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Zhang L, Li B, Wu L. Heart rate variability in patients with atrial fibrillation of sinus rhythm or atrial fibrillation: chaos or merit? Ann Med 2025; 57:2478474. [PMID: 40079735 PMCID: PMC11912244 DOI: 10.1080/07853890.2025.2478474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Revised: 02/26/2025] [Accepted: 03/02/2025] [Indexed: 03/15/2025] Open
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia characterized by consistently irregular atrial and ventricular contractions. Heart rate variability (HRV) refers to the changes in the intervals between consecutive ventricular heartbeats. In sinus rhythm, HRV may be subtle and is quantitatively reflecting the dynamic interplay of the cardiac autonomic nervous system, which plays a crucial role in the onset, development, and maintenance of AF. HRV metrics, consisting of time-domain, frequency-domain, and nonlinear parameters, have been verified to vary significantly before and after AF episodes, and AF treatment-related procedures such as electrical cardioversion, ablation, and surgery of AF. Therefore, HRV may serve as a digital biomarker in predicting AF risk in long-term and acute risk period, identification of patients with AF risk in sinus rhythm and recurrence risk stratification after procedures. HRV in AF rhythm, predominantly influenced by dynamic atrioventricular node conduction under the onslaught of irregular atrial impulses, shows a huge disparity compared to that in sinus rhythm. Despite this, HRV in AF rhythm still provides valuable prognostic information, as reduced HRV may indicate a poor heart function and outcomes in patients with AF. Despite being influenced by lots of variables, HRV can still serve as an independent digital biomarker in the clinical management of AF throughout its entire lifecycle.
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Affiliation(s)
- Lifan Zhang
- Department of Cardiology, Peking University First Hospital, Beijing, China
| | - Bingxun Li
- Department of Cardiology, Peking University First Hospital, Beijing, China
| | - Lin Wu
- Department of Cardiology, Peking University First Hospital, Beijing, China
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Barbosa IOF, de Oliveira BC, Santos CKM, Miranda MCR, Barbosa GA, Júnior ADSM. Smartphone-Based Applications for Atrial Fibrillation Detection: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy. Telemed J E Health 2025; 31:687-700. [PMID: 39888635 DOI: 10.1089/tmj.2024.0579] [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] [Indexed: 02/01/2025] Open
Abstract
Background: Atrial fibrillation (AF) burden is strongly associated with an increased risk of stroke, which, in most cases, can be prevented through earlier detection of AF and the timely initiation of anticoagulation therapy. Smartphone devices can provide a simple, non-invasive, cost-effective early AF detection solution. Methods: PubMed, Embase, and Scopus databases were searched for studies comparing smartphone-based photoplethysmography (PPG) with standard electrocardiogram for AF detection. A bivariate random-effects model with a 95% confidence interval (CI) was applied to generate the summary receiver operating characteristic (SROC) curve. Results: Fourteen studies were included, comprising 5,090 patients with an AF prevalence of 31.6%. The pooled sensitivity and specificity were 0.96 (95% CI, 0.93-0.97) and 0.97 (95% CI, 0.95-0.98). The area under the SROC curve was 0.98 (95% CI, 0.94-0.99). The diagnostic odds ratio was 960 (95% CI, 439-2,104), with significant heterogeneity (I2 = 51%). The projected positive and negative predictive values were 66.5% and 99.7%, respectively, in the elderly population aged >65 years and 39.2% and 99.9% in the general population. Conclusion: Smartphone-based PPG demonstrated relatively high sensitivity and specificity and appears capable of ruling out AF. Patients aged >65 are more likely to benefit from AF screening.
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Affiliation(s)
| | - Beatriz Costa de Oliveira
- Medical Department, Medical Sciences and Life School, Pontifical Catholic University of Goiás, Goiânia, Brazil
| | | | - Maria Clara Ramos Miranda
- Medical Department, Medical Sciences and Life School, Pontifical Catholic University of Goiás, Goiânia, Brazil
| | - Gabriel Alves Barbosa
- Medical Department, Medical Sciences and Life School, Pontifical Catholic University of Goiás, Goiânia, Brazil
| | - Antônio da Silva Menezes Júnior
- Medical Department, Medical Sciences and Life School, Pontifical Catholic University of Goiás, Goiânia, Brazil
- Medical Department, Medical Faculty, Federal University of Goiás, Goiânia, Brazil
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Fernstad J, Svennberg E, Åberg P, Kemp Gudmundsdottir K, Jansson A, Engdahl J. External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study. Europace 2025; 27:euaf031. [PMID: 39960451 PMCID: PMC11965787 DOI: 10.1093/europace/euaf031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Accepted: 02/07/2025] [Indexed: 04/04/2025] Open
Abstract
AIMS The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial flutter (AFL) pericardioversion in an unsupervised ambulatory setting. METHODS AND RESULTS Patients undergoing cardioversion for AF or AFL performed 1-min heart rhythm recordings pericardioversion at least twice daily for 4-6 weeks, using an iPhone 7 smartphone running a PPG application (CORAI Heart Monitor) simultaneously with a single-lead electrocardiogram (ECG) recording (KardiaMobile). The algorithm uses support vector machines to classify heart rhythm from smartphone-PPG. The algorithm was trained on PPG recordings made by patients in a separate cardioversion cohort. Photoplethysmography recordings in the external validation cohort were analysed by the algorithm. Diagnostic performance was calculated by comparing the heart rhythm classification output to the diagnosis from the simultaneous ECG recordings (gold standard). In total, 460 patients performed 34 097 simultaneous PPG and ECG recordings, divided into 180 patients with 16 092 recordings in the training cohort and 280 patients with 18 005 recordings in the external validation cohort. Algorithmic classification of the PPG recordings in the external validation cohort diagnosed AF with sensitivity, specificity, and accuracy of 99.7%, 99.7% and 99.7%, respectively, and AF/AFL with sensitivity, specificity, and accuracy of 99.3%, 99.1% and 99.2%, respectively. CONCLUSION A machine learning-based algorithm demonstrated excellent performance in diagnosing atrial fibrillation and atrial flutter from smartphone-PPG recordings in an unsupervised ambulatory setting, minimizing the need for manual review and ECG verification, in elderly cardioversion populations. CLINICAL TRIAL REGISTRATION Clinicaltrials.gov, NCT04300270.
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Affiliation(s)
- Jonatan Fernstad
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden
- Department of Cardiology, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden
| | - Emma Svennberg
- Karolinska Institutet, Department of Medicine, Huddinge, Karolinska University Hospital, Stockholm, Sweden
| | - Peter Åberg
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden
| | - Katrin Kemp Gudmundsdottir
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden
| | - Anders Jansson
- Department of Clinical Physiology, Danderyd University Hospital, Stockholm, Sweden
| | - Johan Engdahl
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden
- Department of Cardiology, Danderyd University Hospital, Entrévägen 2, 182 88 Stockholm, Sweden
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Zwart LAR, Spruit JR, Jansen RWMM, Riezebos RK, Pisters R, Louter L, De Vries K, Taekema DG, Wold JFH, De Groot JR, Hemels MEW. Opportunistic screening for atrial fibrillation among frail older patients, little effort for a high diagnostic yield. Outcomes of the Dutch-GERAF study. Age Ageing 2025; 54:afaf105. [PMID: 40253687 PMCID: PMC12009541 DOI: 10.1093/ageing/afaf105] [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: 10/17/2024] [Indexed: 04/22/2025] Open
Abstract
BACKGROUND AND AIMS Frailty increases the risk of atrial fibrillation (AF) and its complications. This study investigated the feasibility and diagnostic yield of an eHealth screening for the detection of new AF, in frail older patients. METHODS Patients referred to the Geriatric Medicine outpatient clinics were eligible. A Frailty Index (FI) was calculated. Patients were screened for AF with electrocardiograms (ECGs) at baseline and a smartphone photoplethysmography (PPG) application, during 6 months. RESULTS Nine hundred fifty-two patients (median age 79 years) were included, mean FI of 0.16, 311 were frail (33%) and 751 had sinus rhythm (79%) at baseline. Six hundred forty-one patients (85%) performed PPG recordings (median 2), 295 (39%) at least 3 recordings. Twenty (2.7%) new cases of AF were found, 10 at baseline and 10 during follow-up. Among 16 (2%) patients, additional irregular PPG recordings were acquired, but no confirmatory ECG took place. CONCLUSION The screening strategy proved feasible in very old and frail patients. A diagnostic yield of 2.7% was found by ECG, and an additional 0.9% of new AF cases were suspected on PPG recordings. The non-binding approach of the strategy might be disadvantageous for the patient category. Future PPG AF screening programmes for very old and frail patients should strictly organise their means of AF confirmation.
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Affiliation(s)
- Lennaert A R Zwart
- Dijklander Hospital - Department of Geriatric Medicine, Hoorn, Noord-Holland, Netherlands
- Amsterdam UMC Locatie De Boelelaan - Amsterdam Public Health Department, Amsterdam, Noord-Holland, Netherlands
| | - Jocelyn R Spruit
- North West Hospital Group - Department of Geriatric Medicine, Alkmaar, Noord-Holland, Netherlands
| | - René W M M Jansen
- North West Hospital Group - Department of Geriatric Medicine, Alkmaar, Noord-Holland, Netherlands
| | - Robert K Riezebos
- Isala Hospital - Department of Cardiology, Zwolle, Overijssel, Netherlands
| | - Ron Pisters
- Rijnstate Hospital - Department of Cardiology, Arnhem, Gelderland, Netherlands
| | - Leonora Louter
- Albert Schweitzer Hospital - Department of Geriatric Medicine, Dordrecht, Zuid-Holland, Netherlands
| | - Kerst De Vries
- OLVG - Department of Geriatric Medicine, Amsterdam, Noord-Holland, Netherlands
| | - Diana G Taekema
- Rijnstate Hospital - Department of Geriatric Medicine, Arnhem, Gelderland, Netherlands
| | - Johan F H Wold
- Meander MC - Department of Geriatric Medicine, Amersfoort, Utrecht, Netherlands
| | - Joris R De Groot
- Amsterdam UMC Location AMC - Department of Cardiology, Amsterdam, Noord-Holland, Netherlands
| | - Martin E W Hemels
- Radboud University Nijmegen - Department of Cardiology, Nijmegen, Gelderland, Netherlands
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Banerjee A. Artificial intelligence enabled mobile health technologies in arrhythmias-an opinion article on recent findings. Front Cardiovasc Med 2025; 12:1548554. [PMID: 40027513 PMCID: PMC11868161 DOI: 10.3389/fcvm.2025.1548554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2024] [Accepted: 01/31/2025] [Indexed: 03/05/2025] Open
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Van Gelder IC, Rienstra M, Bunting KV, Casado-Arroyo R, Caso V, Crijns HJGM, De Potter TJR, Dwight J, Guasti L, Hanke T, Jaarsma T, Lettino M, Løchen ML, Lumbers RT, Maesen B, Mølgaard I, Rosano GMC, Sanders P, Schnabel RB, Suwalski P, Svennberg E, Tamargo J, Tica O, Traykov V, Tzeis S, Kotecha D. 2024 ESC Guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J 2024; 45:3314-3414. [PMID: 39210723 DOI: 10.1093/eurheartj/ehae176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
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Santala OE, Lipponen JA, Jäntti H, Rissanen TT, Tarvainen MP, Väliaho ES, Rantula OA, Naukkarinen NS, Hartikainen JEK, Martikainen TJ, Halonen J. Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation. Cardiol Rev 2024; 32:440-447. [PMID: 36946975 PMCID: PMC11296284 DOI: 10.1097/crd.0000000000000526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.
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Affiliation(s)
- Onni E. Santala
- From the School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jukka A. Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Helena Jäntti
- Centre for Prehospital Emergency Care, Kuopio University Hospital, Kuopio, Finland
| | | | - Mika P. Tarvainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Eemu-Samuli Väliaho
- From the School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Olli A. Rantula
- From the School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora S. Naukkarinen
- From the School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Juha E. K. Hartikainen
- From the School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Heart Center, Kuopio University Hospital, Kuopio, Finland
| | | | - Jari Halonen
- From the School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
- Heart Center, Kuopio University Hospital, Kuopio, Finland
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Ghazizadeh E, Naseri Z, Deigner HP, Rahimi H, Altintas Z. Approaches of wearable and implantable biosensor towards of developing in precision medicine. Front Med (Lausanne) 2024; 11:1390634. [PMID: 39091290 PMCID: PMC11293309 DOI: 10.3389/fmed.2024.1390634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/30/2024] [Indexed: 08/04/2024] Open
Abstract
In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.
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Affiliation(s)
- Elham Ghazizadeh
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Naseri
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hans-Peter Deigner
- Institute of Precision Medicine, Furtwangen University, Villingen-Schwenningen, Germany
- Fraunhofer Institute IZI (Leipzig), Rostock, Germany
- Faculty of Science, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Hossein Rahimi
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zeynep Altintas
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
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Slaats BM, Blok S, Somsen GA, Tulevski II, Knops RE, van den Born BJH, Winter MM. Can eHealth programs for cardiac arrhythmias be scaled-up by using the KardiaMobile algorithm? CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024; 5:78-84. [PMID: 38765619 PMCID: PMC11096654 DOI: 10.1016/j.cvdhj.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
Background Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary. Objective The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht). Methods This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined. Results A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected. Conclusion Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.
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Affiliation(s)
- Bridget M.I. Slaats
- Cardiology Centers of the Netherlands, Utrecht, The Netherlands
- Department of Internal Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sebastiaan Blok
- Cardiology Centers of the Netherlands, Utrecht, The Netherlands
- Department of Internal Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Reinoud E. Knops
- Department of Cardiology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bert-Jan H. van den Born
- Cardiology Centers of the Netherlands, Utrecht, The Netherlands
- Department of Internal Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Amsterdam, The Netherlands
| | - Michiel M. Winter
- Cardiology Centers of the Netherlands, Utrecht, The Netherlands
- Department of Cardiology, Amsterdam UMC, Amsterdam, The Netherlands
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Fernstad J, Svennberg E, Åberg P, Kemp Gudmundsdottir K, Jansson A, Engdahl J. Validation of a novel smartphone-based photoplethysmographic method for ambulatory heart rhythm diagnostics: the SMARTBEATS study. Europace 2024; 26:euae079. [PMID: 38533836 PMCID: PMC11023506 DOI: 10.1093/europace/euae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/24/2024] [Indexed: 03/28/2024] Open
Abstract
AIMS In the current guidelines, smartphone photoplethysmography (PPG) is not recommended for diagnosis of atrial fibrillation (AF), without a confirmatory electrocardiogram (ECG) recording. Previous validation studies have been performed under supervision in healthcare settings, with limited generalizability of the results. We aim to investigate the diagnostic performance of a smartphone-PPG method in a real-world setting, with ambulatory unsupervised smartphone-PPG recordings, compared with simultaneous ECG recordings and including patients with atrial flutter (AFL). METHODS AND RESULTS Unselected patients undergoing direct current cardioversion for treatment of AF or AFL were asked to perform 1-min heart rhythm recordings post-treatment, at least twice daily for 30 days at home, using an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile). Photoplethysmography and ECG recordings were read independently by two experienced readers. In total, 280 patients recorded 18 005 simultaneous PPG and ECG recordings. Sufficient quality for diagnosis was seen in 96.9% (PPG) vs. 95.1% (ECG) of the recordings (P < 0.001). Manual reading of the PPG recordings, compared with manually interpreted ECG recordings, had a sensitivity, specificity, and overall accuracy of 97.7%, 99.4%, and 98.9% with AFL recordings included and 99.0%, 99.7%, and 99.5%, respectively, with AFL recordings excluded. CONCLUSION A novel smartphone-PPG method can be used by patients unsupervised at home to achieve accurate heart rhythm diagnostics of AF and AFL with very high sensitivity and specificity. This smartphone-PPG device can be used as an independent heart rhythm diagnostic device following cardioversion, without the requirement of confirmation with ECG.
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Affiliation(s)
- Jonatan Fernstad
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88, Stockholm, Sweden
- Department of Cardiology, Danderyd University Hospital, Entrévägen 2, 182 88, Stockholm, Sweden
| | - Emma Svennberg
- Karolinska Institutet, Department of Medicine, Huddinge, Karolinska University Hospital, Stockholm, Sweden
| | - Peter Åberg
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88, Stockholm, Sweden
| | - Katrin Kemp Gudmundsdottir
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88, Stockholm, Sweden
| | - Anders Jansson
- Department of Clinical Physiology, Danderyd University Hospital, Stockholm, Sweden
| | - Johan Engdahl
- Karolinska Institutet, Department of Clinical Sciences, Danderyd University Hospital, Entrévägen 2, 182 88, Stockholm, Sweden
- Department of Cardiology, Danderyd University Hospital, Entrévägen 2, 182 88, Stockholm, Sweden
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Pucci G, Grillo A, Dalakleidi KV, Fraenkel E, Gkaliagkousi E, Golemati S, Guala A, Hametner B, Lazaridis A, Mayer CC, Mozos I, Pereira T, Veerasingam D, Terentes-Printzios D, Agnoletti D. Atrial Fibrillation and Early Vascular Aging: Clinical Implications, Methodology Issues and Open Questions-A Review from the VascAgeNet COST Action. J Clin Med 2024; 13:1207. [PMID: 38592046 PMCID: PMC10931681 DOI: 10.3390/jcm13051207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 04/10/2024] Open
Abstract
Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with adverse CV outcomes. Vascular aging (VA), which is defined as the progressive deterioration of arterial function and structure over a lifetime, is an independent predictor of both AF development and CV events. A timing identification and treatment of early VA has therefore the potential to reduce the risk of AF incidence and related CV events. A network of scientists and clinicians from the COST Action VascAgeNet identified five clinically and methodologically relevant questions regarding the relationship between AF and VA and conducted a narrative review of the literature to find potential answers. These are: (1) Are VA biomarkers associated with AF? (2) Does early VA predict AF occurrence better than chronological aging? (3) Is early VA a risk enhancer for the occurrence of CV events in AF patients? (4) Are devices measuring VA suitable to perform subclinical AF detection? (5) Does atrial-fibrillation-related rhythm irregularity have a negative impact on the measurement of vascular age? Results showed that VA is a powerful and independent predictor of AF incidence, however, its role as risk modifier for the occurrence of CV events in patients with AF is debatable. Limited and inconclusive data exist regarding the reliability of VA measurement in the presence of rhythm irregularities associated with AF. To date, no device is equipped with tools capable of detecting AF during VA measurements. This represents a missed opportunity to effectively perform CV prevention in people at high risk. Further advances are needed to fill knowledge gaps in this field.
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Affiliation(s)
- Giacomo Pucci
- Unit of Internal Medicine, Santa Maria University Hospital, 05100 Terni, Italy
- Department of Medicine and Surgery, University of Perugia, 06125 Perugia, Italy
| | - Andrea Grillo
- Department of Medicine, Surgery and Health Sciences, University of Trieste, 34149 Trieste, Italy
| | - Kalliopi V Dalakleidi
- Biomedical Simulations and Imaging (BIOSIM) Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Emil Fraenkel
- 1st Department of Internal Medicine, Faculty of General Medicine, Pavol Jozef Šafárik University, 04011 Košice, Slovakia
| | - Eugenia Gkaliagkousi
- 3rd Department of Internal Medicine, Aristotle University of Thessaloniki, Papageorgiou General Hospital, 54124 Thessaloniki, Greece
| | - Spyretta Golemati
- Medical School, National and Kapodistrian University of Athens, 10675 Athens, Greece
| | - Andrea Guala
- Vall d'Hebrón Research Institute (VHIR), 08035 Barcelona, Spain
- CIBER CV, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Bernhard Hametner
- AIT Austrian Institute of Technology, Center for Health & Bioresources, Medical Signal Analysis, 1210 Vienna, Austria
| | - Antonios Lazaridis
- 3rd Department of Internal Medicine, Aristotle University of Thessaloniki, Papageorgiou General Hospital, 54124 Thessaloniki, Greece
| | - Christopher C Mayer
- AIT Austrian Institute of Technology, Center for Health & Bioresources, Medical Signal Analysis, 1210 Vienna, Austria
| | - Ioana Mozos
- Department of Functional Sciences-Pathophysiology, Center for Translational Research and Systems Medicine, "Victor Babes" University of Medicine and Pharmacy, 300173 Timisoara, Romania
| | - Telmo Pereira
- H&TRC-Health & Technology Research Center, Coimbra Health School, Polytechnic University of Coimbra, 3000-331 Coimbra, Portugal
- Laboratory for Applied Research in Health (Labinsaúde), Polytechnic University of Coimbra, 3000-331 Coimbra, Portugal
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, H91 YR71 Galway, Ireland
| | - Dimitrios Terentes-Printzios
- First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Davide Agnoletti
- Cardiovascular Internal Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Cardiovascular Internal Medicine, Medical and Surgical Sciences Department, University of Bologna, 40138 Bologna, Italy
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Gu HY, Huang J, Liu X, Qiao SQ, Cao X. Effectiveness of single-lead ECG devices for detecting atrial fibrillation: An overview of systematic reviews. Worldviews Evid Based Nurs 2024; 21:79-86. [PMID: 37417386 DOI: 10.1111/wvn.12667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/02/2023] [Accepted: 05/27/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND Individuals with atrial fibrillation (AF) are at an increased risk for stroke. Early detection of undiagnosed AF by screening is recommended. Single-lead electrocardiogram (ECG) is the most widely used technology in AF detection. Several systematic reviews on the diagnostic accuracy of single-lead ECG devices for AF detection have been performed but have yielded inconclusive results. AIMS The aim of this study was to synthesize the available evidence on the effectiveness of single-lead ECG devices in detecting AF. METHODS An overview of systematic reviews was conducted. Five English databases (Cochrane Database of Systematic Reviews, PubMed, Embase, Ovid, and Web of Science) and two Chinese databases (Wanfang and CNKI) were searched from inception to July 31, 2021. Systematic reviews that examined the accuracy of tools based on single-lead ECG technology for detecting AF were included. A narrative data synthesis was performed. RESULTS Eight systematic reviews were finally included. Systematic reviews with meta-analysis showed that single-lead ECG-based devices had good sensitivity and specificity (both ≥90%) in detecting AF. According to subgroup analysis, the sensitivities of tools used in populations with a history of AF were all >90%. However, among handheld and thoracic placed single-lead ECG devices, large variations in diagnostic performance were observed. LINKING EVIDENCE TO ACTION Single-lead ECG devices can potentially be used for AF detection. Due to the heterogeneity in the study population and tools, future studies are warranted to explore the suitable circumstances in which each tool could be applied for AF screening in an effective and cost-effective manner.
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Affiliation(s)
- Hai Yue Gu
- The School of Nursing, Sun Yat-Sen University, Guangzhou, China
| | - Jun Huang
- Department of Geriatrics, Guangdong General Hospital, Institute of Geriatrics, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xu Liu
- Department of Infectious Disease, Guangdong Provincial Engineering Research Center of Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Shu Qian Qiao
- The School of Nursing, Sun Yat-Sen University, Guangzhou, China
| | - Xi Cao
- The School of Nursing, Sun Yat-Sen University, Guangzhou, China
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13
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Hong W. Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones. JMIR Mhealth Uhealth 2024; 12:e48803. [PMID: 38252596 PMCID: PMC10823426 DOI: 10.2196/48803] [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: 05/07/2023] [Revised: 11/08/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.
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Affiliation(s)
- Wonki Hong
- Department of Digital Healthcare, Daejeon University, Daejeon, Republic of Korea
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14
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Li K, Cardoso C, Moctezuma-Ramirez A, Elgalad A, Perin E. Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:7146. [PMID: 38131698 PMCID: PMC10742885 DOI: 10.3390/ijerph20247146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/06/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
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Affiliation(s)
- Ke Li
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Cristiano Cardoso
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Angel Moctezuma-Ramirez
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Abdelmotagaly Elgalad
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Emerson Perin
- Center for Clinical Research, The Texas Heart Institute, Houston, TX 77030, USA
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15
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Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
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16
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Manetas-Stavrakakis N, Sotiropoulou IM, Paraskevas T, Maneta Stavrakaki S, Bampatsias D, Xanthopoulos A, Papageorgiou N, Briasoulis A. Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis. J Clin Med 2023; 12:6576. [PMID: 37892714 PMCID: PMC10607777 DOI: 10.3390/jcm12206576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/12/2023] [Accepted: 10/14/2023] [Indexed: 10/29/2023] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.
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Affiliation(s)
- Nikolaos Manetas-Stavrakakis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 28 Athens, Greece; (I.M.S.); (A.B.)
| | - Ioanna Myrto Sotiropoulou
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 28 Athens, Greece; (I.M.S.); (A.B.)
| | | | | | | | | | | | - Alexandros Briasoulis
- Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 28 Athens, Greece; (I.M.S.); (A.B.)
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17
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Poh M, Battisti AJ, Cheng L, Lin J, Patwardhan A, Venkataraman GS, Athill CA, Patel NS, Patel CP, Machado CE, Ellis JT, Crosson LA, Tamura Y, Plowman RS, Turakhia MP, Ghanbari H. Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment. J Am Heart Assoc 2023; 12:e030543. [PMID: 37750558 PMCID: PMC10727259 DOI: 10.1161/jaha.123.030543] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/04/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring. METHODS AND RESULTS We evaluated the performance of a wrist-worn device with lead-I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography-based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14-day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15-minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%-98.0%) and 98.1% specific (95% CI, 97.2%-99.1%) for interval-level AF detection. Photoplethysmography-derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, -6.6% to 8.2%). CONCLUSIONS Continuous monitoring using a photoplethysmography-based convolutional neural network incorporated in a wrist-worn device has clinical-grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist-worn wearables for diagnosis and clinical management of AF. REGISTRATION INFORMATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.
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Affiliation(s)
| | | | | | - Janice Lin
- Verily Life SciencesSouth San FranciscoCA
| | | | | | | | | | | | | | | | | | | | - R. Scooter Plowman
- Verily Life SciencesSouth San FranciscoCA
- Stanford University Medical CenterPalo AltoCA
| | | | - Hamid Ghanbari
- Verily Life SciencesSouth San FranciscoCA
- University of MichiganAnn ArborMI
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18
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Atlas SJ, Ashburner JM, Chang Y, Borowsky LH, Ellinor PT, McManus DD, Lubitz SA, Singer DE. Screening for undiagnosed atrial fibrillation using a single-lead electrocardiogram at primary care visits: patient uptake and practitioner perspectives from the VITAL-AF trial. BMC PRIMARY CARE 2023; 24:135. [PMID: 37391738 PMCID: PMC10311748 DOI: 10.1186/s12875-023-02087-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 06/20/2023] [Indexed: 07/02/2023]
Abstract
BACKGROUND Screening for atrial fibrillation (AF) is appealing because AF is common, when undiagnosed may increase stroke risk, and stroke is preventable with anticoagulants. This study assessed patient and primary care practitioner (PCP) acceptability of screening for AF using a 30-s single-lead electrocardiogram (SL-ECG) during outpatient visits. METHODS Secondary analyses of a cluster randomized trial. All patients ≥ 65 years old without prevalent AF seen during a 1-year period and their PCPs. Screening using a SL-ECG was performed by medical assistants during check-in at 8 intervention sites among verbally consenting patients. PCPs were notified of "possible AF" results; management was left to their discretion. Control practices continued with usual care. Following the trial, PCPs were surveyed about AF screening. Outcomes included screening uptake and results, and PCP preferences for screening. RESULTS Fifteen thousand three hundred ninety three patients were seen in intervention practices (mean age 73.9 years old, 59.7% female). Screening occurred at 78% of 38,502 individual encounters, and 91% of patients completed ≥ 1 screening. The positive predictive value of a "Possible AF" result (4.7% of SL-ECG tracings) at an encounter prior to a new AF diagnosis was 9.5%. Same-day 12-lead ECGs were slightly more frequent among intervention (7.0%) than control (6.2%) encounters (p = 0.07). Among the 208 PCPs completing a survey (73.6%; 78.9% intervention, 67.7% control), most favored screening for AF (87.2% vs. 83.6%, respectively), though SL-ECG screening was favored by intervention PCPs (86%) while control PCPs favored pulse palpation (65%). Both groups were less certain if AF screening should be done outside of office visits with patch monitors (47% unsure) or consumer devices (54% unsure). CONCLUSIONS Though the benefits and harms of screening for AF remain uncertain, most older patients underwent screening and PCPs were able to manage SL-ECG results, supporting the feasibility of routine primary care screening. PCPs exposed to a SL-ECG device preferred it over pulse palpation. PCPs were largely uncertain about AF screening done outside of practice visits. TRIAL REGISTRATION ClinicalTrials.gov NCT03515057. Registered May 3, 2018.
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Affiliation(s)
- Steven J Atlas
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Suite 1600, Boston, MA, 02114, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Jeffrey M Ashburner
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Suite 1600, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Yuchiao Chang
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Suite 1600, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| | - Leila H Borowsky
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Suite 1600, Boston, MA, 02114, USA
| | - Patrick T Ellinor
- Harvard Medical School, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - David D McManus
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Steven A Lubitz
- Harvard Medical School, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel E Singer
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Suite 1600, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
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19
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Borrelli N, Grimaldi N, Papaccioli G, Fusco F, Palma M, Sarubbi B. Telemedicine in Adult Congenital Heart Disease: Usefulness of Digital Health Technology in the Assistance of Critical Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5775. [PMID: 37239504 PMCID: PMC10218523 DOI: 10.3390/ijerph20105775] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/26/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023]
Abstract
The number of adults with congenital heart disease (ACHD) has progressively increased in recent years to surpass that of children. This population growth has produced a new demand for health care. Moreover, the 2019 coronavirus pandemic has caused significant changes and has underlined the need for an overhaul of healthcare delivery. As a result, telemedicine has emerged as a new strategy to support a patient-based model of specialist care. In this review, we would like to highlight the background knowledge and offer an integrated care strategy for the longitudinal assistance of ACHD patients. In particular, the emphasis is on recognizing these patients as a special population with special requirements in order to deliver effective digital healthcare.
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Affiliation(s)
| | | | | | | | | | - Berardo Sarubbi
- Adult Congenital Heart Disease Unit, AO Dei Colli-Monaldi Hospital, 80131 Naples, Italy
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20
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Bhuiya T, Roman S, Aydin T, Patel B, Zeltser R, Makaryus AN. Utility of short-term telemetry heart rhythm monitoring and CHA 2DS 2-VASc stratification in patients presenting with suspected cerebrovascular accident. World J Cardiol 2023; 15:56-63. [PMID: 36911749 PMCID: PMC9993929 DOI: 10.4330/wjc.v15.i2.56] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/10/2023] [Accepted: 02/07/2023] [Indexed: 02/21/2023] Open
Abstract
BACKGROUND Inpatient telemetry heart rhythm monitoring overuse has been linked to higher healthcare costs. AIM To evaluate if CHA2DS2-VASc score could be used to indicate if a patient admitted with possible cerebrovascular accident (CVA) or transient ischemic attack (TIA) requires inpatient telemetry monitoring. METHODS A total of 257 patients presenting with CVA or TIA and placed on telemetry monitoring were analyzed retrospectively. We investigated the utility of telemetry monitoring to diagnose atrial fibrillation/flutter and the CHA2DS2-VASc scoring tool to stratify the risk of having CVA/TIA in these patients. RESULTS In our study population, 63 (24.5%) of the patients with CVA/TIA and telemetry monitoring were determined to have no ischemic neurologic event. Of the 194 (75.5) patients that had a confirmed CVA/TIA, only 6 (2.3%) had an arrhythmia detected during their inpatient telemetry monitoring period. Individuals with a confirmed CVA/TIA had a statistically significant higher CHA2DS2-VASc score compared to individuals without an ischemic event (3.59 vs 2.61, P < 0.001). CONCLUSION Given the low percentage of inpatient arrhythmias identified, further research should focus on discretionary use of inpatient telemetry on higher risk patients to diagnose the arrhythmias commonly leading to CVA/TIA. A prospective study assessing event rate of CVA/TIA in patients with higher CHA2DS2-VASc score should be performed to validate the CHA2DS2-VASc score as a possible risk stratifying tool for patients at risk for CVA/TIA.
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Affiliation(s)
- Tanzim Bhuiya
- Department of Cardiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11553, United States
| | - Sherif Roman
- Department of Cardiology, St. Joseph's University Medical Center, Paterson, NJ 07503, United States
| | - Taner Aydin
- Department of Cardiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11553, United States
| | - Bhakti Patel
- Department of Cardiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11553, United States
| | - Roman Zeltser
- Department of Cardiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11553, United States
- Department of Cardiology, Nassau University Medical Center, East Meadow, NY 11554, United States
| | - Amgad N Makaryus
- Department of Cardiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11553, United States
- Department of Cardiology, Nassau University Medical Center, East Meadow, NY 11554, United States.
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21
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Kalarus Z, Mairesse GH, Sokal A, Boriani G, Średniawa B, Casado-Arroyo R, Wachter R, Frommeyer G, Traykov V, Dagres N, Lip GYH. Searching for atrial fibrillation: looking harder, looking longer, and in increasingly sophisticated ways. An EHRA position paper. Europace 2023; 25:185-198. [PMID: 36256580 PMCID: PMC10112840 DOI: 10.1093/europace/euac144] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Zbigniew Kalarus
- Department of Cardiology, DMS in Zabrze, Medical University of Silesia, Katowice, Poland
- Department of Cardiology, Silesian Center for Heart Diseases, Zabrze, Poland
| | - Georges H Mairesse
- Department of Cardiology and Electrophysiology, Cliniques du Sud Luxembourg—Vivalia, Arlon, Belgium
| | - Adam Sokal
- Department of Cardiology, Silesian Center for Heart Diseases, Zabrze, Poland
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Beata Średniawa
- Department of Cardiology, DMS in Zabrze, Medical University of Silesia, Katowice, Poland
- Department of Cardiology, Silesian Center for Heart Diseases, Zabrze, Poland
| | | | - Rolf Wachter
- Clinic and Policlinic for Cardiology, University Hospital Leipzig, Leipzig, Germany
| | - Gerrit Frommeyer
- Department of Cardiology II (Electrophysiology), University Hospital Münster, Münster, Germany
| | - Vassil Traykov
- Department of Invasive Electrophysiology and Cardiac Pacing, Acibadem City Clinic Tokuda Hospital, Sofia, Bulgaria
| | - Nikolaos Dagres
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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22
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Scholten J, Jansen WPJ, Horsthuis T, Mahes AD, Winter MM, Zwinderman AH, Keijer JT, Minneboo M, de Groot JR, Bokma JP. Six-lead device superior to single-lead smartwatch ECG in atrial fibrillation detection. Am Heart J 2022; 253:53-58. [PMID: 35850242 DOI: 10.1016/j.ahj.2022.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/28/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
This was a head-to-head comparative study on different electrocardiogram (ECG)-based smartwatches and devices for atrial fibrillation detection. We prospectively included 220 patients scheduled for electrical cardioversion and recorded ECGs with 3 different devices (Withings Move ECG, Apple Watch 5, Kardia Mobile 6-leads) as well as the standard 12-lead ECG (gold standard), both before and after cardioversion. All atrial fibrillation detection algorithms had high accuracy (sensitivity and specificity: 91-99%) but were hampered by uninterpretable recordings (20-24%). In cardiologists' interpretation, the 6-lead device was superior (sensitivity 99%, specificity 97%) to both single-lead smartwatches (P < .05) for atrial fibrillation detection.
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Affiliation(s)
- Josca Scholten
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands; Location Academic Medical Center, Department of Cardiology, Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, North Holland 1105 AZ, the Netherlands
| | - Ward P J Jansen
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands
| | - Thomas Horsthuis
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands
| | - Anuska D Mahes
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands
| | - Michiel M Winter
- Location Academic Medical Center, Department of Cardiology, Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, North Holland 1105 AZ, the Netherlands
| | - Aeilko H Zwinderman
- Location Academic Medical Center, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Center, Amsterdam, North Holland, the Netherlands
| | - Jan T Keijer
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands
| | - Madelon Minneboo
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands; Location Academic Medical Center, Department of Cardiology, Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, North Holland 1105 AZ, the Netherlands
| | - Joris R de Groot
- Location Academic Medical Center, Department of Cardiology, Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, North Holland 1105 AZ, the Netherlands
| | - Jouke P Bokma
- Department of Cardiology, Tergooi Hospital, Blaricum, North Holland, the Netherlands; Location Academic Medical Center, Department of Cardiology, Amsterdam University Medical Center, Meibergdreef 9, Amsterdam, North Holland 1105 AZ, the Netherlands.
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23
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Papaccioli G, Bassi G, Lugi C, Parente E, D'Andrea A, Proietti R, Imbalzano E, Al Turki A, Russo V. Smartphone and new tools for atrial fibrillation diagnosis: evidence for clinical applicability. Minerva Cardiol Angiol 2022; 70:616-627. [PMID: 35212504 DOI: 10.23736/s2724-5683.22.05841-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults. AF increases the risk of heart failure, cardiac ischemic disease, dementia and Alzheimer's disease. Either clinical and subclinical AF increase the risk of stroke and worsen the patients' clinical outcome. The early diagnosis of AF episodes, even if asymptomatic or clinically silent, is of pivotal importance to ensure prompt and adequate thromboembolic risk prevention therapies. The development of technology is allowing new systematic mass screening possibilities, especially in patients with higher stroke risk. The mobile health devices available for AF detection are: smartphones, wrist-worn, earlobe sensors and handheld ECG. These devices showed a high accuracy in AF detection especially when a combined approach with single-lead ECG and photoplethysmography algorithms is used. The use of wearable devices for AF screening is a feasible method but more head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness across different study populations.
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Affiliation(s)
- Giovanni Papaccioli
- Department of Medical Translational Sciences, University of Campania "Luigi Vanvitelli" - Monaldi Hospital, Naples, Italy
| | - Giuseppe Bassi
- Department of Medical Translational Sciences, University of Campania "Luigi Vanvitelli" - Monaldi Hospital, Naples, Italy
| | - Cecilia Lugi
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, Rome, Italy
| | - Erika Parente
- Department of Medical Translational Sciences, University of Campania "Luigi Vanvitelli" - Monaldi Hospital, Naples, Italy
| | | | - Riccardo Proietti
- Liverpool Center for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Egidio Imbalzano
- Department of Clinical and Experimental Medicine, University Hospital of Messina "G. Martino", University of Messina, Messina, Italy
| | - Ahmed Al Turki
- Division of Cardiology, McGill University Health Center, Montreal, QC, Canada
| | - Vincenzo Russo
- Department of Medical Translational Sciences, University of Campania "Luigi Vanvitelli" - Monaldi Hospital, Naples, Italy -
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24
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Sattar Y, Song D, Sarvepalli D, Zaidi SR, Ullah W, Arshad J, Mir T, Zghouzi M, Elgendy IY, Qureshi W, Chalfoun N, Alraies MC. Accuracy of pulsatile photoplethysmography applications or handheld devices vs. 12-lead ECG for atrial fibrillation screening: a systematic review and meta-analysis. J Interv Card Electrophysiol 2022; 65:33-44. [PMID: 34775555 DOI: 10.1007/s10840-021-01068-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/22/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND The relative accuracy of pulsatile photoplethysmography applications (PPG) or handheld (HH) devices compared with the gold standard 12-lead electrocardiogram (ECG) for the diagnosis of atrial fibrillation is unknown. METHODS Digital databases were searched to identify relevant articles. Raw data were pooled using a bivariate model to calculate diagnostic accuracy measures and estimate Hierarchical Summary Receiver Operating Characteristic (HSROC). RESULTS A total of 10 articles comprising 4296 patients (mean age 68.9 years, with 56% males) were included in the analysis. Compared with EKG, the pooled sensitivity of PPG and HH devices in AF detection was 0.93 (95% CI 0.87-0.96; p < 0.05) and 0.87 (95% CI. 0.74-0.94; p < 0.05), respectively. The pooled specificity of PPG and HH devices in AF detection was 0.91 (95% CI 0.88-0.94; p < 0.05) and 0.96 (95% CI 0.90-0.98; p < 0.05), respectively. The diagnostic odds ratio was 129 and 144 for PPG and HH devices, respectively. Fagan's nomogram showed the probability of a patient having AF and normal rhythm on PPG or HH devices was 2-3%, while the post-test probability of having AF with an irregular R-R interval on PPG or HH devices was 73% and 82%, respectively. The scatter plot of positive and negative likelihood ratio showed high confirmation of AF and reliability of exclusion of absence of irregular R-R intervals (positive likelihood ratio > 10, and negative likelihood ratio < 0.1) on HH devices while PPG was used as confirmation only. CONCLUSIONS The PPG or HH devices can serve as a reliable alternative for the detection of AF.
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Affiliation(s)
- Yasar Sattar
- Cardiology, West Virginia University, Morgantown, WV, USA
| | - David Song
- Cardiology, West Virginia University, Morgantown, WV, USA
| | | | | | - Waqas Ullah
- Cardiology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Junaid Arshad
- Internal Medicine, Institute of Medical Sciences, Islamabad, Pakistan
| | - Tanveer Mir
- Cardiology, Detroit Medical Center Heart Hospital, 311 Mack Ave, Detroit, MI, 48201, USA
| | - Mohamed Zghouzi
- Cardiology, Detroit Medical Center Heart Hospital, 311 Mack Ave, Detroit, MI, 48201, USA
| | | | - Waqas Qureshi
- Cardiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Nagib Chalfoun
- Cardiology, Spectrum Health Heart and Vascular, Michigan State University, Grand Rapids, MI, USA
| | - MChadi Alraies
- Cardiology, Detroit Medical Center Heart Hospital, 311 Mack Ave, Detroit, MI, 48201, USA.
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25
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Gill S, Bunting KV, Sartini C, Cardoso VR, Ghoreishi N, Uh HW, Williams JA, Suzart-Woischnik K, Banerjee A, Asselbergs FW, Eijkemans M, Gkoutos GV, Kotecha D. Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis. Heart 2022; 108:1600-1607. [PMID: 35277454 PMCID: PMC9554073 DOI: 10.1136/heartjnl-2021-320417] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 01/24/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES Timely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection. METHODS This is a systematic review of MEDLINE, EMBASE and Cochrane (1980-December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool. RESULTS 28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%-98%), with substantial heterogeneity (p<0.01). Studies were of poor quality overall and none met all the QUADAS-2 criteria, with particular issues regarding selection bias and the potential for publication bias. CONCLUSION PPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection.
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Affiliation(s)
- Simrat Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Karina V Bunting
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Claudio Sartini
- Medical Affairs and Pharmacovigilance, Pharmaceuticals, Integrated Evidence Generation, Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
| | - Victor Roth Cardoso
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Narges Ghoreishi
- Medical Affairs and Pharmacovigilance, Pharmaceuticals, Integrated Evidence Generation, Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
| | - Hae-Won Uh
- Julius Center for Health Sciences and Primary Care, University Medical Centre, Utrecht, Netherlands
| | - John A Williams
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Kiliana Suzart-Woischnik
- Medical Affairs and Pharmacovigilance, Pharmaceuticals, Integrated Evidence Generation, Bayer AG, Leverkusen, Nordrhein-Westfalen, Germany
| | - Amitava Banerjee
- Farr Institute of Health Informatics Research, University College London, London, UK
| | - Folkert W Asselbergs
- Department of Cardiology, University Medical Centre Utrecht Department of Cardiology, Utrecht, Netherlands
- Department of Cardiology, University College London Faculty of Population Health Sciences, London, UK
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Mjc Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Centre, Utrecht, Netherlands
| | - Georgios V Gkoutos
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- Health Data Research UK Midlands Site, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, University Medical Centre Utrecht Department of Cardiology, Utrecht, Netherlands
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26
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Biersteker TE, Boogers MJ, Schalij MJ, Penning de Vries BBL, Groenwold RHH, van Alem AP, de Weger A, van Hof N, Treskes RW. Mobile health vs. standard care after cardiac surgery: results of The Box 2.0 study. Europace 2022; 25:49-58. [PMID: 35951658 PMCID: PMC9907478 DOI: 10.1093/europace/euac115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
AIMS Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery. METHODS AND RESULTS We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27). CONCLUSION Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research.
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Affiliation(s)
- Tom E Biersteker
- Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Mark J Boogers
- Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | | | - Bas B L Penning de Vries
- Department of Clinical Epidemiology and Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Rolf H H Groenwold
- Department of Clinical Epidemiology and Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Anouk P van Alem
- Department of Cardiology, Haaglanden Medisch Centrum, The Hague, The Netherlands
| | - Arend de Weger
- Department of Cardiothoracic Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicolette van Hof
- Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
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27
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Gonçalves-Teixeira P, Costa T, Fragoso I, Ferreira D, Brandão M, Leite-Moreira A, Sampaio F, Ribeiro J, Fontes-Carvalho R. Screening, Diagnosis and Management of Atrial Fibrillation in Cancer Patients: Current Evidence and Future Perspectives. Arq Bras Cardiol 2022; 119:328-341. [PMID: 35946695 PMCID: PMC9363048 DOI: 10.36660/abc.20201362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 05/12/2021] [Indexed: 11/18/2022] Open
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in the general population, carrying a high morbimortality burden, and this also holds true in cancer patients. The association between AF and cancer goes even further, with some studies suggesting that AF can be a marker of occult cancer. There is, however, a remarkable paucity of data concerning specific challenges of AF management in cancer patients. AF prompt recognition and management in this special population can lessen the arrhythmia-related morbidity and have an important prognostic benefit. This review will focus on current AF diagnosis and management challenges in cancer patients, with special emphasis on AF screening strategies and devices, and anticoagulation therapy with non-vitamin K antagonist oral anti-coagulants (NOACs) for thromboembolic prevention in these patients. Some insights concerning future perspectives for AF prevention, diagnosis, and treatment in this special population will also be addressed.
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Affiliation(s)
- Pedro Gonçalves-Teixeira
- Departamento de Cardiologia, Vila Nova de Gaia Hospital Center, Gaia - Portugal.,Departamento de Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto - Portugal.,Clínica Cardio-Oncológica, Centro Hospitalar Vila Nova de Gaia, Gaia - Portugal
| | - Telma Costa
- Clínica Cardio-Oncológica, Centro Hospitalar Vila Nova de Gaia, Gaia - Portugal.,Departamento de Oncologia, Centro Hospitalar Vila Nova de Gaia, Gaia - Portugal
| | - Isabel Fragoso
- Unidade de Atenção Primária à Saúde Aracetti, Arazede - Portugal
| | - Diogo Ferreira
- Departamento de Cardiologia, Vila Nova de Gaia Hospital Center, Gaia - Portugal.,Departamento de Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto - Portugal
| | - Mariana Brandão
- Departamento de Cardiologia, Vila Nova de Gaia Hospital Center, Gaia - Portugal
| | - Adelino Leite-Moreira
- Departamento de Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto - Portugal.,Departamento de Cirurgia Cardiotorácica, Hospital Universitário São João, Porto - Portugal.,Unidade de Pesquisa Cardiovascular (UnIC), Faculdade de Medicina, Universidade do Porto, Porto - Portugal
| | - Francisco Sampaio
- Departamento de Cardiologia, Vila Nova de Gaia Hospital Center, Gaia - Portugal.,Departamento de Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto - Portugal
| | - José Ribeiro
- Departamento de Cardiologia, Vila Nova de Gaia Hospital Center, Gaia - Portugal.,Clínica Cardio-Oncológica, Centro Hospitalar Vila Nova de Gaia, Gaia - Portugal
| | - Ricardo Fontes-Carvalho
- Departamento de Cardiologia, Vila Nova de Gaia Hospital Center, Gaia - Portugal.,Departamento de Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto - Portugal
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28
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Svennberg E, Tjong F, Goette A, Akoum N, Di Biase L, Bordachar P, Boriani G, Burri H, Conte G, Deharo JC, Deneke T, Drossart I, Duncker D, Han JK, Heidbuchel H, Jais P, de Oliveira Figueiredo MJ, Linz D, Lip GYH, Malaczynska-Rajpold K, Márquez MF, Ploem C, Soejima K, Stiles MK, Wierda E, Vernooy K, Leclercq C, Meyer C, Pisani C, Pak HN, Gupta D, Pürerfellner H, Crijns HJGM, Chavez EA, Willems S, Waldmann V, Dekker L, Wan E, Kavoor P, Turagam MK, Sinner M. How to use digital devices to detect and manage arrhythmias: an EHRA practical guide. Europace 2022; 24:979-1005. [PMID: 35368065 PMCID: PMC11636571 DOI: 10.1093/europace/euac038] [Citation(s) in RCA: 155] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Emma Svennberg
- Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Fleur Tjong
- Heart Center, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Andreas Goette
- St. Vincenz Hospital Paderborn, Paderborn, Germany
- MAESTRIA Consortium/AFNET, Münster, Germany
| | - Nazem Akoum
- Heart Institute, University of Washington School of Medicine, Seattle, WA, USA
| | - Luigi Di Biase
- Albert Einstein College of Medicine at Montefiore Hospital, New York, NY, USA
| | | | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Haran Burri
- Cardiology Department, University Hospital of Geneva, Geneva, Switzerland
| | - Giulio Conte
- Cardiocentro Ticino Institute, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Jean Claude Deharo
- Assistance Publique—Hôpitaux de Marseille, Centre Hospitalier Universitaire La Timone, Service de Cardiologie, Marseille, France
- Aix Marseille Université, C2VN, Marseille, France
| | - Thomas Deneke
- Heart Center Bad Neustadt, Bad Neustadt an der Saale, Germany
| | - Inga Drossart
- European Society of Cardiology, Sophia Antipolis, France
- ESC Patient Forum, Sophia Antipolis, France
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| | - Janet K Han
- Cardiac Arrhythmia Centers, Veterans Affairs Greater Los Angeles Healthcare System and University of California, Los Angeles, CA, USA
| | - Hein Heidbuchel
- Department of Cardiology, Antwerp University Hospital, Antwerp, Belgium
- Cardiovascular Research Group, Antwerp University, Antwerp, Belgium
| | - Pierre Jais
- Bordeaux University Hospital, Bordeaux, France
| | | | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, the Netherlands
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | | | - Manlio F Márquez
- Department of Electrocardiology, Instituto Nacional de Cardiología, Mexico City, Mexico
- Cardiology, Electrophysiology Service, American British Cowdray Medical Center, Mexico City, México
| | - Corrette Ploem
- Department of Ethics, Law and Medical Humanities, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Kyoko Soejima
- Kyorin University School of Medicine, Mitaka, Tokyo, Japan
| | - Martin K Stiles
- Waikato Clinical School, University of Auckland, Hamilton, New Zealand
| | - Eric Wierda
- Department of Cardiology, Dijklander Hospital, Hoorn, the Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Christian Meyer
- Division of Cardiology/Angiology/Intensive Care, EVK Düsseldorf, Teaching Hospital University of Düsseldorf, Düsseldorf, Germany
| | - Cristiano Pisani
- Arrhythmia Unit, Heart Institute, InCor, University of São Paulo Medical School, São Paulo, Brazil
| | - Hui Nam Pak
- Yonsei University, Severance Cardiovascular Hospital, Yonsei University Health System, Seoul, Republic of Korea
| | - Dhiraj Gupta
- Faculty of Health and Life Sciences, Liverpool Heart and Chest Hospital, University of Liverpool, Liverpool, UK
| | | | - H J G M Crijns
- Em. Professor of Cardiology, University of Maastricht, Maastricht, Netherlands
| | - Edgar Antezana Chavez
- Division of Cardiology, Hospital General de Agudos Dr. Cosme Argerich, Pi y Margall 750, C1155AHB Buenos Aires, Argentina
- Division of Cardiology, Hospital Belga, Antezana 455, C0000 Cochabamba, Bolivia
| | | | - Victor Waldmann
- Electrophysiology Unit, European Georges Pompidou Hospital, Paris, France
- Adult Congenital Heart Disease Unit, European Georges Pompidou Hospital, Paris, France
| | - Lukas Dekker
- Catharina Ziekenhuis Eindhoven, Eindhoven, Netherlands
| | - Elaine Wan
- Cardiology and Cardiac Electrophysiology, Columbia University, New York, NY, USA
| | - Pramesh Kavoor
- Cardiology Department, Westmead Hospital, Westmead, New South Wales, Australia
| | | | - Moritz Sinner
- Univ. Hospital Munich, Campus Grosshadern, Munich, Germany
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29
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El-Sherif DM, Abouzid M. Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak. Global Health 2022; 18:67. [PMID: 35765078 PMCID: PMC9238163 DOI: 10.1186/s12992-022-00856-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mobile health applications (mHealth apps) offer enormous promise for illness monitoring and treatment to improve the provided medical care and promote health and wellbeing. OBJECTIVE We applied bibliometric quantitative analysis and network visualization to highlight research trends and areas of particular interest. We expect by summarizing the trends in mHealth app research, our work will serve as a roadmap for future investigations. METHODS Relevant English publications were extracted from the Scopus database. VOSviewer (version 1.6.17) was used to build coauthorship networks of authors, countries, and the co-occurrence networks of author keywords. RESULTS We analyzed 550 published articles on mHealth apps from 2020 to February 1, 2021. The yearly publications increased from 130 to 390 in 2021. JMIR mHealth and uHealth (33/550, 6.0%), J. Med. Internet Res. (27/550, 4.9%), JMIR Res. Protoc. (22/550, 4.0%) were the widest journals for these publications. The United States has the largest number of publications (143/550, 26.0%), and England ranks second (96/550, 17.5%). The top three productive authors were: Giansanti D., Samuel G., Lucivero F., and Zhang L. Frequent authors' keywords have formed major 4 clusters representing the hot topics in the field: (1) artificial intelligence and telehealthcare; (2) digital contact tracing apps, privacy and security concerns; (3) mHealth apps and mental health; (4) mHealth apps in public health and health promotion. CONCLUSIONS mHealth apps undergo current developments, and they remain hot topics in COVID-19. These findings might be useful in determining future perspectives to improve infectious disease control and present innovative solutions for healthcare.
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Affiliation(s)
- Dina M. El-Sherif
- National Institute of Oceanography and Fisheries (NIOF), Cairo, Egypt
| | - Mohamed Abouzid
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-781 Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 60-781 Poznan, Poland
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30
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Santala OE, Lipponen JA, Jäntti H, Rissanen TT, Tarvainen MP, Laitinen TP, Laitinen TM, Castrén M, Väliaho ES, Rantula OA, Naukkarinen NS, Hartikainen JEK, Halonen J, Martikainen TJ. Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study. JMIR Cardio 2022; 6:e31230. [PMID: 35727618 PMCID: PMC9257607 DOI: 10.2196/31230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 12/27/2021] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Background The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. Objective The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. Methods Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. Results Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. Conclusions The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial Registration ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335
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Affiliation(s)
- Onni E Santala
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jukka A Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Helena Jäntti
- Centre for Prehospital Emergency Care, Kuopio University Hospital, Kuopio, Finland
| | | | - Mika P Tarvainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Tomi P Laitinen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Tiina M Laitinen
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Maaret Castrén
- Department of Emergency Medicine and Services, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Eemu-Samuli Väliaho
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Olli A Rantula
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora S Naukkarinen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Juha E K Hartikainen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Heart Center, Kuopio University Hospital, Kuopio, Finland
| | - Jari Halonen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Heart Center, Kuopio University Hospital, Kuopio, Finland
| | - Tero J Martikainen
- Department of Emergency Care, Kuopio University Hospital, Kuopio, Finland
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31
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Kumar D, Maharjan R, Maxhuni A, Dominguez H, Frølich A, Bardram JE. mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening. ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2022; 3:1-28. [DOI: 10.1145/3494581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/01/2021] [Indexed: 07/25/2023]
Abstract
This article presents the design, technical implementation, and feasibility evaluation of
mCardia
—a context-aware, mobile
electrocardiogram
(ECG) collection system for longitudinal arrhythmia screening under free-living conditions. Along with ECG,
mCardia
also records active and passive contextual data, including patient-reported symptoms and physical activity. This contextual data can provide a more accurate understanding of what happens before, during, and after an arrhythmia event, thereby providing additional information in the diagnosis of arrhythmia. By using a plugin-based architecture for ECG and contextual sensing,
mCardia
is device-agnostic and can integrate with various wireless ECG devices and supports cross-platform deployment. We deployed the
mCardia
system in a feasibility study involving 24 patients who used the system over a two-week period. During the study, we observed high patient acceptance and compliance with a satisfactory yield of collected ECG and contextual data. The results demonstrate the high usability and feasibility of
mCardia
for longitudinal ambulatory monitoring under free-living conditions. The article also reports from two clinical cases, which demonstrate how a cardiologist can utilize the collected contextual data to improve the accuracy of arrhythmia analysis. Finally, the article discusses the lessons learned and the challenges found in the
mCardia
design and the feasibility study.
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Affiliation(s)
- Devender Kumar
- Department of Health Technology, Technical University of Denmark, Copenhagen, Denmark
| | - Raju Maharjan
- Department of Health Technology, Technical University of Denmark, Copenhagen, Denmark
| | - Alban Maxhuni
- Department of Health Technology, Technical University of Denmark, Copenhagen, Denmark
| | - Helena Dominguez
- Bispebjerg-Frederiksberg Hospital, Department of Cardiology, Copenhagen, Denmark
| | - Anne Frølich
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Jakob E. Bardram
- Department of Health Technology, Technical University of Denmark, Copenhagen, Denmark
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32
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Bonini N, Vitolo M, Imberti JF, Proietti M, Romiti GF, Boriani G, Paaske Johnsen S, Guo Y, Lip GYH. Mobile health technology in atrial fibrillation. Expert Rev Med Devices 2022; 19:327-340. [PMID: 35451347 DOI: 10.1080/17434440.2022.2070005] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming widespread, thanks to everyday life devices such as smartphones. Their use is validated both in monitoring and in screening scenarios. In the published literature, the diagnostic accuracy of mHealth solutions wide differs, and their current clinical use is not well established in principal guidelines. AREAS COVERED mHealth solutions have progressively built an AF-detection chain to guide patients from the device's alert signal to the health care practitioners' (HCPs) attention. This review aims to critically evaluate the latest evidence regarding mHealth devices and the future possible patient's uses in everyday life. EXPERT OPINION The patients are the first to be informed of the rhythm anomaly, leading to the urgency of increasing the patients' AF self-management. Furthermore, HCPs need to update themselves about mHealth devices use in clinical practice. Nevertheless, these are promising instruments in specific populations, such as post-stroke patients, to promote an early arrhythmia diagnosis in the post-ablation/cardioversion period, allowing checks on the efficacy of the treatment or intervention.
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Affiliation(s)
- Niccolò Bonini
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Marco Vitolo
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Jacopo Francesco Imberti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Marco Proietti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.,Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy
| | - Giulio Francesco Romiti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Department of Translational and Precision Medicine, Sapienza-University of Rome, Rome, Italy
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Søren Paaske Johnsen
- Danish Center for Clinical Health Services Research (DACS), Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Yutao Guo
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Danish Center for Clinical Health Services Research (DACS), Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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33
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Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, Allen J, Hallab M, Bianchini E, Mayer CC, Terentes-Printzios D, Dittrich V, Hametner B, Veerasingam D, Žikić D, Marozas V. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol 2022; 322:H493-H522. [PMID: 34951543 PMCID: PMC8917928 DOI: 10.1152/ajpheart.00392.2021] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 12/07/2022]
Abstract
The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Research Centre for Biomedical Engineering, University of London, London, United Kingdom
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Kristjan Pilt
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Martin Bachler
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Serena Zanelli
- Laboratoire Analyze, Géométrie et Applications, University Sorbonne Paris Nord, Paris, France
- Axelife, Redon, France
| | - Dániel Kulin
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- E-Med4All Europe, Limited, Budapest, Hungary
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Magid Hallab
- Axelife, Redon, France
- Centre de recherche et d'Innovation, Clinique Bizet, Paris, France
| | | | - Christopher C Mayer
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dimitrios Terentes-Printzios
- Hypertension and Cardiometabolic Unit, First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Verena Dittrich
- Redwave Medical, Gesellschaft mit beschränkter Haftung, Jena, Germany
| | - Bernhard Hametner
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, Galway, Ireland
| | - Dejan Žikić
- Faculty of Medicine, Institute of Biophysics, University of Belgrade, Belgrade, Serbia
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
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34
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Liu Z, Zhou B, Jiang Z, Chen X, Li Y, Tang M, Miao F. Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network. J Am Heart Assoc 2022; 11:e023555. [PMID: 35322685 PMCID: PMC9075456 DOI: 10.1161/jaha.121.023555] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been reported. Our study investigated the feasibility of using photoplethysmography signals and a deep convolutional neural network to classify multiclass arrhythmia types. Methods and Results ECG and photoplethysmography signals were collected simultaneously from a group of patients who underwent radiofrequency ablation for arrhythmias. A deep convolutional neural network was developed to classify multiple rhythms based on 10‐second photoplethysmography waveforms. Classification performance was evaluated by calculating the area under the microaverage receiver operating characteristic curve, overall accuracy, sensitivity, specificity, and positive and negative predictive values against annotations on the rhythm of arrhythmias provided by 2 cardiologists consulting the ECG results. A total of 228 patients were included; 118 217 pairs of 10‐second photoplethysmography and ECG waveforms were used. When validated against an independent test data set (23 384 photoplethysmography waveforms from 45 patients), the DCNN achieved an overall accuracy of 85.0% for 6 rhythm types (sinus rhythm, premature ventricular contraction, premature atrial contraction, ventricular tachycardia, supraventricular tachycardia, and atrial fibrillation); the microaverage area under the microaverage receiver operating characteristic curve was 0.978; the average sensitivity, specificity, and positive and negative predictive values were 75.8%, 96.9%, 75.2%, and 97.0%, respectively. Conclusions This study demonstrated the feasibility of classifying multiclass arrhythmias from photoplethysmography signals using deep learning techniques. The approach is attractive for population‐based screening and may hold promise for the long‐term surveillance and management of arrhythmia. Registration URL: www.chictr.org.cn. Identifier: ChiCTR2000031170.
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Affiliation(s)
- Zengding Liu
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China.,University of Chinese Academy of Sciences Beijing China
| | - Bin Zhou
- Department of Cardiology Laboratory of Heart Center Zhujiang HospitalSouthern Medical University Guangzhou China.,Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Zhiming Jiang
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Xi Chen
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Ye Li
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China.,Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Min Tang
- Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Fen Miao
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
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35
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Chan N, Orchard J, Agbayani M, Boddington D, Chao T, Johar S, John B, Joung B, Krishinan S, Krittayaphong R, Kurokawa S, Lau C, Lim TW, Linh PT, Long VH, Naik A, Okumura Y, Sasano T, Yan B, Raharjo SB, Hanafy DA, Yuniadi Y, Nwe N, Awan ZA, Huang H, Freedman B. 2021 Asia Pacific Heart Rhythm Society (APHRS) practice guidance on atrial fibrillation screening. J Arrhythm 2022; 38:31-49. [PMID: 35222749 PMCID: PMC8851593 DOI: 10.1002/joa3.12669] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/11/2021] [Accepted: 12/15/2021] [Indexed: 12/19/2022] Open
Abstract
In this paper, the Asia Pacific Heart Rhythm Society (APHRS) sought to provide practice guidance on AF screening based on recent evidence, with specific considerations relevant to the Asia-Pacific region. A key recommendation is opportunistic screening for people aged ≥65 years (all countries), with systematic screening to be considered for people aged ≥75 years or who have additional risk factors (all countries).
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Affiliation(s)
- Ngai‐Yin Chan
- Princess Margaret HospitalHong Kong Special Administrative RegionChina
| | - Jessica Orchard
- Agnes Ginges Centre for Molecular CardiologyCentenary InstituteSydneyAustralia
- Charles Perkins CentreThe University of SydneySydneyAustralia
| | - Michael‐Joseph Agbayani
- Division of ElectrophysiologyPhilippine Heart CenterManilaPhilippines
- Division of Cardiovascular MedicinePhilippine General HospitalManilaPhilippines
| | | | - Tze‐Fan Chao
- Division of CardiologyDepartment of MedicineTaipei Veterans General HospitalTaipeiTaiwan
- Institute of Clinical Medicine, and Cardiovascular Research CenterNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Sofian Johar
- Consultant CardiologistHead of CardiologyRIPAS HospitalBandar Seri BegawanBrunei Darussalam
- Consultant Cardiac ElectrophysiologistHead of Cardiac ElectrophysiologyGleneagles JPMCJerudongBrunei Darussalam
- Institute of Health SciencesUniversiti Brunei DarussalamJalan Tungku Link GadongBrunei Darussalam
| | - Bobby John
- Cardiology UnitTownsville University HospitalTownsvilleAustralia
- James Cook UniversityTownsvilleAustralia
| | - Boyoung Joung
- Internal MedicineYonsei University College of MedicineSeoulRepublic of Korea
| | | | - Rungroj Krittayaphong
- Division of CardiologyDepartment of MedicineSiriraj HospitalMahidol UniversityBangkokThailand
| | - Sayaka Kurokawa
- Division of CardiologyDepartment of MedicineNihon University School of MedicineTokyoJapan
| | - Chu‐Pak Lau
- Department of MedicineQueen Mary HospitalThe University of Hong KongHong Kong Special Administrative RegionChina
| | - Toon Wei Lim
- National University HospitalNational University Heart CentreSingapore
| | | | | | - Ajay Naik
- Division of CardiologyCare Institute of Medical Sciences HospitalAhmedabadIndia
| | - Yasuo Okumura
- Division of CardiologyDepartment of MedicineNihon University School of MedicineTokyoJapan
| | - Tetsuo Sasano
- Department of Cardiovascular MedicineTokyo Medical and Dental UniversityTokyoJapan
| | - Bernard Yan
- Melbourne Brain CentreUniversity of MelbourneMelbourneAustralia
| | - Sunu Budhi Raharjo
- Department of Cardiology and Vascular MedicineFaculty of MedicineUniversitas Indonesia, and National Cardiovascular Center Harapan KitaJakartaIndonesia
| | - Dicky Armein Hanafy
- Department of Cardiology and Vascular MedicineFaculty of MedicineUniversitas Indonesia, and National Cardiovascular Center Harapan KitaJakartaIndonesia
| | - Yoga Yuniadi
- Department of Cardiology and Vascular MedicineFaculty of MedicineUniversitas Indonesia, and National Cardiovascular Center Harapan KitaJakartaIndonesia
| | - Nwe Nwe
- Department of CardiologyYangon General HospitalUniversity of MedicineYangonMyanmar
| | | | - He Huang
- Wuhan University Renmin HospitalWuhanChina
| | - Ben Freedman
- Charles Perkins CentreThe University of SydneySydneyAustralia
- Heart Research InstituteCharles Perkins CentreUniversity of SydneySydneyAustralia
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36
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Belenkov YN, Kozhevnikova MV. [Mobile health technologies in cardiology]. KARDIOLOGIIA 2022; 62:4-12. [PMID: 35168528 DOI: 10.18087/cardio.2022.1.n1963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/13/2022] [Indexed: 06/14/2023]
Abstract
Digital medicine is becoming an essential part of the healthcare system. The intense development of mobile technologies, the global coverage of mobile networks, and the growing attachment in the society to mobile devices have prompted the creation of mobile healthcare (mHealth). At present, mobile healthcare technologies have been tested in various cardiovascular diseases. Among the main tasks set for telemedicine, it is necessary to note improvements of general medical care, monitoring of patients' condition, accuracy of clinical diagnoses, timely correction of therapy, and improvement of emergency care. Clinical studies are performed in parallel with active work in the field of informational technologies to provide safety of data storage and intellectual processing. Finally, despite the broad public support for the development of this area of medicine, the search continues for methods to improve patients' compliance with the prescribed therapy. This article presents current information about the use of mHealth in cardiology, study results, prospects of mobile healthcare, and major difficulties in implementing projects in this area.
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Affiliation(s)
- Yu N Belenkov
- I.M. Sechenov First Moscow Medical University (Sechenov University), Moscow
| | - M V Kozhevnikova
- I.M. Sechenov First Moscow Medical University (Sechenov University), Moscow
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37
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Mobile Single-Lead Electrocardiogram Technology for Atrial Fibrillation Detection in Acute Ischemic Stroke Patients. J Clin Med 2022; 11:jcm11030665. [PMID: 35160117 PMCID: PMC8836576 DOI: 10.3390/jcm11030665] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/13/2022] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
(1) Background: AliveCor KardiaMobile (KM) is a portable electrocardiography recorder for detection of atrial fibrillation (AF). The aim of the study was to define the group of acute ischemic stroke (AIS) patients who can use the KM device and assess the diagnostic test accuracy. (2) Methods: the AIS patients were recruited to the study. Thirty-second single-lead electrocardiogram (ECG) usages were recorded on demand for three days using KM portable device. Each KM ECG record was verified by a cardiologist. The feasibility was evaluated using operationalization criteria. (3) Results: the recruitment rate among AIS patients was 26.3%. The withdrawal rate before the start of the intervention was 26%. The withdrawal rate after the start of the intervention was 6%. KM device detected AF in 2.8% of AIS patients and in 2.2% of ECG records. Cardiologist confirmed the AF in 0.3% AIS patients. Sensitivity and specificity of KM for AF was 100% and 98.3%, respectively. (4) Conclusions: the results of this study suggest that it is feasible to use KM device to detect AF in the selected AIS patients (younger and in better neurological condition). KM detected AF in the selected AIS patients with high specificity and sensitivity.
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Elbey MA, Young D, Kanuri SH, Akella K, Murtaza G, Garg J, Atkins D, Bommana S, Sharma S, Turagam M, Pillarisetti J, Park P, Tummala R, Shah A, Koerber S, Shivamurthy P, Vasamreddy C, Gopinathannair R, Lakkireddy D. Diagnostic Utility of Smartwatch Technology for Atrial Fibrillation Detection - A Systematic Analysis. J Atr Fibrillation 2021; 13:20200446. [PMID: 34950348 DOI: 10.4022/jafib.20200446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 05/26/2020] [Accepted: 07/01/2020] [Indexed: 11/10/2022]
Abstract
Background Smartphone technologies have been recently developed to assess heart rate and rhythm, but their role in accurately detecting atrial fibrillation (AF) remains unknown. Objective We sought to perform a meta-analysis using prospective studies comparing Smartwatch technology with current monitoring standards for AF detection (ECG, Holter, Patch Monitor, ILR). Methods We performed a comprehensive literature search for prospective studies comparing Smartwatch technology simultaneously with current monitoring standards (ECG, Holter, and Patch monitor) for AF detection since inception to November 25th, 2019. The outcome studied was the accuracy of AF detection. Accuracy was determined with concomitant usage of ECG monitoring, Holter monitoring, loop recorder, or patch monitoring. Results A total of 9 observational studies were included comparing smartwatch technology, 3 using single-lead ECG monitoring, and six studies using photoplethysmography with routine AF monitoring strategies. A total of 1559 patients were enrolled (mean age 63.5 years, 39.5% had an AF history). The mean monitoring time was 75.6 days. Smartwatch was non-inferior to composite ECG monitoring strategies (OR 1.06, 95% CI 0.93 - 1.21, p=0.37), composite 12 lead ECG/Holter monitoring (OR 0.90, 95% CI 0.62 - 1.30, p=0.57) and patch monitoring (OR 1.28, 95% CI 0.84 - 1.94, p=0.24) for AF detection. The sensitivity and specificity for AF detection using a smartwatch was 95% and 94%, respectively. Conclusions Smartwatch based single-lead ECG and photoplethysmography appear to be reasonable alternatives for AF monitoring.
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Affiliation(s)
- Mehmet Ali Elbey
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Daisy Young
- Department of Internal Medicine, Stony Brook Southampton Hospital, Southampton, NY
| | - Sri Harsha Kanuri
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Krishna Akella
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Ghulam Murtaza
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Jalaj Garg
- Division of Cardiology, Cardiac Arrhythmia Service, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Donita Atkins
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Sudha Bommana
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Sharan Sharma
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Mohit Turagam
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Peter Park
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Rangarao Tummala
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Alap Shah
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Scott Koerber
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Poojita Shivamurthy
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Chandrasekhar Vasamreddy
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Rakesh Gopinathannair
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Dhanunjaya Lakkireddy
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
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Abstract
Atrial fibrillation (AF) is one of the main cardiac arrhythmias associated with higher risk of cardiovascular morbidity and mortality. AF can cause adverse symptoms and reduced quality of life. One of the strategies for the management of AF is rate control, which can modulate ventricle rate, alleviate adverse associated symptoms and improve the quality of life. As primary management of AF through rate control or rhythm is a topic under debate, the purpose of this review is to explore the rationale for the rate control approach in managing AF by considering the guidelines, recommendations and determinants for the choice of rate control drugs, including beta blockers, digoxin and non- dihydropyridine calcium channel blockers for patients with AF and other comorbidities and atrioventricular nodal ablation and pacing. Despite the limitations of rate control treatment, which may not be effective in preventing disease progression or in reducing symptoms in highly symptomatic patients, it is widely used for almost all patients with atrial fibrillation. Although rate control is one of the first line management of all patient with atrial fibrillation, several issues remain debateable.
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Affiliation(s)
- Muath Alobaida
- Department of Basic Sciences, Prince Sultan bin Abdulaziz College for Emergency Medical Services, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Abdullah Alrumayh
- Department of Basic Sciences, Prince Sultan bin Abdulaziz College for Emergency Medical Services, King Saud University, Riyadh, Kingdom of Saudi Arabia
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Santala OE, Halonen J, Martikainen S, Jäntti H, Rissanen TT, Tarvainen MP, Laitinen TP, Laitinen TM, Väliaho ES, Hartikainen JEK, Martikainen TJ, Lipponen JA. Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study. JMIR Mhealth Uhealth 2021; 9:e29933. [PMID: 34677135 PMCID: PMC8571685 DOI: 10.2196/29933] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/30/2021] [Accepted: 08/27/2021] [Indexed: 01/19/2023] Open
Abstract
Background Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. Objective We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. Methods Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). Results The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). Conclusions A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. Trial Registration ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335
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Affiliation(s)
- Onni E Santala
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jari Halonen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Heart Center, Kuopio University Hospital, Kuopio, Finland
| | - Susanna Martikainen
- Department of Health and Social Management, University of Eastern Finland, Kuopio, Finland
| | - Helena Jäntti
- Center for Prehospital Emergency Care, Kuopio University Hospital, Kuopio, Finland
| | | | - Mika P Tarvainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Tomi P Laitinen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Tiina M Laitinen
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Eemu-Samuli Väliaho
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Juha E K Hartikainen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Heart Center, Kuopio University Hospital, Kuopio, Finland
| | - Tero J Martikainen
- Department of Emergency Care, Kuopio University Hospital, Kuopio, Finland
| | - Jukka A Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
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41
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Mobile health solutions for atrial fibrillation detection and management: a systematic review. Clin Res Cardiol 2021; 111:479-491. [PMID: 34549333 PMCID: PMC8454991 DOI: 10.1007/s00392-021-01941-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/07/2021] [Indexed: 01/28/2023]
Abstract
Aim We aimed to systematically review the available literature on mobile Health (mHealth) solutions, including handheld and wearable devices, implantable loop recorders (ILRs), as well as mobile platforms and support systems in atrial fibrillation (AF) detection and management. Methods This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The electronic databases PubMed (NCBI), Embase (Ovid), and Cochrane were searched for articles published until 10 February 2021, inclusive. Given that the included studies varied widely in their design, interventions, comparators, and outcomes, no synthesis was undertaken, and we undertook a narrative review. Results We found 208 studies, which were deemed potentially relevant. Of these studies included, 82, 46, and 49 studies aimed at validating handheld devices, wearables, and ILRs for AF detection and/or management, respectively, while 34 studies assessed mobile platforms/support systems. The diagnostic accuracy of mHealth solutions differs with respect to the type (handheld devices vs wearables vs ILRs) and technology used (electrocardiography vs photoplethysmography), as well as application setting (intermittent vs continuous, spot vs longitudinal assessment), and study population. Conclusion While the use of mHealth solutions in the detection and management of AF is becoming increasingly popular, its clinical implications merit further investigation and several barriers to widespread mHealth adaption in healthcare systems need to be overcome. Graphic abstract Mobile health solutions for atrial fibrillation detection and management: a systematic review. ![]()
Supplementary Information The online version contains supplementary material available at 10.1007/s00392-021-01941-9.
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van der Velden RMJ, Verhaert DVM, Hermans ANL, Duncker D, Manninger M, Betz K, Gawalko M, Desteghe L, Pisters R, Hemels M, Pison L, Sohaib A, Sultan A, Steven D, Wijtvliet P, Gupta D, Svennberg E, Luermans JCLM, Chaldoupi M, Vernooy K, den Uijl D, Lodzinski P, Jansen WPJ, Eckstein J, Bollmann A, Vandervoort P, Crijns HJGM, Tieleman R, Heidbuchel H, Pluymaekers NAHA, Hendriks JM, Linz D, TeleCheck-AF Investigators. The photoplethysmography dictionary: practical guidance on signal interpretation and clinical scenarios from TeleCheck-AF. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:363-373. [PMID: 36713592 PMCID: PMC9707923 DOI: 10.1093/ehjdh/ztab050] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/26/2021] [Accepted: 06/03/2021] [Indexed: 02/01/2023]
Abstract
Aims Within the TeleCheck-AF project, numerous centres in Europe used on-demand photoplethysmography (PPG) technology to remotely assess heart rate and rhythm in conjunction with teleconsultations. Based on the TeleCheck-AF investigator experiences, we aimed to develop an educational structured stepwise practical guide on how to interpret PPG signals and to introduce typical clinical scenarios how on-demand PPG was used. Methods and results During an online conference, the structured stepwise practical guide on how to interpret PPG signals was discussed and further refined during an internal review process. We provide the number of respective PPG recordings (FibriCheck®) and number of patients managed within a clinical scenario during the TeleCheck-AF project. To interpret PPG recordings, we introduce a structured stepwise practical guide and provide representative PPG recordings. In the TeleCheck-AF project, 2522 subjects collected 90 616 recordings in total. The majority of these recordings were classified by the PPG algorithm as sinus rhythm (57.6%), followed by AF (23.6%). In 9.7% of recordings, the quality was too low to interpret. The most frequent clinical scenarios where PPG technology was used in the TeleCheck-AF project was a follow-up after AF ablation (1110 patients) followed by heart rate and rhythm assessment around (tele)consultation (966 patients). Conclusion We introduce a newly developed structured stepwise practical guide on PPG signal interpretation developed based on presented experiences from TeleCheck-AF. The present clinical scenarios for the use of on-demand PPG technology derived from the TeleCheck-AF project will help to implement PPG technology in the management of AF patients.
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Affiliation(s)
- Rachel M J van der Velden
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Dominique V M Verhaert
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Astrid N L Hermans
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - David Duncker
- Department of Cardiology and Angiology, Hannover Heart Rhythm Center, Hannover Medical School, Hannover, Germany
| | - Martin Manninger
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Konstanze Betz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Monika Gawalko
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- 1st Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Lien Desteghe
- Heart Center Hasselt, Jessa Hospital, Hasselt, Belgium
- Department of Cardiology, Antwerp University Hospital and Antwerp University, Antwerp, Belgium
| | - Ron Pisters
- Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Martin Hemels
- Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Laurent Pison
- Department of Cardiology, Hospital East Limburg, Genk, Belgium
| | - Afzal Sohaib
- Department of Cardiology, St Bartholomew’s Hospital, Bart’s Health NHS Trust, London, UK
- Department of Cardiology, King George Hospital, London, UK
| | - Arian Sultan
- Department of Electrophysiology, Heart Center, University Hospital Cologne, Cologne, Germany
| | - Daniel Steven
- Department of Electrophysiology, Heart Center, University Hospital Cologne, Cologne, Germany
| | - Petra Wijtvliet
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Cardiology, Martini Ziekenhuis, Groningen, The Netherlands
| | - Dhiraj Gupta
- Department of Cardiology, Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Emma Svennberg
- Division of Cardiovascular Medicine, Department of Clinical Sciences, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Justin C L M Luermans
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Marisevi Chaldoupi
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Dennis den Uijl
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Piotr Lodzinski
- 1st Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Ward P J Jansen
- Department of Cardiology, Tergooi Hospital, Hilversum, the Netherlands
| | - Jens Eckstein
- Department of Internal Medicine, University Hospital Basel, Basel, Switzerland
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | | | - Harry J G M Crijns
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Robert Tieleman
- Department of Cardiology, Martini Ziekenhuis, Groningen, The Netherlands
| | - Hein Heidbuchel
- Department of Cardiology, Antwerp University Hospital and Antwerp University, Antwerp, Belgium
| | - Nikki A H A Pluymaekers
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
| | - Jeroen M Hendriks
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia
- Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia
- Faculty of Health and Medical Sciences, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
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Betz K, van der Velden R, Gawalko M, Hermans A, Pluymaekers N, Hillmann HAK, Hendriks J, Duncker D, Linz D. [Interpretation of photoplethysmography: a step-by-step guide]. Herzschrittmacherther Elektrophysiol 2021; 32:406-411. [PMID: 34304276 PMCID: PMC8310409 DOI: 10.1007/s00399-021-00795-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 06/30/2021] [Indexed: 11/05/2022]
Abstract
By applying photoplethysmography (PPG), the camera of the mobile phone can be used to remotely assess heart rate and rhythm, which was widely used in conjunction with teleconsultations within the TeleCheck-AF project during the coronavirus disease 2019 (COVID-19) pandemic. Herein, we provide an educational, structured, stepwise practical guide on how to interpret PPG signals. A better understanding of PPG recordings is critical for the implementation of this widely available technology into clinical practice.
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Affiliation(s)
- Konstanze Betz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht UMC+, 6202 AZ, Maastricht, Niederlande
| | - Rachel van der Velden
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht UMC+, 6202 AZ, Maastricht, Niederlande
| | - Monika Gawalko
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht UMC+, 6202 AZ, Maastricht, Niederlande
| | - Astrid Hermans
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht UMC+, 6202 AZ, Maastricht, Niederlande
| | - Nikki Pluymaekers
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht UMC+, 6202 AZ, Maastricht, Niederlande
| | - Henrike A K Hillmann
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Deutschland
| | - Jeroen Hendriks
- Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, Australien
- Department of Cardiology, Radboud University Medical Centre, Nijmegen, Niederlande
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Deutschland
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Centre and Cardiovascular Research Institute Maastricht, Maastricht UMC+, 6202 AZ, Maastricht, Niederlande.
- Department of Cardiology, Radboud University Medical Centre, Nijmegen, Niederlande.
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australien.
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Kopenhagen, Dänemark.
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Książczyk M, Dębska-Kozłowska A, Warchoł I, Lubiński A. Enhancing Healthcare Access-Smartphone Apps in Arrhythmia Screening: Viewpoint. JMIR Mhealth Uhealth 2021; 9:e23425. [PMID: 34448723 PMCID: PMC8433858 DOI: 10.2196/23425] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/04/2021] [Accepted: 07/28/2021] [Indexed: 01/23/2023] Open
Abstract
Atrial fibrillation is the most commonly reported arrhythmia and, if undiagnosed or untreated, may lead to thromboembolic events. It is therefore desirable to provide screening to patients in order to detect atrial arrhythmias. Specific mobile apps and accessory devices, such as smartphones and smartwatches, may play a significant role in monitoring heart rhythm in populations at high risk of arrhythmia. These apps are becoming increasingly common among patients and professionals as a part of mobile health. The rapid development of mobile health solutions may revolutionize approaches to arrhythmia screening. In this viewpoint paper, we assess the availability of smartphone and smartwatch apps and evaluate their efficacy for monitoring heart rhythm and arrhythmia detection. The findings obtained so far suggest they are on the right track to improving the efficacy of early detection of atrial fibrillation, thus lowering the risk of stroke and reducing the economic burden placed on public health.
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Affiliation(s)
- Marcin Książczyk
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland.,Department of Noninvasive Cardiology, Medical University of Lodz, Łódź, Poland
| | - Agnieszka Dębska-Kozłowska
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland
| | - Izabela Warchoł
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland
| | - Andrzej Lubiński
- Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Łódź, Poland
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Zaprutko T, Zaprutko J, Sprawka J, Pogodzińska M, Michalak M, Paczkowska A, Kus K, Nowakowska E, Baszko A. The comparison of Kardia Mobile and Hartmann Veroval 2 in 1 in detecting first diagnosed atrial fibrillation. Cardiol J 2021; 30:762-770. [PMID: 34355779 PMCID: PMC10635734 DOI: 10.5603/cj.a2021.0083] [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: 03/15/2021] [Revised: 07/08/2021] [Accepted: 07/08/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is the leading cause of stroke. The European Society of Cardiology (ESC) advises opportunistic AF screening among patients aged ≥ 65 years. Considering this, the aim herein, was compare the feasibility of two different systems of smartphone-based electrocardiogram (ECG) recordings to identify AF among those without a previous arrhythmia history. METHODS Prospective AF screening was conducted at six pharmacies using Kardia Mobile and Hartmann Veroval 2 in 1. A single-lead ECG was acquired by the placement of fingers on the pads. A cardiologist evaluated findings from both devices. RESULTS Atrial fibrillation was identified in 3.60% and previously unknown AF was detected in 1.92% of the study participants. Sensitivity and specificity of the Kardia application in detecting AF were 66.7% (95% confidence interval [CI] 38.4-88.2%) and 98.5% (95% CI 96.7-99.5%), and for Veroval 10.0% (95% CI 0.23-44.5%) and 94.96% (95% CI 92.15-96.98%), accordingly. Inter-rater agreement was k = 0.088 (95% CI 1.59-16.1%). CONCLUSIONS Mobile devices can detect AF, but each finding must be verified by a professional. The Kardia application appeared to be more user-friendly than Veroval. Cardiovascular screening using mobile devices is feasible at pharmacies. Hence it might be considered for routine use.
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Affiliation(s)
- Tomasz Zaprutko
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, Poznan, Poland.
| | - Joanna Zaprutko
- Second Department of Cardiology, Poznan University of Medical Sciences, HCP Medical Center, Poznan, Poland
| | - Józefina Sprawka
- Student Scientific Society, Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, Poznan, Poland
| | - Monika Pogodzińska
- Student Scientific Society, Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, Poznan, Poland
| | - Michał Michalak
- Department of Computer Sciences and Statistics, Poznan University of Medical Sciences, Poznan, Poland
| | - Anna Paczkowska
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, Poznan, Poland
| | - Krzysztof Kus
- Second Department of Cardiology, Poznan University of Medical Sciences, HCP Medical Center, Poznan, Poland
| | - Elżbieta Nowakowska
- Department of Pharmacology and Toxicology, University of Zielona Gora, Poland
| | - Artur Baszko
- Second Department of Cardiology, Poznan University of Medical Sciences, HCP Medical Center, Poznan, Poland
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46
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Zaprutko T, Florczak-Wyspiańska J, Kopciuch D, Paczkowska A, Ratajczak P, Dorszewska J, Nowakowska E, Kus K. Costs of Stroke and Incidence of First Diagnosis of Atrial Fibrillation at Time of Stroke. Neurology Ward Hospital Poznań, Poland 2018. Healthcare (Basel) 2021; 9:healthcare9080999. [PMID: 34442136 PMCID: PMC8394020 DOI: 10.3390/healthcare9080999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/29/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022] Open
Abstract
Stroke is a major cause of morbidity in industrialized countries, representing 8% of total deaths across Europe in 2017. It is also a very costly disorder, frequently caused by atrial fibrillation. We aimed to calculate the cost of stroke hospitalization in 2018 in Poznań (Poland). We also intended to present patients with the first AF diagnosis at the time of stroke. The study was conducted from January 2019 to July 2020. Data were obtained from hospital records and from the hospital accounting department. Out of 164 patients included in the study, 41 had AF and in 18 cases AF was first diagnosed at the time of stroke. The cost of hospitalization in Poznań was EUR 139,257.21 (x¯= EUR 849.13). Among those with concomitant AF, the general cost of inpatient care was EUR 33,859.18 (x¯= EUR 825.83). Considering those who had AF first diagnosed during hospitalization the cost was EUR 16,248.97 (x¯= EUR 906.24). Stroke is associated with high costs of inpatient care, which turned out to be higher among those with AF first diagnosed at the time of stroke. The number of patients who used oral anticoagulants at the time of admission was relatively low. The most frequently used NOAC was dabigatran.
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Affiliation(s)
- Tomasz Zaprutko
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, 7 Rokietnicka St, 60-806 Poznan, Poland; (D.K.); (A.P.); (P.R.); (K.K.)
- Correspondence: ; Tel./Fax: +48-61-845-26-84
| | - Jolanta Florczak-Wyspiańska
- Department of Neurology, Poznan University of Medical Sciences, 49 Przybyszewskiego St, 60-355 Poznan, Poland; (J.F.-W.); (J.D.)
| | - Dorota Kopciuch
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, 7 Rokietnicka St, 60-806 Poznan, Poland; (D.K.); (A.P.); (P.R.); (K.K.)
| | - Anna Paczkowska
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, 7 Rokietnicka St, 60-806 Poznan, Poland; (D.K.); (A.P.); (P.R.); (K.K.)
| | - Piotr Ratajczak
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, 7 Rokietnicka St, 60-806 Poznan, Poland; (D.K.); (A.P.); (P.R.); (K.K.)
| | - Jolanta Dorszewska
- Department of Neurology, Poznan University of Medical Sciences, 49 Przybyszewskiego St, 60-355 Poznan, Poland; (J.F.-W.); (J.D.)
| | - Elżbieta Nowakowska
- Department of Toxicology and Pharmacology, University of Zielona Góra, 28 Zyty St, 65-046 Zielona Góra, Poland;
| | - Krzysztof Kus
- Department of Pharmacoeconomics and Social Pharmacy, Poznan University of Medical Sciences, 7 Rokietnicka St, 60-806 Poznan, Poland; (D.K.); (A.P.); (P.R.); (K.K.)
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47
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Lopez Perales CR, Van Spall HGC, Maeda S, Jimenez A, Laţcu DG, Milman A, Kirakoya-Samadoulougou F, Mamas MA, Muser D, Casado Arroyo R. Mobile health applications for the detection of atrial fibrillation: a systematic review. Europace 2021; 23:11-28. [PMID: 33043358 DOI: 10.1093/europace/euaa139] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Indexed: 12/21/2022] Open
Abstract
AIMS Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF. METHODS AND RESULTS We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients. CONCLUSION Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
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Affiliation(s)
- Carlos Ruben Lopez Perales
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium.,Servicio de Cardiología, Hospital Universitario Miguel Servet, Isabel La Catolica 1-3, Zaragoza 50009, Spain
| | - Harriette G C Van Spall
- Division of Cardiology, Department of Medicine, Population Health Research Institute, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, Canada
| | - Shingo Maeda
- Advanced Arrhythmia Research, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, 113-8519 Tokyo, Japan
| | - Alejandro Jimenez
- Division of Cardiology, University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201, USA
| | - Decebal Gabriel Laţcu
- Department of Cardiology, Centre Hospitalier Princesse Grace, Avenue Pasteur, 98000, Monaco, Monaco (Principalty)
| | - Anat Milman
- Department of Cardiology, Leviev Heart Institute, The Chaim Sheba Medical Center, Tel Hashomer, and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Fati Kirakoya-Samadoulougou
- Centre de Recherche en Epidémiologie, Biostatistiques et Recherche Clinique, Ecole de Santé Publique, Université librede Bruxelles, Avenue Franklin Roosevelt 50 - 1050, Brussels, Belgium
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Keele University, Stoke-on-Trent, Keele, Newcastle ST5 5BG, UK.,Royal Stoke University Hospital, Newcastle Rd, Stoke-on-Trent ST4 6QG, UK
| | - Daniele Muser
- Section of Cardiac Electrophysiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Ruben Casado Arroyo
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
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48
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Gawałko M, Duncker D, Manninger M, van der Velden RMJ, Hermans ANL, Verhaert DVM, Pison L, Pisters R, Hemels M, Sultan A, Steven D, Gupta D, Heidbuchel H, Sohaib A, Wijtvliet P, Tieleman R, Gruwez H, Chun J, Schmidt B, Keaney JJ, Müller P, Lodziński P, Svennberg E, Hoekstra O, Jansen WPJ, Desteghe L, de Potter T, Tomlinson DR, Neubeck L, Crijns HJGM, Pluymaekers NAHA, Hendriks JM, Linz D. The European TeleCheck-AF project on remote app-based management of atrial fibrillation during the COVID-19 pandemic: centre and patient experiences. Europace 2021; 23:1003-1015. [PMID: 33822029 PMCID: PMC8083545 DOI: 10.1093/europace/euab050] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/19/2021] [Indexed: 01/10/2023] Open
Abstract
Aims TeleCheck-AF is a multicentre international project initiated to maintain care delivery for patients with atrial fibrillation (AF) during COVID-19 through teleconsultations supported by an on-demand photoplethysmography-based heart rate and rhythm monitoring app (FibriCheck®). We describe the characteristics, inclusion rates, and experiences from participating centres according the TeleCheck-AF infrastructure as well as characteristics and experiences from recruited patients. Methods and results Three surveys exploring centre characteristics (n = 25), centre experiences (n = 23), and patient experiences (n = 826) were completed. Self-reported patient characteristics were obtained from the app. Most centres were academic (64%) and specialized public cardiology/district hospitals (36%). Majority of the centres had AF outpatient clinics (64%) and only 36% had AF ablation clinics. The time required to start patient inclusion and total number of included patients in the project was comparable for centres experienced (56%) or inexperienced in mHealth use. Within 28 weeks, 1930 AF patients were recruited, mainly for remote AF control (31% of patients) and AF ablation follow-up (42%). Average inclusion rate was highest during the lockdown restrictions and reached a steady state at a lower level after easing the restrictions (188 vs. 52 weekly recruited patients). Majority (>80%) of the centres reported no problems during the implementation of the TeleCheck-AF approach. Recruited patients [median age 64 (55–71), 62% male] agreed that the FibriCheck® app was easy to use (94%). Conclusion Despite different health care settings and mobile health experiences, the TeleCheck-AF approach could be set up within an extremely short time and easily used in different European centres during COVID-19.
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Affiliation(s)
- Monika Gawałko
- Department of Cardiology, Maastricht University Medical Centre, Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands.,1st Department of Cardiology, Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - David Duncker
- Department of Cardiology and Angiology, Hannover Heart Rhythm Center, Hannover Medical School, Hannover, Germany
| | - Martin Manninger
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Rachel M J van der Velden
- Department of Cardiology, Maastricht University Medical Centre, Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands
| | - Astrid N L Hermans
- Department of Cardiology, Maastricht University Medical Centre, Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands
| | | | | | - Ron Pisters
- Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Martin Hemels
- Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Arian Sultan
- Department of Electrophysiology, University of Cologne, Heart Centre, Cologne, Germany
| | - Daniel Steven
- Department of Electrophysiology, University of Cologne, Heart Centre, Cologne, Germany
| | - Dhiraj Gupta
- Department of Cardiology, Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Hein Heidbuchel
- Antwerp University and University Hospital, Antwerp, Belgium
| | | | - Petra Wijtvliet
- Department of Cardiology, Martini Hospital, Groningen, The Netherlands
| | - Robert Tieleman
- Department of Cardiology, Martini Hospital, Groningen, The Netherlands
| | | | - Julian Chun
- Cardioangiologisches Centrum Bethanien, Frankfurt, Germany
| | - Boris Schmidt
- Cardioangiologisches Centrum Bethanien, Frankfurt, Germany
| | - John J Keaney
- Mater Misericordiae University Hospital, Dublin, Ireland
| | - Patrick Müller
- Department of Cardiology II-Electrophysiology, University Hospital of Münster, Münster, Germany
| | - Piotr Lodziński
- 1st Department of Cardiology, Doctoral School, Medical University of Warsaw, Warsaw, Poland
| | - Emma Svennberg
- Department of Clinical Sciences, Karolinska Institutet, Danderyd's University Hospital, Stockholm, Sweden.,Deptartment of Cardiology, Karolinska University Hospital, Stockholm, Sweden
| | | | | | - Lien Desteghe
- Antwerp University Hospital and Antwerp University, Antwerp, Belgium.,Hasselt University and Jessa Hospital, Hasselt, Belgium
| | - Tom de Potter
- Cardiovascular Center, Onze Lieve Vrouwziekenhuis, Aalst, Belgium
| | | | - Lis Neubeck
- School of Health and Social Care, Edinburgh Napier University, Edinburgh, UK
| | - Harry J G M Crijns
- Department of Cardiology, Maastricht University Medical Centre, Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands
| | - Nikki A H A Pluymaekers
- Department of Cardiology, Maastricht University Medical Centre, Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands
| | - Jeroen M Hendriks
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia.,Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Centre, Cardiovascular Research Institute Maastricht, 6202 AZ Maastricht, The Netherlands.,Department of Cardiology, Radboud University Medical Centre, Nijmegen, The Netherlands.,Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia.,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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49
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Masterson Creber R, Turchioe MR. Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges. Card Electrophysiol Clin 2021; 13:555-567. [PMID: 34330381 PMCID: PMC8328196 DOI: 10.1016/j.ccep.2021.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.
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Affiliation(s)
- Ruth Masterson Creber
- Division of Health Informatics, Weill Cornell Medicine, 425 E 61st St, Floor 3, New York, NY 10065, USA.
| | - Meghan Reading Turchioe
- Division of Health Informatics, Weill Cornell Medicine, 425 E 61st St, Floor 3, New York, NY 10065, USA
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50
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Ouyang V, Ma B, Pignatelli N, Sengupta S, Sengupta P, Mungulmare K, Fletcher RR. The use of multi-site photoplethysmography (PPG) as a screening tool for coronary arterial disease and atherosclerosis. Physiol Meas 2021; 42. [PMID: 32764197 DOI: 10.1088/1361-6579/abad48] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 08/07/2020] [Indexed: 11/11/2022]
Abstract
Objective. We present the design and validation of a non-invasive smart-phone based screening tool for atherosclerosis and coronary arterial disease (CAD), which is the leading cause of mortality worldwide.Approach. We designed a three-channel photoplethysmography (PPG) device that connects to a smart phone application for measuring pulse transit time (PTT) and pulse wave velocity (PWV) using PPG probes that are simultaneously clipped onto to the ear, index finger, and big toe, respectively. Validation was performed through a clinical study with 100 participants (age 20 to 77) at a research hospital in Nagpur, India. Study subjects were stratified by age and divided into three groups corresponding to the disease severity: CAD, hypertensive ('Pre-CAD'), and Healthy.Main results. PWV measurements derived from the Ear-Toe probe measurements yielded the best performance, with median PWV values increasing monotonically as a function of disease severity and age, as follows: 14.2 m s-1for the older-patient CAD group, 12.2 m s-1for the younger-patient CAD group, 11.6 m s-1for the older-patient Pre-CAD group, 10.2 m s-1for the younger-patient Pre-CAD group, 9.7 m s-1for the older healthy controls, and 8.4 m s-1for the younger healthy controls. Using just two simple features, the PTT and patient height, we demonstrate a machine learning prediction model for CAD with a median accuracy of 0.83 (AUC).Significance. This work demonstrates the ability to predict atherosclerosis and CAD using a single simple physiological measurement with a multi-site PPG tool that is electrically powered by a mobile phone and does not require any electrocardiogram reference. Furthermore, this method only requires a single anthropometric measurement, which is the patient's height.
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Affiliation(s)
- Victoria Ouyang
- Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Botong Ma
- Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Niccolo Pignatelli
- Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | | | | | | | - Richard Ribon Fletcher
- Massachusetts Institute of Technology, Cambridge, MA, United States of America.,University of Massachusetts Medical School, Worcester, MA, United States of America
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