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Kanegae H, Fujishiro K, Fukatani K, Ito T, Kario K. Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques. J Clin Hypertens (Greenwich) 2024. [PMID: 38850282 DOI: 10.1111/jch.14848] [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: 02/05/2024] [Revised: 05/01/2024] [Accepted: 05/14/2024] [Indexed: 06/10/2024]
Abstract
Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset AF that incorporated the use electrocardiogram to diagnose AF, data from participants with a wide age range, and considered hypertension and measures of atrial stiffness. In Japan, Industrial Safety and Health Law requires employers to provide annual health check-ups to their employees. This study included 13 410 individuals who underwent health check-ups on at least four successive years between 2005 and 2015 (new-onset AF, n = 110; non-AF, n = 13 300). Data were entered into a risk prediction model using machine learning methods (eXtreme Gradient Boosting and Shapley Additive Explanation values). Data were randomly split into a training set (80%) used for model construction and development, and a test set (20%) used to test performance of the derived model. The area under the receiver operator characteristic curve for the model in the test set was 0.789. The best predictor of new-onset AF was age, followed by the cardio-ankle vascular index, estimated glomerular filtration rate, sex, body mass index, uric acid, γ-glutamyl transpeptidase level, triglycerides, systolic blood pressure at cardio-ankle vascular index measurement, and alanine aminotransferase level. This new model including arterial stiffness measure, developed with data from a general population using machine learning methods, could be used to identify at-risk individuals and potentially facilitation the prevention of future AF development.
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Affiliation(s)
- Hiroshi Kanegae
- Department of Medicine, Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
- Genki Plaza Medical Center for Health Care, Tokyo, Japan
| | - Kentaro Fujishiro
- Research and Development Division, Japan Health Promotion Foundation, Tokyo, Japan
| | | | | | - Kazuomi Kario
- Department of Medicine, Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
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Steinfeldt J, Wild B, Buergel T, Pietzner M, Upmeier Zu Belzen J, Vauvelle A, Hegselmann S, Denaxas S, Hemingway H, Langenberg C, Landmesser U, Deanfield J, Eils R. Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats. Nat Commun 2024; 15:4257. [PMID: 38763986 PMCID: PMC11102902 DOI: 10.1038/s41467-024-48568-8] [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: 11/17/2023] [Accepted: 05/03/2024] [Indexed: 05/21/2024] Open
Abstract
The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.
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Affiliation(s)
- Jakob Steinfeldt
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Benjamin Wild
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Thore Buergel
- Institute of Cardiovascular Sciences, University College London, London, UK
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Maik Pietzner
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK
| | - Julius Upmeier Zu Belzen
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
| | - Andre Vauvelle
- Institute of Health Informatics, University College London, London, UK
| | - Stefan Hegselmann
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Massachusetts, USA
- Pattern Recognition and Image Analysis Lab, University of Münster, Münster, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK
| | - Ulf Landmesser
- Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany
- Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany
- Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Berlin, Germany
| | - John Deanfield
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany.
- Health Data Science Unit, Heidelberg University Hospital and BioQuant, Heidelberg, Germany.
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3
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Kell DB, Lip GYH, Pretorius E. Fibrinaloid Microclots and Atrial Fibrillation. Biomedicines 2024; 12:891. [PMID: 38672245 PMCID: PMC11048249 DOI: 10.3390/biomedicines12040891] [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: 03/08/2024] [Revised: 03/27/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Søltofts Plads, Building 220, 2800 Kongens Lyngby, Denmark
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Private Bag X1 Matieland, Stellenbosch 7602, South Africa
| | - Gregory Y. H. Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool L7 8TX, UK;
- Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, 9220 Aalborg, Denmark
| | - Etheresia Pretorius
- Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Private Bag X1 Matieland, Stellenbosch 7602, South Africa
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Kawamura Y, Vafaei Sadr A, Abedi V, Zand R. Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection? J Clin Med 2024; 13:1313. [PMID: 38592138 PMCID: PMC10932407 DOI: 10.3390/jcm13051313] [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: 01/16/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.
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Affiliation(s)
- Yuki Kawamura
- School of Clinical Medicine, University of Cambridge, Cambridge CB3 0SP, UK
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
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Li Y, Ma J, Xiao J, Wang Y, He W. Use of extreme gradient boosting, light gradient boosting machine, and deep neural networks to evaluate the activity stage of extraocular muscles in thyroid-associated ophthalmopathy. Graefes Arch Clin Exp Ophthalmol 2024; 262:203-210. [PMID: 37773288 DOI: 10.1007/s00417-023-06256-1] [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: 06/01/2023] [Revised: 09/14/2023] [Accepted: 09/21/2023] [Indexed: 10/01/2023] Open
Abstract
PURPOSE To develop a machine learning model to evaluate the activity stage of extraocular muscles in thyroid-associated ophthalmopathy (TAO). METHODS This study retrospectively analysed data from patients with TAO who underwent contrast-enhanced magnetic resonance imaging (MRI) from 2015 to 2022. Three independent machine learning models, namely, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and deep neural networks (DNNs), were constructed using common clinical features. The performance of these models was compared using evaluation metrics such as the area under the receiver operating curve (AUC), accuracy, precision, recall, and F1 score. The importance of features was explained using Shapley additive explanations (SHAP). RESULTS A total of 2561 eyes of 1479 TAO patients were included in this study. The original dataset was randomly divided into a training set (80%, n = 2048) and a test set (20%, n = 513). In the performance evaluation of the test set, the LightGBM model had the best diagnostic performance (AUC 0.9260). According to the SHAP results, features such as conjunctival congestion, swollen caruncles, oedema of the upper eyelid, course of TAO, and intraocular pressure had the most significant impact on the LightGBM model. CONCLUSION This study used contrast-enhanced MRI as an objective evaluation criterion and constructed a LightGBM model based on readily accessible clinical data. The model had good classification performance, making it a promising artificial intelligence (AI)-assisted tool to help community hospitals evaluate the inflammatory activity of extraocular muscles in TAO patients in a timely manner.
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Affiliation(s)
- Yunfei Li
- Department of Ophthalmology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, 610041, Sichuan Province, China
| | - Jingyu Ma
- School of Mathematics and Statistics, Lanzhou University, 222 South Tianshui Rd, Lanzhou, 730000, Gansu Province, China
| | - Jun Xiao
- School of Materials and Energy, Lanzhou University, 222 South Tianshui Rd, Lanzhou, 730000, Gansu Province, China
| | - Yujiao Wang
- Department of Ophthalmology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, 610041, Sichuan Province, China
| | - Weimin He
- Department of Ophthalmology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Wuhou District, Chengdu, 610041, Sichuan Province, China.
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6
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Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
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Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
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7
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Harmon DM, Sehrawat O, Maanja M, Wight J, Noseworthy PA. Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation. Arrhythm Electrophysiol Rev 2023; 12:e12. [PMID: 37427304 PMCID: PMC10326669 DOI: 10.15420/aer.2022.31] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/22/2022] [Indexed: 07/11/2023] Open
Abstract
AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.
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Affiliation(s)
- David M Harmon
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - John Wight
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
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8
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Patel S, Kongnakorn T, Nikolaou A, Javaid Y, Mokgokong R. Cost-effectiveness of targeted screening for non-valvular atrial fibrillation in the United Kingdom in older patients using digital approaches. J Med Econ 2023; 26:326-334. [PMID: 36757910 DOI: 10.1080/13696998.2023.2179210] [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: 02/10/2023]
Abstract
AIM Screening for non-valvular atrial fibrillation (NVAF) is key in identifying patients with undiagnosed disease who may be eligible for anticoagulation therapy. Understanding the economic value of screening is necessary to assess optimal strategies for payers and healthcare systems. We evaluated the cost effectiveness of opportunistic screening with handheld digital devices and pulse palpation, as well as targeted screening predictive algorithms for UK patients ≥75 years of age. METHODS A previously developed Markov cohort model was adapted to evaluate clinical and economic outcomes of opportunistic screening including pulse palpation, Zenicor (extended 14 days), KardiaMobile (extended), and two algorithms compared to no screening. Key model inputs including epidemiology estimates, screening effectiveness, and risks for medical events were derived from the STROKESTOP, ARISTOTLE studies, and published literature, and cost inputs were obtained from a UK national cost database. Health and cost outcomes, annually discounted at 3.5%, were reported for a cohort of 10,000 patients vs. no screening over a time horizon equivalent to a patient's lifetime, Analyses were performed from a UK National Health Services and personal social services perspective. RESULTS Zenicor, pulse palpation, and KardiaMobile were dominant (providing better health outcomes at lower costs) vs. no screening; both algorithms were cost-effective vs. no screening, with incremental cost-effectiveness ratios per quality-adjusted life-year (QALY) of £1,040 and £1,166. Zenicor, pulse palpation, and KardiaMobile remained dominant options vs. no screening in all scenarios explored. Deterministic sensitivity analyses indicated long-term stroke care costs, prevalence of undiagnosed NVAF in patients 75-79 years of age, and clinical efficacy of anticoagulant on stroke prevention were the main drivers of the cost-effectiveness results. CONCLUSIONS Screening for NVAF at ≥75 years of age could result in fewer NVAF-related strokes. NVAF screening is cost-effective and may be cost-saving depending on the program chosen.
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Affiliation(s)
| | | | | | - Yassir Javaid
- Danes Camp Surgery, National Health Service, Northampton, UK
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9
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Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study. Clin Res Cardiol 2022:10.1007/s00392-022-02140-w. [DOI: 10.1007/s00392-022-02140-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
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10
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Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Front Cardiovasc Med 2022; 9:945726. [PMID: 36312266 PMCID: PMC9608631 DOI: 10.3389/fcvm.2022.945726] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.
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Affiliation(s)
- Loucia Karatzia
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Dunja Aksentijevic
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,*Correspondence: Dunja Aksentijevic,
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11
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Nadarajah R, Wu J, Frangi AF, Hogg D, Cowan C, Gale CP. What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key. EUROPEAN HEART JOURNAL - QUALITY OF CARE AND CLINICAL OUTCOMES 2022; 8:391-397. [PMID: 34940849 PMCID: PMC9170568 DOI: 10.1093/ehjqcco/qcab094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 12/10/2021] [Indexed: 11/14/2022]
Abstract
Atrial fibrillation (AF) is increasingly common, though often undiagnosed, leaving many people untreated and at elevated risk of ischaemic stroke. Current European guidelines do not recommend systematic screening for AF, even though a number of studies have shown that periods of serial or continuous rhythm monitoring in older people in the general population increase detection of AF and the prescription of oral anticoagulation. This article discusses the conflicting results of two contemporary landmark trials, STROKESTOP and the LOOP, which provided the first evidence on whether screening for AF confers a benefit for people in terms of clinical outcomes. The benefit and efficiency of systematic screening for AF in the general population could be optimized by targeting screening to only those at higher risk of developing AF. For this purpose, evidence is emerging that prediction models developed using artificial intelligence in routinely collected electronic health records can provide strong discriminative performance for AF and increase detection rates when combined with rhythm monitoring in a clinical study. We consider future directions for investigation in this field and how this could be best aligned to the current evidence base to target screening in people at elevated risk of stroke.
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Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds , 6 Clarendon Way, Leeds LS2 9DA, UK
- Leeds Institute of Data Analytics, University of Leeds , Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust , Leeds, UK
| | - Jianhua Wu
- Leeds Institute of Data Analytics, University of Leeds , Leeds, UK
- School of Dentistry, University of Leeds , Leeds, UK
| | - Alejandro F Frangi
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds , 6 Clarendon Way, Leeds LS2 9DA, UK
- Leeds Institute of Data Analytics, University of Leeds , Leeds, UK
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds , Leeds, UK
- Alan Turing Institute , London, UK
| | - David Hogg
- School of Computing, University of Leeds , Leeds, UK
| | - Campbell Cowan
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust , Leeds, UK
| | - Chris P Gale
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds , 6 Clarendon Way, Leeds LS2 9DA, UK
- Leeds Institute of Data Analytics, University of Leeds , Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust , Leeds, UK
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12
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Sivanandarajah P, Wu H, Bajaj N, Khan S, Ng FS. Is machine learning the future for atrial fibrillation screening? CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:136-145. [PMID: 35720677 PMCID: PMC9204790 DOI: 10.1016/j.cvdhj.2022.04.001] [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] [Indexed: 11/29/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
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Affiliation(s)
- Pavidra Sivanandarajah
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sadia Khan
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
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13
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Hill NR, Groves L, Dickerson C, Ochs A, Pang D, Lawton S, Hurst M, Pollock KG, Sugrue DM, Tsang C, Arden C, Wyn Davies D, Martin AC, Sandler B, Gordon J, Farooqui U, Clifton D, Mallen C, Rogers J, Camm AJ, Cohen AT. Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:195-204. [PMID: 36713002 PMCID: PMC9707963 DOI: 10.1093/ehjdh/ztac009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/24/2022] [Accepted: 03/22/2022] [Indexed: 02/01/2023]
Abstract
Aims The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England. Methods and results Eligible participants (aged ≥30 years without AF diagnosis; n = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as high risk of undiagnosed AF (n = 944), were invited for diagnostic testing (n = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely. The primary endpoint was the proportion of AF, atrial flutter, and fast atrial tachycardia diagnoses during the trial (June 2019-February 2021) in high-risk participants. Atrial fibrillation and related arrhythmias were diagnosed in 5.63% and 4.93% of high-risk participants in intervention and control arms, respectively {odds ratio (OR) [95% confidence interval (CI)]: 1.15 (0.77-1.73), P = 0.486}. Among intervention arm participants who underwent diagnostic testing (28.1%), 9.41% received AF and related arrhythmia diagnoses [vs. 4.93% (control); OR (95% CI): 2.24 (1.31-3.73), P = 0.003]. Conclusion The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.
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Affiliation(s)
- Nathan R Hill
- Bristol Myers Squibb Pharmaceutical Ltd, Uxbridge, UK
| | - Lara Groves
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | | | - Andreas Ochs
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - Dong Pang
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - Sarah Lawton
- School of Medicine, Keele University, Staffordshire, UK
| | - Michael Hurst
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | | | | | - Carmen Tsang
- Health Economics and Outcomes Research Ltd, Cardiff, UK
| | - Chris Arden
- University Hospital Southampton, Southampton, UK
| | | | - Anne Celine Martin
- Université de Paris, INSERM, Innovative Therapies in Haemostasis, F-75006 Paris, France,Service de Cardiologie, AP-HP, Hôpital Européen Georges Pompidou, F-75015 Paris, France
| | | | | | | | - David Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | | | | | - Alan John Camm
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St George’s University of London, London, UK
| | - Alexander T Cohen
- Department of Haematological Medicine, Guys and St Thomas’ NHS Foundation Trust, King's College London, London, UK
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14
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Wang YC, Xu X, Hajra A, Apple S, Kharawala A, Duarte G, Liaqat W, Fu Y, Li W, Chen Y, Faillace RT. Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study. Diagnostics (Basel) 2022; 12:diagnostics12030689. [PMID: 35328243 PMCID: PMC8947563 DOI: 10.3390/diagnostics12030689] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/01/2022] [Accepted: 03/06/2022] [Indexed: 02/04/2023] Open
Abstract
Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.
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Affiliation(s)
- Yu-Chiang Wang
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
- Correspondence:
| | - Xiaobo Xu
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Adrija Hajra
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Samuel Apple
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Amrin Kharawala
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Gustavo Duarte
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Wasla Liaqat
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiwen Fu
- Department of Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051, USA;
| | - Weijia Li
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiyun Chen
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Robert T. Faillace
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
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15
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Szymanski T, Ashton R, Sekelj S, Petrungaro B, Pollock KG, Sandler B, Lister S, Hill NR, Farooqui U. Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients. Europace 2022; 24:1240-1247. [PMID: 35226101 DOI: 10.1093/europace/euac016] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 01/27/2022] [Indexed: 11/13/2022] Open
Abstract
AIMS We investigated whether the use of an atrial fibrillation (AF) risk prediction algorithm could improve AF detection compared with opportunistic screening in primary care and assessed the associated budget impact. METHODS AND RESULTS Eligible patients were registered with a general practice in UK, aged 65 years or older in 2018/19, and had complete data for weight, height, body mass index, and systolic and diastolic blood pressure recorded within 1 year. Three screening scenarios were assessed: (i) opportunistic screening and diagnosis (standard care); (ii) standard care replaced by the use of the algorithm; and (iii) combined use of standard care and the algorithm. The analysis considered a 3-year time horizon, and the budget impact for the National Health Service (NHS) costs alone or with personal social services (PSS) costs. Scenario 1 would identify 79 410 new AF cases (detection gap reduced by 22%). Scenario 2 would identify 70 916 (gap reduced by 19%) and Scenario 3 would identify 99 267 new cases (gap reduction 27%). These rates translate into 2639 strokes being prevented in Scenario 1, 2357 in Scenario 2, and 3299 in Scenario 3. The 3-year NHS budget impact of Scenario 1 would be £45.3 million, £3.6 million (difference ‒92.0%) with Scenario 2, and £46.3 million (difference 2.2%) in Scenario 3, but for NHS plus PSS would be ‒£48.8 million, ‒£80.4 million (64.8%), and ‒£71.3 million (46.1%), respectively. CONCLUSION Implementation of an AF risk prediction algorithm alongside standard opportunistic screening could close the AF detection gap and prevent strokes while substantially reducing NHS and PSS combined care costs.
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Affiliation(s)
| | - Rachel Ashton
- Imperial College Health Partners, London NW1 2FB, UK
| | - Sara Sekelj
- Imperial College Health Partners, London NW1 2FB, UK.,UCLPartners, London W1T 7HA, UK
| | - Bruno Petrungaro
- Imperial College Health Partners, London NW1 2FB, UK.,The Health Economics Unit, West Bromwich B70 9LD, UK
| | - Kevin G Pollock
- Bristol Myers Squibb Pharmaceuticals Ltd, Uxbridge Business Park, Sanderson Road, Uxbridge, Middlesex UB8 1DH, UK
| | - Belinda Sandler
- Bristol Myers Squibb Pharmaceuticals Ltd, Uxbridge Business Park, Sanderson Road, Uxbridge, Middlesex UB8 1DH, UK
| | - Steven Lister
- Bristol Myers Squibb Pharmaceuticals Ltd, Uxbridge Business Park, Sanderson Road, Uxbridge, Middlesex UB8 1DH, UK
| | - Nathan R Hill
- Bristol Myers Squibb Pharmaceuticals Ltd, Uxbridge Business Park, Sanderson Road, Uxbridge, Middlesex UB8 1DH, UK
| | - Usman Farooqui
- Bristol Myers Squibb Pharmaceuticals Ltd, Uxbridge Business Park, Sanderson Road, Uxbridge, Middlesex UB8 1DH, UK
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16
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Zheng X, Wang F, Zhang J, Cui X, Jiang F, Chen N, Zhou J, Chen J, Lin S, Zou J. Using machine learning to predict atrial fibrillation diagnosed after ischemic stroke. Int J Cardiol 2022; 347:21-27. [PMID: 34774886 DOI: 10.1016/j.ijcard.2021.11.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/26/2021] [Accepted: 11/07/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Selecting best candidates for prolonged poststroke cardiac monitoring in acute ischemic stroke (AIS) patients is still challenging. We aimed to develop a machine learning (ML) model to select AIS patients at high risk of poststroke atrial fibrillation (AF) for prolonged cardiac monitoring and then to compare ML model with traditional risk scores and classic statistical logistic regression (classic-LR) model. METHODS AIS patients from July 2012 to September 2020 across Nanjing First Hospital were collected. We performed the LASSO regression for selecting the critical features and built five ML models to assess the risk of poststroke AF. The SHAP and partial dependence plot (PDP) method were introduced to interpret the optimal model. We also compared ML model with CHADS2 score, CHA2DS2-VASc score, AS5F score, HAVOC score, and classic-LR model. RESULTS A total of 3929 AIS patients were included. Among the five ML models, deep neural network (DNN) was the model with best performance. It also exhibited superior performance compared with CHADS2 score, CHA2DS2-VASc score, AS5F score, HAVOC score and classic-LR model. The results of SHAP and PDP method revealed age, cardioembolic stroke, large-artery atherosclerosis stroke, and NIHSS score at admission were the top four important features and revealed the DNN model had good interpretability and reliability. CONCLUSION The DNN model achieved best performance and improved prediction performance compared with traditional risk scores and classic-LR model. The DNN model can be applied to identify AIS patients at high risk of poststroke AF as best candidates for prolonged poststroke cardiac monitoring.
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Affiliation(s)
- Xiaohan Zheng
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fusang Wang
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Juan Zhang
- Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaoli Cui
- Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Fuping Jiang
- Department of Geriatrics, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Nihong Chen
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Junshan Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jinsong Chen
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
| | - Song Lin
- Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
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17
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Hill NR, Groves L, Dickerson C, Boyce R, Lawton S, Hurst M, Pollock KG, Sugrue DM, Lister S, Arden C, Davies DW, Martin AC, Sandler B, Gordon J, Farooqui U, Clifton D, Mallen C, Rogers J, Camm AJ, Cohen AT. Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England. J Med Econ 2022; 25:974-983. [PMID: 35834373 DOI: 10.1080/13696998.2022.2102355] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
OBJECTIVE The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting. METHODS Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY. CONCLUSIONS Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.
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Affiliation(s)
- Nathan R Hill
- Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK
| | - Lara Groves
- HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK
| | - Carissa Dickerson
- HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK
| | - Rebecca Boyce
- HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK
| | - Sarah Lawton
- School of Medicine, Keele University, Staffordshire, UK
| | - Michael Hurst
- Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK
| | | | - Daniel M Sugrue
- HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK
| | - Steven Lister
- Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK
| | - Chris Arden
- NHS Foundation Trust, University Hospital Southampton, Southampton, UK
| | | | - Anne-Celine Martin
- Université de Paris, Innovative Therapies in Haemostasis, INSERM, Hôpital Européen Georges Pompidou, Service de Cardiologie, Paris, France
| | | | - Jason Gordon
- HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK
| | | | - David Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Jennifer Rogers
- Statistical Research and Consultancy, Unit 2, PHASTAR, London, UK
| | - A John Camm
- Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St. George's University of London, London, UK
| | - Alexander T Cohen
- Department of Haematological Medicine, Guys and St Thomas' NHS Foundation Trust, King's College London, London, UK
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18
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Tseng AS, Noseworthy PA. Prediction of Atrial Fibrillation Using Machine Learning: A Review. Front Physiol 2021; 12:752317. [PMID: 34777014 PMCID: PMC8581234 DOI: 10.3389/fphys.2021.752317] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/04/2021] [Indexed: 02/01/2023] Open
Abstract
There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.
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Affiliation(s)
- Andrew S Tseng
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
| | - Peter A Noseworthy
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
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19
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Gallone G, Bruno F, D'Ascenzo F, DE Ferrari GM. What will we ask to artificial intelligence for cardiovascular medicine in the next decade? Minerva Cardiol Angiol 2021; 70:92-101. [PMID: 34713677 DOI: 10.23736/s2724-5683.21.05753-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Artificial intelligence (AI) comprises a wide range of technologies and methods with heterogeneous degrees of complexity, applications and abilities. In the cardiovascular field, AI holds the potential to fulfil many unsolved challenges, eventually translating into improved patient care. In particular, AI appears as the most promising tool to overcome the gap between ever-increasing data-rich technologies and their practical implementation in cardiovascular research, in the cardiologist routine, in the patient daily life and at the healthcare-policy level. A multiplicity of AI technologies is progressively pervading several aspects of precision cardiovascular medicine including early diagnosis, automated imaging processing and interpretation, disease sub-phenotyping, risk prediction and remote monitoring systems. Several methodological, logistical, educational and ethical challenges are emerging by integrating AI systems at any stage of cardiovascular medicine. This review will discuss the basics of AI methods, the growing body of evidence supporting the role of AI in the cardiovascular field and the challenges to overcome for an effective AI-integrated cardiovascular medicine.
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Affiliation(s)
- Guglielmo Gallone
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy - .,Department of Medical Sciences, University of Turin, Turin, Italy -
| | - Francesco Bruno
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy.,Department of Medical Sciences, University of Turin, Turin, Italy
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy.,Department of Medical Sciences, University of Turin, Turin, Italy
| | - Gaetano M DE Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy.,Department of Medical Sciences, University of Turin, Turin, Italy
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20
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Matteucci A, Bonanni M, Versaci F, Frati G, Peruzzi M, Sangiorgi G, Biondi-Zoccai G, Massaro G. Cardiovascular medicine: a year in review. Minerva Cardiol Angiol 2021; 70:40-55. [PMID: 34713681 DOI: 10.23736/s2724-5683.21.05816-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Cardiovascular medicine is facing several challenges in the current era, dominated by the rapid spread of a previously unknown virus around the world. Indeed, the 2020 COVID-19 pandemic set the course of cardiovascular science and education in an extraordinary way, hogging the attention of the medical community. Notably, while COVID-19 impacted research progress, there has been considerable effort in exploring topics of great interest, from the management of acute coronary syndromes to new horizons in the treatment of heart failure, from novelties in the surgical treatment of cardiovascular disease to new data on implantable cardiac devices, and from new diagnostic applications of multimodal imaging techniques to relevant basic science findings. Minerva Cardiology and Angiology, formerly Minerva Cardioangiologica, has strived to inform its readers on these topics and novelties, aiming for a succinct yet poignant melding of timeliness and accuracy. Accordingly, the purpose of this narrative review is to highlight and summarize the major research and review articles published during 2020. In particular, we provide a broad overview of the novelties identifying six major areas of interest in the field of cardiovascular sciences in which new evidences have contributed to improving prevention, diagnosis and treatment of heart and vessels diseases.
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Affiliation(s)
- Andrea Matteucci
- Department of Experimental Medicine, Tor Vergata University of Rome, Rome, Italy -
| | - Michela Bonanni
- Department of Experimental Medicine, Tor Vergata University of Rome, Rome, Italy
| | - Francesco Versaci
- UOC UTIC Emodinamica e Cardiologia, S. Maria Goretti Hospital, Latina, Italy
| | - Giacomo Frati
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy.,IRCCS NEUROMED, Pozzilli, Isernia, Italy
| | - Mariangela Peruzzi
- Mediterranea Cardiocentro, Napoli, Italy.,Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Sangiorgi
- Department of Biomedicine and Prevention, Tor Vergata University of Rome, Rome, Italy
| | - Giuseppe Biondi-Zoccai
- IRCCS NEUROMED, Pozzilli, Isernia, Italy.,Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Gianluca Massaro
- Division of Cardiology, Tor Vergata University of Rome, Rome, Italy
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21
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Nadarajah R, Alsaeed E, Hurdus B, Aktaa S, Hogg D, Bates MGD, Cowan C, Wu J, Gale CP. Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis. Heart 2021; 108:1020-1029. [PMID: 34607811 PMCID: PMC9209680 DOI: 10.1136/heartjnl-2021-320036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 09/09/2021] [Indexed: 12/02/2022] Open
Abstract
Objective Atrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community. Methods Ovid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation. Results Eleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526–0.815), CHA2DS2-VASc (Congestive heart failure, Hypertension, Age>75 (2 points), Stroke/transient ischemic attack/thromboembolism (2 points), Vascular disease, Age 65–74, Sex category) (summary c-statistic 0.679; 95% CI 0.620 to 0.736; 95% PI 0.531–0.811) and HATCH (Hypertension, Age, stroke or Transient ischemic attack, Chronic obstructive pulmonary disease, Heart failure) (summary c-statistic 0.669; 95% CI 0.600 to 0.732; 95% PI 0.513–0.803) models resulted in a c-statistic with a statistically significant 95% PI and moderate discriminative performance. No model met eligibility for inclusion in meta-analysis if studies at high risk of bias were excluded and certainty of effect estimates was ‘low’. Models derived by machine learning demonstrated strong discriminative performance, but lacked rigorous external validation. Conclusions Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. Systematic review registration PROSPERO CRD42021245093.
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Affiliation(s)
- Ramesh Nadarajah
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK .,Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Eman Alsaeed
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
| | - Ben Hurdus
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Suleman Aktaa
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK.,Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - David Hogg
- School of Computing, University of Leeds, Leeds, UK
| | - Matthew G D Bates
- Department of Cardiology, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
| | - Campbel Cowan
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Jianhua Wu
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK.,School of Dentistry, University of Leeds, Leeds, Leeds, UK
| | - Chris P Gale
- Leeds Institute of Data Analytics, University of Leeds, Leeds, UK.,Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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22
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Hermans ANL, Gawalko M, Dohmen L, van der Velden RMJ, Betz K, Verhaert DVM, Pluymaekers NAHA, Hendriks JM, Linz D. A systematic review of mobile health opportunities for atrial fibrillation detection and management. Eur J Prev Cardiol 2021; 29:e205-e208. [PMID: 34550370 DOI: 10.1093/eurjpc/zwab158] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Astrid N L Hermans
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Monika Gawalko
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands.,Institute of Pharmacology, West German Heart and Vascular Centre, University Duisburg-Essen, Hufelandstraße 55, Essen 45147, Germany.,1st Department of Cardiology, Medical University of Warsaw, Banacha 1A, 02-197 Warsaw, Poland
| | - Lisa Dohmen
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Rachel M J van der Velden
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Konstanze Betz
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Dominique V M Verhaert
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands.,Department of Cardiology, Radboud University Medical Center and Radboud Institute for Health Sciences, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
| | - Nikki A H A Pluymaekers
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | - Jeroen M Hendriks
- Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 1 Port Road, SA 5000 Adelaide, Australia.,Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Sturt North Sturt Rd, Bedford Park SA 5042, Adelaide, Australia
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands.,Department of Cardiology, Radboud University Medical Center and Radboud Institute for Health Sciences, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands.,Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, 1 Port Road, SA 5000 Adelaide, Australia.,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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23
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Abstract
Atrial fibrillation (AF) will become one of the biggest challenges in cardiovascular medicine in the near future. Attempting an improvement in future patient care calls explicitly for the screening of subclinical AF. Digital health solutions implementing communication technologies for the collection and analysis of digitally assessable data will most likely serve this need. Several new rapidly developing methods were introduced in the past decade. Although the vast majority still require scientific validation, the body of evidence is growing and several randomized controlled trials are planned. This review aims to give an overview of current technologies with a specific focus on mobile health (mHealth) and appraise their value with regard to the available scientific data.
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24
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Antoniades C, Asselbergs FW, Vardas P. The year in cardiovascular medicine 2020: digital health and innovation. Eur Heart J 2021; 42:732-739. [PMID: 33388767 PMCID: PMC7882364 DOI: 10.1093/eurheartj/ehaa1065] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/26/2020] [Accepted: 12/18/2020] [Indexed: 12/20/2022] Open
Affiliation(s)
- Charalambos Antoniades
- Acute Vascular Imaging Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX39DU, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, L6 West Wing, John Radcliffe Hospital, Headley Way, Oxford OX39DU, UK
| | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht University, Heidelberglaan 8, 3584 CX , Utrecht, the Netherlands
- Institute of Cardiovascular Science and Institute of Health Informatics, Faculty of Population Health Sciences, University College London, 222 Euston Road, NW1 2DA, London, UK
| | - Panos Vardas
- Heart Sector, Hygeia Hospitals Groups, Erithrou Stavrou 4, Marousi 151 23, Athens, Greece
- Cardiology Department, Medical School, University of Crete, University Campus of Voutes, 700 13, Heraclion, Greece
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25
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Kang J, Hanif M, Mirza E, Khan MA, Malik M. Machine learning in primary care: potential to improve public health. J Med Eng Technol 2020; 45:75-80. [PMID: 33283565 DOI: 10.1080/03091902.2020.1853839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
It is estimated that missed opportunities for diagnosis occur in 1 in 20 primary care appointments. This is not only detrimental to individual patients, but also to the healthcare system as health outcomes are affected and healthcare expenditure inevitably increases. There are many potential solutions to limit the number of missed opportunities for diagnosis and management, one of which is through the use of artificial intelligence. Artificial intelligence and machine learning research and capabilities have exponentially grown in the past decades, with their applications in medicine showing great promise. As such, this review aims to discuss the possible uses of machine learning in primary care to maximise the quality of care provided.
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Affiliation(s)
- Jungwoo Kang
- Barts and the London Medical School, Queen Mary University of London, London, United Kingdom
| | - Moghees Hanif
- Barts and the London Medical School, Queen Mary University of London, London, United Kingdom
| | - Eushaa Mirza
- Barts and the London Medical School, Queen Mary University of London, London, United Kingdom
| | - Muhammad Asad Khan
- Barts and the London Medical School, Queen Mary University of London, London, United Kingdom
| | - Muzaffar Malik
- Department of Medical Education, Brighton and Sussex Medical School, University of Brighton, Brighton, United Kingdom
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26
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Application of a machine learning algorithm for detection of atrial fibrillation in secondary care. IJC HEART & VASCULATURE 2020; 31:100674. [PMID: 34095444 PMCID: PMC8164133 DOI: 10.1016/j.ijcha.2020.100674] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 11/02/2020] [Indexed: 11/20/2022]
Abstract
Machine learning algorithms can accurately identify undiagnosed atrial fibrillation in patients. Algorithms developed in primary care can be used in secondary care with reasonable performance. An appreciable proportion of patients with undiagnosed AF could be detected in secondary care.
Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases risk of stroke. Opportunistic AF testing in high-risk patients typically requires frequent electrocardiogram tests to capture the arrhythmia. Risk-prediction algorithms may help to more accurately identify people with undiagnosed AF and machine learning (ML) may aid in the diagnosis of AF. Here, we applied an AF-risk prediction algorithm to secondary care data linked to primary care data in the DISCOVER database in order to evaluate changes in model performance, and identify patients not previously detected in primary care. We identified an additional 5,444 patients who had an AF diagnosis only in secondary care during the data extraction period. 2,696 (49.5%) were accepted by the algorithm and the algorithm correctly assigned 2,637 (97.8%) patients to the AF cohort. Using a risk threshold of 7.4% in patients aged ≥ 30 years, algorithm sensitivity and specificity was 38% and 95%, respectively. Approximately 15% of AF patients assigned to the AF cohort by the algorithm had a secondary care diagnosis with no record of AF in primary care. These additional patients did not substantially alter algorithm performance. The additional detection of previously undiagnosed AF patients in secondary care highlights unexpected potential utility of this ML algorithm.
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27
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Halasz G. Predicting the risk of atrial fibrillation: is the machine learning the answer? Eur J Prev Cardiol 2020; 28:596-597. [PMID: 33624085 DOI: 10.1093/eurjpc/zwaa058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Geza Halasz
- Cardiology Department, Guglielmo Da Saliceto Hospital, Via Giuseppe Taverna 49, Piacenza 29121, Italy
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