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Denysyuk HV, Pires IM, Garcia NM. A roadmap for empowering cardiovascular disease patients: a 5P-Medicine approach and technological integration. PeerJ 2024; 12:e17895. [PMID: 39224824 PMCID: PMC11368085 DOI: 10.7717/peerj.17895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/19/2024] [Indexed: 09/04/2024] Open
Abstract
This article explores the multifaceted concept of cardiovascular disease (CVD) patients' empowerment, emphasizing a shift from compliance-oriented models to active patient participation. In recognizing that cardiovascular disease is a paramount global health challenge, this study illuminates the pressing need for empowering patients, underscoring their role as active participants in their healthcare journey. Grounded in 5P-Medicine principles-Predictive, Preventive, Participatory, Personalized, and Precision Medicine-the importance of empowering CVD patients through analytics, prevention, participatory decision making, and personalized treatments is highlighted. Incorporating a comprehensive overview of patient empowerment strategies, including self-management, health literacy, patient involvement, and shared decision making, the article advocates for tailored approaches aligned with individual needs, cultural contexts, and healthcare systems. Technological integration is examined to enhance patient engagement and personalized healthcare experiences. The critical role of patient-centered design in integrating digital tools for CVD management is emphasized, ensuring successful adoption and meaningful impact on healthcare outcomes. The conclusion proposes vital research questions addressing challenges and opportunities in CVD patient empowerment. These questions stress the importance of medical community research, understanding user expectations, evaluating existing technologies, defining ideal empowerment scenarios, and conducting a literature review for informed advancements. This article lays the foundation for future research, contributing to ongoing patient-centered healthcare evolution, especially in empowering individuals with a 5P-Medicine approach to cardiovascular diseases.
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Affiliation(s)
- Hanna V. Denysyuk
- Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal
| | - Ivan Miguel Pires
- Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal
| | - Nuno M. Garcia
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
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Kumar S, Weinstein J, Melchinger HC, Smith A, Capodilupo E, Akar JG, Garg K, O'Connor KD, Staunton MK, Martin M, Akhlaghi N, Edeh O, Perez S, Lee V, Lee KAV, Wilson FP. Observational study protocol for an arrhythmia notification feature. BMJ Open 2024; 14:e075110. [PMID: 38830741 PMCID: PMC11149124 DOI: 10.1136/bmjopen-2023-075110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 01/16/2024] [Indexed: 06/05/2024] Open
Abstract
INTRODUCTION Screening for atrial fibrillation (AF) in the general population may help identify individuals at risk, enabling further assessment of risk factors and institution of appropriate treatment. Algorithms deployed on wearable technologies such as smartwatches and fitness bands may be trained to screen for such arrhythmias. However, their performance needs to be assessed for safety and accuracy prior to wide-scale implementation. METHODS AND ANALYSIS This study will assess the ability of the WHOOP strap to detect AF using its WHOOP Arrhythmia Notification Feature (WARN) algorithm in an enriched cohort with a 2:1 distribution of previously diagnosed AF (persistent and paroxysmal) and healthy controls. Recruited participants will collect data for 7 days with the WHOOP wrist-strap and BioTel ePatch (electrocardiography gold-standard). Primary outcome will be participant level sensitivity and specificity of the WARN algorithm in detecting AF in analysable windows compared with the ECG gold-standard. Similar analyses will be performed on an available epoch-level basis as well as comparison of these findings in important subgroups. ETHICS AND DISSEMINATION The study was approved by the ethics board at the study site. Participants will be enrolled after signing an online informed consent document. Updates will be shared via clinicaltrials.gov. The data obtained from the conclusion of this study will be presented in national and international conferences with publication in clinical research journals. TRIAL REGISTRATION NUMBER NCT05809362.
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Affiliation(s)
- Sanchit Kumar
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jason Weinstein
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Hannah Camille Melchinger
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Abigail Smith
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Joseph G Akar
- Division of Cardiology, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kanika Garg
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kyle D O'Connor
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mary Kate Staunton
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Melissa Martin
- Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Narjes Akhlaghi
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Oluoma Edeh
- University of New Mexico, Albuquerque, New Mexico, USA
| | - Stephanie Perez
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Kyoung A V Lee
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Francis P Wilson
- Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
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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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Syversen A, Dosis A, Jayne D, Zhang Z. Wearable Sensors as a Preoperative Assessment Tool: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:482. [PMID: 38257579 PMCID: PMC10820534 DOI: 10.3390/s24020482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.
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Affiliation(s)
- Aron Syversen
- School of Computing, University of Leeds, Leeds LS2 9JT, UK
| | - Alexios Dosis
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - David Jayne
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - Zhiqiang Zhang
- School of Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK;
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020828. [PMID: 36679626 PMCID: PMC9865666 DOI: 10.3390/s23020828] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/27/2022] [Accepted: 01/09/2023] [Indexed: 06/02/2023]
Abstract
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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