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      Smart wearable devices in cardiovascular care: where we are and how to move forward

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          Abstract

          Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical ‘ABCD’ guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.

          Abstract

          In this Review, Elshazly and colleagues summarize the basic engineering principles of common wearable sensors and discuss their broad applications in cardiovascular disease prevention, diagnosis and management.

          Key points

          • Smart wearables generate a plethora of data through various sensors and software algorithms and understanding their basic engineering principles and limitations can be helpful for clinicians and scientists.

          • Evidence supports the use of wearable devices in cardiovascular risk assessment and cardiovascular disease prevention, diagnosis and management, but large, well-designed trials are needed to establish their advantages.

          • Several challenges still hinder the widespread adoption of wearables in clinical practice, including a concern for device accuracy, patient privacy and cost, and how to separate actionable data from noise.

          • Overcoming these challenges requires that various stakeholders come together to develop comprehensive evaluation frameworks, pragmatic regulatory policies, clinical trials and medical education curricula.

          • A practical ‘ABCD’ guide for clinicians can facilitate the integration of these devices in routine clinical practice.

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          Most cited references100

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            Wearable biosensors for healthcare monitoring

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              Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

              Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.

                Author and article information

                Contributors
                mes2015@qatar-med.cornell.edu
                Journal
                Nat Rev Cardiol
                Nat Rev Cardiol
                Nature Reviews. Cardiology
                Nature Publishing Group UK (London )
                1759-5002
                1759-5010
                4 March 2021
                : 1-19
                Affiliations
                [1 ]GRID grid.415436.1, ISNI 0000 0004 0443 7314, Department of Medicine, , NewYork-Presbyterian Brooklyn Methodist Hospital, ; Brooklyn, NY USA
                [2 ]GRID grid.413548.f, ISNI 0000 0004 0571 546X, Department of Oncology, National Center for Cancer Care and Research, , Hamad Medical Corporation, ; Doha, Qatar
                [3 ]Department of Medical Education, Weill Cornell Medicine, Doha, Qatar
                [4 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Cardiovascular Medicine, , University of California Irvine, ; Irvine, CA USA
                [5 ]Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Baltimore, MD USA
                [6 ]GRID grid.419794.6, ISNI 0000 0001 2111 8997, Scripps Research Translational Institute and Division of Cardiovascular Diseases, , Scripps Clinic, ; La Jolla, CA USA
                [7 ]GRID grid.168010.e, ISNI 0000000419368956, Center for Digital Health, , Stanford University, ; Stanford, CA USA
                [8 ]GRID grid.280747.e, ISNI 0000 0004 0419 2556, VA Palo Alto Health Care System, ; Palo Alto, CA USA
                [9 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Department of Cardiovascular Medicine, , Heart and Vascular Institute, Cleveland Clinic, ; Cleveland, OH USA
                [10 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Medicine, , Weill Cornell Medicine, ; New York, NY USA
                Author information
                http://orcid.org/0000-0001-7128-2701
                http://orcid.org/0000-0002-4704-4495
                http://orcid.org/0000-0002-5802-692X
                Article
                522
                10.1038/s41569-021-00522-7
                7931503
                33664502
                114d2bdf-451b-498d-9183-938c3888ed7c
                © Springer Nature Limited 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 1 February 2021
                Categories
                Review Article

                cardiac device therapy,machine learning
                cardiac device therapy, machine learning

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