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1.
Am J Cardiol ; 153: 125-128, 2021 08 15.
Article in English | MEDLINE | ID: covidwho-1293528

ABSTRACT

Mobile electrocardiogram (mECG) devices are being used increasingly, supplying recordings to providers and providing automatic rhythm interpretation. Given the intermittent nature of certain cardiac arrhythmias, mECGs allow instant access to a recording device. In the current COVID-19 pandemic, efforts to limit in-person patient interactions and avoid overwhelming emergency and inpatient services would add value. Our goal was to evaluate whether a mECG device would reduce healthcare utilization overall, particularly those of urgent nature. We identified a cohort of KardiaMobile (AliveCor, USA) mECG users and compared their healthcare utilization 1 year prior to obtaining the device and 1 year after. One hundred and twenty-eight patients were studied (mean age 64, 47% female). Mean duration of follow-up pre-intervention was 9.8 months. One hundred and twenty-three of 128 individuals completed post-intervention follow-up. Patients were less likely to have cardiac monitors ordered (30 vs 6; p <0.01), outpatient office visits (525 vs 382; p <0.01), cardiac-specific ED visits (51 vs 30; p <0.01), arrhythmia related ED visits (45 vs 20; p <0.01), and unplanned arrhythmia admissions (34 vs 11; p <0.01) in the year after obtaining a KardiaMobile device compared to the year prior to obtaining the device. Mobile technology is available for heart rhythm monitoring and can give feedback to the user. This study showed a reduction of in-person, healthcare utilization with mECG device use. In conclusion, this strategy would be expected to decrease the risk of exposure to patients and providers and would avoid overwhelming emergency and inpatient services.


Subject(s)
Arrhythmias, Cardiac/diagnosis , COVID-19/epidemiology , Computers, Handheld/statistics & numerical data , Electrocardiography/instrumentation , Monitoring, Physiologic/methods , Outpatients/statistics & numerical data , Pandemics , Patient Acceptance of Health Care/statistics & numerical data , Arrhythmias, Cardiac/epidemiology , Arrhythmias, Cardiac/physiopathology , Comorbidity , Female , Humans , Male , Middle Aged , Retrospective Studies , United States/epidemiology
2.
Glob Heart ; 16(1): 42, 2021 06 08.
Article in English | MEDLINE | ID: covidwho-1285504

ABSTRACT

Background: QTc prolongation is an adverse effect of COVID-19 therapies. The use of a handheld device in this scenario has not been addressed. Objectives: To evaluate the feasibility of QTc monitoring with a smart device in COVID-19 patients receiving QTc-interfering therapies. Methods: Prospective study of consecutive COVID-19 patients treated with hydroxychloroquine ± azithromycin ± lopinavir-ritonavir. ECG monitoring was performed with 12-lead ECG or with KardiaMobile-6L. Both registries were also sequentially obtained in a cohort of healthy patients. We evaluated differences in QTc in COVID-19 patients between three different monitoring strategies: 12-lead ECG at baseline and follow-up (A), 12-lead ECG at baseline and follow-up with the smart device (B), and fully monitored with handheld 6-lead ECG (group C). Time needed to obtain an ECG registry was also documented. Results: One hundred and eighty-two COVID-19 patients were included (A: 119(65.4%); B: 50(27.5%); C: 13(7.1%). QTc peak during hospitalization did significantly increase in all groups. No differences were observed between the three monitoring strategies in QTc prolongation (p = 0.864). In the control group, all but one ECG registry with the smart device allowed QTc measurement and mean QTc did not differ between both techniques (p = 0.612), displaying a moderate reliability (ICC 0.56 [0.19-0.76]). Time of ECG registry was significantly longer for the 12-lead ECG than for handheld device in both cohorts (p < 0.001). Conclusion: QTc monitoring with KardiaMobile-6L in COVID-19 patients was feasible. Time of ECG registration was significantly lower with the smart device, which may offer an important advantage for prevention of virus dissemination among healthcare providers.


Subject(s)
COVID-19/drug therapy , Electrocardiography/methods , Long QT Syndrome/diagnosis , Aged , Aged, 80 and over , Anti-Bacterial Agents/adverse effects , Antiviral Agents/adverse effects , Azithromycin/adverse effects , Drug Combinations , Electrocardiography/instrumentation , Enzyme Inhibitors/adverse effects , Feasibility Studies , Female , Humans , Hydroxychloroquine/adverse effects , Long QT Syndrome/chemically induced , Lopinavir/adverse effects , Male , Middle Aged , Point-of-Care Systems , Prospective Studies , Reproducibility of Results , Ritonavir/adverse effects , SARS-CoV-2
4.
Circulation ; 143(13): 1274-1286, 2021 03 30.
Article in English | MEDLINE | ID: covidwho-1180993

ABSTRACT

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.


Subject(s)
Artificial Intelligence , Electrocardiography/methods , Heart Diseases/diagnosis , Heart Rate/physiology , Adult , Aged , Area Under Curve , COVID-19/physiopathology , COVID-19/virology , Electrocardiography/instrumentation , Female , Heart Diseases/physiopathology , Humans , Long QT Syndrome/diagnosis , Long QT Syndrome/physiopathology , Male , Middle Aged , Prospective Studies , ROC Curve , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Smartphone
6.
Circulation ; 143(13): 1274-1286, 2021 03 30.
Article in English | MEDLINE | ID: covidwho-1058120

ABSTRACT

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.


Subject(s)
Artificial Intelligence , Electrocardiography/methods , Heart Diseases/diagnosis , Heart Rate/physiology , Adult , Aged , Area Under Curve , COVID-19/physiopathology , COVID-19/virology , Electrocardiography/instrumentation , Female , Heart Diseases/physiopathology , Humans , Long QT Syndrome/diagnosis , Long QT Syndrome/physiopathology , Male , Middle Aged , Prospective Studies , ROC Curve , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Smartphone
7.
Sensors (Basel) ; 20(7)2020 Apr 04.
Article in English | MEDLINE | ID: covidwho-827180

ABSTRACT

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.


Subject(s)
Electrocardiography/instrumentation , Phonocardiography/instrumentation , Ventricular Function , Wearable Electronic Devices , Heart Ventricles , Humans , Respiratory Rate
8.
J Cardiovasc Electrophysiol ; 31(11): 2803-2811, 2020 11.
Article in English | MEDLINE | ID: covidwho-732126

ABSTRACT

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is a worldwide pandemic, and cardiovascular complications and arrhythmias in these patients are common. Cardiac monitoring is recommended for at risk patients; however, the availability of telemetry capable hospital beds is limited. We sought to evaluate a patch-based mobile telemetry system for inpatient cardiac monitoring during the pandemic. METHODS: A prospective cohort study was performed of inpatients hospitalized during the pandemic who had mobile telemetry devices placed; patients were studied up until the time of discharge or death. The primary outcome was a composite of management changes based on data obtained from the system and detection of new arrhythmias. Other clinical outcomes and performance characteristics of the mobile telemetry system were studied. RESULTS: Eighty-two patients underwent mobile telemetry device placement, of which 31 (37.8%) met the primary outcome, which consisted of 24 (29.3%) with new arrhythmias detected and 18 (22.2%) with management changes. Twenty-one patients (25.6%) died during the study, but none from primary arrhythmias. In analyses, age and heart failure were associated with the primary outcome. Monitoring occurred for an average of 5.3 ± 3.4 days, with 432 total patient-days of monitoring performed; of these, QT-interval measurements were feasible in 400 (92.6%). CONCLUSION: A mobile telemetry system was successfully implemented for inpatient use during the COVID-19 pandemic and was shown to be useful to inform patient management, detect occult arrhythmias, and monitor the QT-interval. Patients with advanced age and structural heart disease may be more likely to benefit from this system.


Subject(s)
Arrhythmias, Cardiac/diagnosis , COVID-19/complications , Electrocardiography/instrumentation , Heart Rate , Inpatients , Telemetry/instrumentation , Action Potentials , Aged , Aged, 80 and over , Arrhythmias, Cardiac/etiology , Arrhythmias, Cardiac/mortality , Arrhythmias, Cardiac/therapy , COVID-19/diagnosis , COVID-19/mortality , COVID-19/therapy , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Prospective Studies , Time Factors , Treatment Outcome
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