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1.
BMJ Open ; 11(12), 2021.
Article in English | ProQuest Central | ID: covidwho-1592842

ABSTRACT

IntroductionPersonal digital devices that provide health information, such as the Apple Watch, have developed an increasing array of cardiopulmonary tracking features which have received regulatory clearance and are directly marketed to consumers. Despite their widespread and increasing use, data about the impact of personal digital device use on patient-reported outcomes and healthcare utilisation are sparse. Among a population of patients with atrial fibrillation and/or atrial flutter undergoing cardioversion, our primary aim is to determine the impact of the heart rate measurement, irregular rhythm notification, and ECG features of the Apple Watch on quality of life and healthcare utilisation.Methods and analysisWe are conducting a prospective, open-label multicentre pragmatic randomised clinical trial, leveraging a unique patient-centred health data sharing platform for enrolment and follow-up. A total of 150 patients undergoing cardioversion for atrial fibrillation or atrial flutter will be randomised 1:1 to receive the Apple Watch Series 6 or Withings Move at the time of cardioversion. The primary outcome is the difference in the Atrial Fibrillation Effect on QualiTy-of-life global score at 6 months postcardioversion. Secondary outcomes include inpatient and outpatient healthcare utilisation. Additional secondary outcomes include a comparison of the Apple Watch ECG and pulse oximeter features with gold-standard data obtained in routine clinical care settings.Ethics and disseminationThe Institutional Review Boards at Yale University, Mayo Clinic, and Duke University Health System have approved the trial protocol. This trial will provide important data to policymakers, clinicians and patients about the impact of the heart rate, irregular rhythm notification, and ECG features of widely used personal digital devices on patient quality of life and healthcare utilisation. Findings will be disseminated to study participants, at professional society meetings and in peer-reviewed journals.Trial registration numberNCT04468321

2.
Mayo Clin Proc ; 95(7): 1354-1368, 2020 07.
Article in English | MEDLINE | ID: covidwho-1500136

ABSTRACT

OBJECTIVE: To explore the transcriptomic differences between patients with hypertrophic cardiomyopathy (HCM) and controls. PATIENTS AND METHODS: RNA was extracted from cardiac tissue flash frozen at therapeutic surgical septal myectomy for 106 patients with HCM and 39 healthy donor hearts. Expression profiling of 37,846 genes was performed using the Illumina Human HT-12v3 Expression BeadChip. All patients with HCM were genotyped for pathogenic variants causing HCM. Technical validation was performed using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot. This study was started on January 1, 1999, and final analysis was completed on April 20, 2020. RESULTS: Overall, 22% of the transcriptome (8443 of 37,846 genes) was expressed differentially between HCM and control tissues. Analysis by genotype revealed that gene expression changes were similar among genotypic subgroups of HCM, with only 4% (1502 of 37,846) to 6% (2336 of 37,846) of the transcriptome exhibiting differential expression between genotypic subgroups. The qRT-PCR confirmed differential expression in 92% (11 of 12 genes) of tested transcripts. Notably, in the context of coronavirus disease 2019 (COVID-19), the transcript for angiotensin I converting enzyme 2 (ACE2), a negative regulator of the angiotensin system, was the single most up-regulated gene in HCM (fold-change, 3.53; q-value =1.30×10-23), which was confirmed by qRT-PCR in triplicate (fold change, 3.78; P=5.22×10-4), and Western blot confirmed greater than 5-fold overexpression of ACE2 protein (fold change, 5.34; P=1.66×10-6). CONCLUSION: More than 20% of the transcriptome is expressed differentially between HCM and control tissues. Importantly, ACE2 was the most up-regulated gene in HCM, indicating perhaps the heart's compensatory effort to mount an antihypertrophic, antifibrotic response. However, given that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses ACE2 for viral entry, this 5-fold increase in ACE2 protein may confer increased risk for COVID-19 manifestations and outcomes in patients with increased ACE2 transcript expression and protein levels in the heart.


Subject(s)
Betacoronavirus , Cardiomyopathy, Hypertrophic/genetics , Cardiomyopathy, Hypertrophic/virology , Coronavirus Infections/complications , Peptidyl-Dipeptidase A/genetics , Peptidyl-Dipeptidase A/metabolism , Pneumonia, Viral/complications , Adolescent , Adult , Aged , Angiotensin-Converting Enzyme 2 , COVID-19 , Cardiomyopathy, Hypertrophic/metabolism , Case-Control Studies , Child , Genotype , Humans , Middle Aged , Myocardium/metabolism , Pandemics , RNA, Messenger/metabolism , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Young Adult
3.
Mayo Clin Proc ; 95(6): 1213-1221, 2020 06.
Article in English | MEDLINE | ID: covidwho-1450185

ABSTRACT

As the coronavirus disease 19 (COVID-19) global pandemic rages across the globe, the race to prevent and treat this deadly disease has led to the "off-label" repurposing of drugs such as hydroxychloroquine and lopinavir/ritonavir, which have the potential for unwanted QT-interval prolongation and a risk of drug-induced sudden cardiac death. With the possibility that a considerable proportion of the world's population soon could receive COVID-19 pharmacotherapies with torsadogenic potential for therapy or postexposure prophylaxis, this document serves to help health care professionals mitigate the risk of drug-induced ventricular arrhythmias while minimizing risk of COVID-19 exposure to personnel and conserving the limited supply of personal protective equipment.


Subject(s)
Death, Sudden, Cardiac , Hydroxychloroquine , Long QT Syndrome , Lopinavir , Risk Adjustment/methods , Ritonavir , Torsades de Pointes , Anti-Infective Agents/administration & dosage , Anti-Infective Agents/adverse effects , Betacoronavirus/drug effects , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/drug therapy , Coronavirus Infections/epidemiology , Death, Sudden, Cardiac/etiology , Death, Sudden, Cardiac/prevention & control , Drug Combinations , Drug Monitoring/methods , Drug Repositioning/ethics , Drug Repositioning/methods , Electrocardiography/methods , Humans , Hydroxychloroquine/administration & dosage , Hydroxychloroquine/adverse effects , Long QT Syndrome/chemically induced , Long QT Syndrome/mortality , Long QT Syndrome/therapy , Lopinavir/administration & dosage , Lopinavir/adverse effects , Pandemics , Pneumonia, Viral/drug therapy , Pneumonia, Viral/epidemiology , Ritonavir/administration & dosage , Ritonavir/adverse effects , SARS-CoV-2 , Torsades de Pointes/chemically induced , Torsades de Pointes/mortality , Torsades de Pointes/therapy
4.
Mayo Clin Proc ; 96(8): 2081-2094, 2021 08.
Article in English | MEDLINE | ID: covidwho-1336718

ABSTRACT

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.


Subject(s)
Artificial Intelligence , COVID-19/diagnosis , Electrocardiography , Case-Control Studies , Humans , Predictive Value of Tests , Sensitivity and Specificity
5.
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.
EClinicalMedicine ; 33: 100773, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1103840

ABSTRACT

Background: Treatment options for outpatients with COVID-19 could reduce morbidity and prevent SARS-CoV-2 transmission. Methods: In this randomized, double-blind, three-arm (1:1:1) placebo-equivalent controlled trial conducted remotely throughout the United States, adult outpatients with laboratory-confirmed SARS-CoV-2 infection were recruited. Participants were randomly assigned to receive hydroxychloroquine (HCQ) (400 mg BID x1day, followed by 200 mg BID x9days) with or without azithromycin (AZ) (500 mg, then 250 mg daily x4days) or placebo-equivalent (ascorbic acid (HCQ) and folic acid (AZ)), stratified by risk for progression to severe COVID-19 (high-risk vs. low-risk). Self-collected nasal swabs for SARS-CoV-2 PCR, FLUPro symptom surveys, EKGs and vital signs were collected daily. Primary endpoints were: (a) 14-day progression to lower respiratory tract infection (LRTI), 28-day COVID-19 related hospitalization, or death; (b) 14-day time to viral clearance; secondary endpoints included time to symptom resolution (ClinicalTrials.gov: NCT04354428). Due to the low rate of clinical outcomes, the study was terminated for operational futility. Findings: Between 15th April and 27th July 2020, 231 participants were enrolled and 219 initiated medication a median of 5.9 days after symptom onset. Among 129 high-risk participants, incident LRTI occurred in six (4.7%) participants (two control, four HCQ/AZ) and COVID-19 related hospitalization in seven (5.4%) (four control, one HCQ, two HCQ/AZ); no LRTI and two (2%) hospitalizations occurred in the 102 low-risk participants (one HCQ, one HCQ/AZ). There were no deaths. Among 152 participants with viral shedding at enrollment, median time to clearance was 5 days (95% CI=4-6) in HCQ, 6 days (95% CI=4-8) in HCQ/AZ, and 8 days (95% CI=6-10) in control. Viral clearance was faster in HCQ (HR=1.62, 95% CI=1.01-2.60, p = 0.047) but not HCQ/AZ (HR=1.25, p = 0.39) compared to control. Among 197 participants who met the COVID-19 definition at enrollment, time to symptom resolution did not differ by group (HCQ: HR=1.02, 95% CI-0.63-1.64, p = 0.95, HCQ/AZ: HR=0.91, 95% CI=0.57-1.45, p = 0.70). Interpretation: Neither HCQ nor HCQ/AZ shortened the clinical course of outpatients with COVID-19, and HCQ, but not HCQ/AZ, had only a modest effect on SARS-CoV-2 viral shedding. HCQ and HCQ/AZ are not effective therapies for outpatient treatment of SARV-CoV-2 infection. Funding: The COVID-19 Early Treatment Study was funded by the Bill & Melinda Gates Foundation (INV-017062) through the COVID-19 Therapeutics Accelerator. University of Washington Institute of Translational Health Science (ITHS) grant support (UL1 TR002319), KL2 TR002317, and TL1 TR002318 from NCATS/NIH funded REDCap. The content is solely the responsibility of the authors and does not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated. PAN and MJA were supported by the Mayo Clinic Windland Smith Rice Comprehensive Sudden Cardiac Death Program.Trial registration ClinicalTrials.gov number NCT04354428.

7.
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
9.
Int J Cardiol ; 326: 114-123, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-898899

ABSTRACT

BACKGROUND: An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. METHOD: We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. RESULTS: We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82-0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged <70 years (0.85 versus 0.80), males (0.84 versus 0.79), patients without ACS (0.86 versus 0.80), and patients who did not undergo revascularization (0.84 versus 0.80). CONCLUSIONS: The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.


Subject(s)
Artificial Intelligence , Ventricular Dysfunction, Left , Aged , Aged, 80 and over , Echocardiography , Electrocardiography , Female , Humans , Intensive Care Units , Male , Middle Aged , Ventricular Dysfunction, Left/diagnostic imaging , Ventricular Dysfunction, Left/epidemiology
10.
Circulation ; 141(21): e823-e831, 2020 05 26.
Article in English | MEDLINE | ID: covidwho-827449

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a global pandemic that is wreaking havoc on the health and economy of much of human civilization. Electrophysiologists have been impacted personally and professionally by this global catastrophe. In this joint article from representatives of the Heart Rhythm Society, the American College of Cardiology, and the American Heart Association, we identify the potential risks of exposure to patients, allied healthcare staff, industry representatives, and hospital administrators. We also describe the impact of COVID-19 on cardiac arrhythmias and methods of triage based on acuity and patient comorbidities. We provide guidance for managing invasive and noninvasive electrophysiology procedures, clinic visits, and cardiac device interrogations. In addition, we discuss resource conservation and the role of telemedicine in remote patient care along with management strategies for affected patients.


Subject(s)
Arrhythmias, Cardiac/etiology , Betacoronavirus , Coronavirus Infections/epidemiology , Electrocardiography , Pandemics , Pneumonia, Viral/epidemiology , Practice Guidelines as Topic , American Heart Association , Arrhythmias, Cardiac/therapy , COVID-19 , Cardiology , Cardiopulmonary Resuscitation , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Coronavirus Infections/physiopathology , Humans , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Pneumonia, Viral/physiopathology , SARS-CoV-2 , Societies, Medical , Telemedicine , Triage , United States
11.
Mayo Clin Proc ; 95(11): 2464-2466, 2020 11.
Article in English | MEDLINE | ID: covidwho-779413

ABSTRACT

Coronavirus disease 2019 (COVID-19) can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence electrocardiogram (AI ECG) to screen for ventricular dysfunction in patients with documented COVID-19. We examined all patients in the Mayo Clinic system who underwent clinically indicated electrocardiography and echocardiography within 2 weeks following a positive COVID-19 test and had permitted use of their data for research were included. Of the 27 patients who met the inclusion criteria, one had a history of normal ventricular function who developed COVID-19 myocarditis with rapid clinical decline. The initial AI ECG in this patient indicated normal ventricular function. Repeat AI ECG showed a probability of ejection fraction (EF) less than or equal to 40% of 90.2%, corroborated with an echocardiographic EF of 35%. One other patient had a pre-existing EF less than or equal to 40%, accurately detected by the algorithm before and after COVID-19 diagnosis, and another was found to have a low EF by AI ECG and echocardiography with the COVID-19 diagnosis. The area under the curve for detection of EF less than or equal to 40% was 0.95. This case series suggests that the AI ECG, previously shown to detect ventricular dysfunction in a large general population, may be useful as a screening tool for the detection of cardiac dysfunction in patients with COVID-19.


Subject(s)
Artificial Intelligence , Coronavirus Infections/complications , Electrocardiography/methods , Pneumonia, Viral/complications , Ventricular Dysfunction, Left/diagnosis , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Echocardiography , Feasibility Studies , Female , Humans , Male , Middle Aged , Pandemics , Retrospective Studies , SARS-CoV-2 , Ventricular Dysfunction, Left/virology
12.
Heart Rhythm ; 17(9): e233-e241, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-656387

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a global pandemic that is wreaking havoc on the health and economy of much of human civilization. Electrophysiologists have been impacted personally and professionally by this global catastrophe. In this joint article from representatives of the Heart Rhythm Society, the American College of Cardiology, and the American Heart Association, we identify the potential risks of exposure to patients, allied healthcare staff, industry representatives, and hospital administrators. We also describe the impact of COVID-19 on cardiac arrhythmias and methods of triage based on acuity and patient comorbidities. We provide guidance for managing invasive and noninvasive electrophysiology procedures, clinic visits, and cardiac device interrogations. In addition, we discuss resource conservation and the role of telemedicine in remote patient care along with management strategies for affected patients.


Subject(s)
Arrhythmias, Cardiac/diagnosis , Arrhythmias, Cardiac/therapy , Betacoronavirus , Coronavirus Infections/prevention & control , Electrocardiography , Electrophysiologic Techniques, Cardiac , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Arrhythmias, Cardiac/etiology , COVID-19 , Coronavirus Infections/complications , Coronavirus Infections/epidemiology , Humans , Infection Control/organization & administration , Pneumonia, Viral/complications , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Telemedicine/organization & administration , Triage/organization & administration
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