, MN) from 4 French hospitals with ≥1 transmitted NSVT episode were split into 3 subgroups: training set, validation set, and test set. Each NSVT episode was labeled as either physiological or nonphysiological. Four machine learning algorithms-2DTF-CNN, 2D-DenseNet, 2DTF-VGG, and 1D-AgResNet-were developed using training and validation data sets. Accuracies of the classifiers were compared with an analysis of the remote monitoring team of the Bordeaux University Hospital using F2 scores (favoring sensitivity over predictive positive value) using an independent test set. RESULTS: A total of 807 devices transmitted 10,471 NSVT recordings (82% ICD; 18% PM), of which 87 devices (10.8%) transmitted 544 NSVT recordings with nonphysiological signals. The classification by the remote monitoring team resulted in an F2 score of 0.932 (sensitivity 95%; specificity 99%) The 4 machine learning algorithms showed high and comparable F2 scores (2DTF-CNN: 0.914; 2D-DenseNet: 0.906; 2DTF-VGG: 0.863; 1D-AgResNet: 0.791), and only 1D-AgResNet had significantly different labeling from that of the remote monitoring team. CONCLUSION: Machine learning algorithms were accurate in detecting nonphysiological signals within electrograms transmitted by PMs and ICDs. An artificial intelligence approach may render remote monitoring less resourceful and improve patient safety.

Feasibility and Diagnostic Value of Recording Smartwatch Electrocardiograms in Neonates and Children.

OBJECTIVE: The objective of this study was to evaluate the agreement of smartwatch-derived single-lead electrocardiogram (ECG) recordings with 12-lead ECGs for diagnosing electrocardiographic abnormalities. STUDY DESIGN: A 12-lead ECG and an ECG using Apple Watch were obtained in 110 children (aged 1 week to 16 years) with normal (n = 75) or abnormal (n = 35) 12-lead ECGs (atrioventricular block [7], supraventricular tachycardia [SVT] {5}, bundle branch block [12], ventricular preexcitation [6], long QT [5]). In children aged <6 years, the ECG recording was performed with the active participation of an adult who applied the neonate or child's finger to the crown of the watch. In older children, tracings were obtained after brief teaching without adult guidance. All 12-lead ECGs were independently evaluated by 2 blinded cardiologists. Apple Watch ECGs were independently evaluated by another blinded cardiologist. RESULTS: In 109 children (99.1%), the smartwatch tracing was of sufficient quality for evaluation. Smartwatch tracings were 84% sensitive and 100% specific for the detection of an abnormal ECG. All 75 normal tracings were correctly identified. Of the 35 children with abnormalities on 12-lead ECGs, 5 (14%) were missed, most often because of baseline wander and artifacts. Rhythm disorders (atrioventricular block or SVT) and bundle branch blocks were correctly detected in most cases (11 of 12 and 11 of 12, respectively); preexcitation and long QT was detected in 4 of 6 and 4 of 5, respectively. CONCLUSION: Smartwatch ECGs recorded with parental assistance in children aged up to 6 years and independently in older children have the potential to detect clinically relevant conditions.

Using a smartwatch to record an electrocardiogram in the pediatric population.

The accuracy of smartwatch ECG recordings in adults has been demonstrated primarily in the automated diagnosis of atrial fibrillation. While the detection of atrial fibrillation is a priority among adults given the arrhythmia's prevalence and actionable ramifications, the potential value of smartwatch ECG recordings in children differs considerably. In this case series, we will describe some examples of smartwatch ECGs recorded in children, highlighting the feasibility and potential indications of this technology in the pediatric population.

Safety of hydroxychloroquine and darunavir or lopinavir in COVID-19 infection.

BACKGROUND: Combination therapy with hydroxychloroquine and darunavir/ritonavir or lopinavir/ritonavir has been suggested as an approach to improve the outcome of patients with moderate/severe COVID-19 infection. OBJECTIVES: To examine the safety of combination therapy with hydroxychloroquine and darunavir/ritonavir or lopinavir/ritonavir. METHODS: This was an observational cohort study of patients hospitalized for COVID-19 pneumonia treated with hydroxychloroquine and darunavir/ritonavir or lopinavir/ritonavir. Clinical evaluations, electrocardiograms and the pharmacokinetics of hydroxychloroquine, darunavir and lopinavir were examined according to clinical practice and guidelines. RESULTS: Twenty-one patients received hydroxychloroquine with lopinavir/ritonavir (median age 68 years; 10 males) and 25 received hydroxychloroquine with darunavir/ritonavir (median age 71 years; 15 males). During treatment, eight patients (17.4%) developed ECG abnormalities. Ten patients discontinued treatment, including seven for ECG abnormalities a median of 5 (range 2-6) days after starting treatment. All ECG abnormalities reversed 1-2 days after interrupting treatment. Four patients died within 14 days. ECG abnormalities were significantly associated with age over 70 years, coexisting conditions (such as hypertension, chronic cardiovascular disease and kidney failure) and initial potential drug interactions, but not with the hydroxychloroquine concentration. CONCLUSIONS: Of the patients with COVID-19 who received hydroxychloroquine with lopinavir or darunavir, 17% had ECG abnormalities, mainly related to age or in those with a history of cardiovascular disease.