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Ramos SR, Reynolds H, Johnson C, Melkus G, Kershaw T, Thayer JF, Vorderstrasse A. Perceptions of HIV-Related Comorbidities and Usability of a Virtual Environment for Cardiovascular Disease Prevention Education in Sexual Minority Men With HIV: Formative Phases of a Pilot Randomized Controlled Trial. J Med Internet Res 2024; 26:e57351. [PMID: 38924481 DOI: 10.2196/57351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/24/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials. OBJECTIVE This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV. METHODS This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase 1, a total of 15 participants completed interviews exploring HIV-related illnesses of concern that would be used to tailor the virtual environment. In phase 2, usability testing and beta testing were conducted with 10 participants to assess interest, features, and content. RESULTS In phase 1, we found that CVD risk factors included high blood pressure, myocardial infarction, stroke, and diabetes. Cancer (prostate, colon, and others) was a common concern, as were mental health conditions. In phase 2, all participants completed the 12-item usability checklist with favorable feedback within 30 to 60 minutes. Beta-testing interviews suggested (1) mixed perceptions of health and HIV, (2) high risk for comorbid conditions, (3) virtual environment features were promising, and (4) the need for diverse avatar representations. CONCLUSIONS We identified several comorbid conditions of concern, and findings carry significant implications for mitigating barriers to preventive health screenings, given the shared risk factors between HIV and related comorbidities. Highly rated aspects of the virtual environment were anonymity; meeting others with HIV who identify as gay or bisexual; validating lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) images and content; and accessibility to CVD prevention education. Critical end-user feedback from beta testing suggested more options for avatar customization in skin, hair, and body representation. Our next phase will test the virtual environment as a new approach to advancing cardiovascular health equity in ethnic and racial sexual minority men with HIV. TRIAL REGISTRATION ClinicalTrials.gov NCT04061915; https://clinicaltrials.gov/study/NCT05242952. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/38348.
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Affiliation(s)
- S Raquel Ramos
- School of Nursing, Yale University, Orange, CT, United States
- School of Public Health, Social and Behavioral Sciences, Yale University, New Haven, CT, United States
- Center for Interdisciplinary Research on AIDS, Yale University, New Haven, NY, United States
| | - Harmony Reynolds
- Cardiovascular Clinical Research Center, Leon H. Charney Division of Cardiology, Department of Medicine, NYU Grossman School of Medicine, New York, NY, United States
| | - Constance Johnson
- Czik School of Nursing, The University of Texas Health Science Center at Houston, Houston, TX, United States
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Gail Melkus
- Rory Myers College of Nursing, New York University, New York, NY, United States
| | - Trace Kershaw
- School of Public Health, Social and Behavioral Sciences, Yale University, New Haven, CT, United States
- Center for Interdisciplinary Research on AIDS, Yale University, New Haven, NY, United States
| | - Julian F Thayer
- School of Social Ecology, Psychological Science, University of California, Irvine, CA, United States
| | - Allison Vorderstrasse
- Elaine Marieb College of Nursing, University of Massachusetts Amherst, Amherst, MA, United States
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Dhingra LS, Aminorroaya A, Camargos AP, Khunte A, Sangha V, McIntyre D, Chow CK, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.27.24307952. [PMID: 38854022 PMCID: PMC11160804 DOI: 10.1101/2024.05.27.24307952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Importance Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design Multicohort study. Setting Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants Individuals without HF at baseline. Exposures AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.
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Affiliation(s)
- Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Daniel McIntyre
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
| | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia
- Department of Cardiology, Westmead Hospital, Sydney, Australia
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Luisa CC Brant
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Khera R. Artificial intelligence-enhanced exposomics: novel insights into cardiovascular health. Eur Heart J 2024; 45:1550-1552. [PMID: 38544282 DOI: 10.1093/eurheartj/ehae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/09/2024] Open
Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 195 Church St, 6th Floor, New Haven, CT 06510, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
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Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.02.24305232. [PMID: 38633808 PMCID: PMC11023679 DOI: 10.1101/2024.04.02.24305232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.
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Affiliation(s)
- Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Luisa CC Brant
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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Thangaraj PM, Shankar SV, Oikonomou EK, Khera R. RCT-Twin-GAN Generates Digital Twins of Randomized Control Trials Adapted to Real-world Patients to Enhance their Inference and Application. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299464. [PMID: 38106089 PMCID: PMC10723568 DOI: 10.1101/2023.12.06.23299464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Randomized clinical trials (RCTs) are designed to produce evidence in selected populations. Assessing their effects in the real-world is essential to change medical practice, however, key populations are historically underrepresented in the RCTs. We define an approach to simulate RCT-based effects in real-world settings using RCT digital twins reflecting the covariate patterns in an electronic health record (EHR). Methods We developed a Generative Adversarial Network (GAN) model, RCT-Twin-GAN, which generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from an EHR cohort. We improved upon a traditional tabular conditional GAN, CTGAN, with a loss function adapted for data distributions and by conditioning on multiple discrete and continuous covariates simultaneously. We assessed the similarity between a Heart Failure with preserved Ejection Fraction (HFpEF) RCT (TOPCAT), a Yale HFpEF EHR cohort, and RCT-Twin. We also evaluated cardiovascular event-free survival stratified by Spironolactone (treatment) use. Results By applying RCT-Twin-GAN to 3445 TOPCAT participants and conditioning on 3445 Yale EHR HFpEF patients, we generated RCT-Twin datasets between 1141-3445 patients in size, depending on covariate conditioning and model parameters. RCT-Twin randomly allocated spironolactone (S)/ placebo (P) arms like an RCT, was similar to RCT by a multi-dimensional distance metric, and balanced covariates (median absolute standardized mean difference (MASMD) 0.017, IQR 0.0034-0.030). The 5 EHR-conditioned covariates in RCT-Twin were closer to the EHR compared with the RCT (MASMD 0.008 vs 0.63, IQR 0.005-0.018 vs 0.59-1.11). RCT-Twin reproduced the overall effect size seen in TOPCAT (5-year cardiovascular composite outcome odds ratio (95% confidence interval) of 0.89 (0.75-1.06) in RCT vs 0.85 (0.69-1.04) in RCT-Twin). Conclusions RCT-Twin-GAN simulates RCT-derived effects in real-world patients by translating these effects to the covariate distributions of EHR patients. This key methodological advance may enable the direct translation of RCT-derived effects into real-world patient populations and may enable causal inference in real-world settings.
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Affiliation(s)
- Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Sumukh Vasisht Shankar
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
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Thangaraj PM, Khera R. Accelerating chest pain evaluation with machine learning. EUROPEAN HEART JOURNAL. ACUTE CARDIOVASCULAR CARE 2023; 12:753-754. [PMID: 37793075 PMCID: PMC11004857 DOI: 10.1093/ehjacc/zuad117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/06/2023]
Affiliation(s)
- Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 330 Cedar Street, Boardman 110, New Haven, CT 06520, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, 330 Cedar Street, Boardman 110, New Haven, CT 06520, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, Floor 9, New Haven, CT 06510, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church Street, 6th Floor, New Haven, CT 06510, USA
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Shankar SV, Oikonomou EK, Khera R. CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.02.23296404. [PMID: 37873174 PMCID: PMC10593062 DOI: 10.1101/2023.10.02.23296404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
In the rapidly evolving landscape of modern healthcare, the integration of wearable and portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobile have revolutionized the acquisition and processing of intricate health data streams that were previously accessible only through devices only available to healthcare providers. Amidst the variety of data collected by these gadgets, single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. Notably, there has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs, facilitating clinical diagnosis as well as the detection of rare cardiac disorders. This design study describes the development of an innovative multi-platform system aimed at the rapid deployment of AI-based ECG solutions for clinical investigation and care delivery. The study examines various design considerations, aligning them with specific applications, and develops data flows to maximize efficiency for research and clinical use. This process encompasses the reception of single-lead ECGs from diverse wearable devices, channeling this data into a centralized data lake, and facilitating real-time inference through AI models for ECG interpretation. An evaluation of the platform demonstrates a mean duration from acquisition to reporting of results of 33.0 to 35.7 seconds, after a standard 30 second acquisition, allowing the complete process to be completed in 63.0 to 65.7 seconds. There were no substantial differences in acquisition to reporting across two commercially available devices (Apple Watch and KardiaMobile). These results demonstrate the succcessful translation of design principles into a fully integrated and efficient strategy for leveraging 1-lead ECGs across platforms and interpretation by AI-ECG algorithms. Such a platform is critical to translating AI discoveries for wearable and portable ECG devices to clinical impact through rapid deployment.
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Affiliation(s)
- Sumukh Vasisht Shankar
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Evangelos K Oikonomou
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Rohan Khera
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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