Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
Recruitment Status
COMPLETED
Total Enrollment
140000 participants
Study Classification
OBSERVATIONAL
Study Start Date
2020-11-02
Study Completion Date
2023-10-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial health-care expenditure as a result of stroke, sudden death, heart failure and unplanned hospitalisation. There is a compelling argument for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Strategies to identify individuals at higher risk of new onset AF are required. previous risk scores have been limited by data and methodology. The investigators will use routinely collected hospital-linked primary care data and focus on the use of artificial intelligence methods to develop and validate a model for the prediction of incident AF. Specifically, the investigators will investigate how population-based data may be used for precision medicine using a deep neural networks learning model. Using clinical factors readily accessible in primary care, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, as well as when incident AF will occur in those at risk, thus accelerating research assessing technologies for the improvement of risk prediction, and the targeting of high-risk individuals for preventive measures and screening.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices
NCT07291570
Atrial Fibrillation (AF)
NOT_YET_RECRUITING
Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): Pilot Study
NCT05898165
Atrial Fibrillation
Heart Diseases
Cardiovascular Diseases
ACTIVE_NOT_RECRUITING
A Study to Assess the Effectiveness of an Atrial Fibrillation (AF) Risk Prediction Algorithm and Diagnostic Test in Identifying Patients With AF.
NCT04045639
Atrial Fibrillation
COMPLETED
Predicting Disease Progression in Atrial Fibrillation: A Multiparametric Approach for Prognostic Marker Identification and Personalized Patient Management
NCT06647914
Atrial Fibrillation (AF)
NOT_YET_RECRUITING
Intelligent Monitoring to Predict Atrial Fibrillation
NCT06600620
Atrial Fibrillation New Onset
Postoperative Cardiovascular Complications
Atrial Fibrillation
SUSPENDED
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Atrial fibrillation (AF) is a common chronic condition with substantial impact on health outcomes. Many cases of AF are detected too late - as a manifestation of stroke, heart failure, or other complication. Whilst earlier detection of AF offers the potential to prevent premature cardiovascular disease, population screening is not recommended.
Atrial fibrillation (AF) is a leading cardiovascular health problem. It is the most common sustained cardiac arrhythmia, affecting 1-2% of the population of Europe and the USA, with a lifetime risk of one in four in the general population. It has an increasing prevalence as the population ages. Consequently, these estimates are likely to increase, and presently are underestimated given that AF may long remain undiagnosed. AF incurs 1-3% of healthcare expenditure as a result of stroke, sudden death, heart failure, unplanned hospitalisation, and associated complications. The resultant emerging AF epidemic and its associated costly complications (including, but not limited to stroke, depression, heart failure, acute coronary syndrome, cognitive decline and unscheduled hospitalisation) has ensured that AF is now a major threat to healthy longevity. The early diagnosis of AF, ideally before manifestation of the first complication, remains a major public health challenge. While for some patients AF may present with symptomatic palpitations, for others the first diagnosis of AF may be when they present to healthcare professionals with stroke, acute cardiac decompensation or co-morbidity exacerbation - a stage that is unnecessarily late in the disease trajectory. This is because many patients with AF may not have AF-associated symptoms. Given that nearly one third of patients admitted to a stroke ward have AF at the time of their admission to hospital and that oral anticoagulants reduce the risk of stroke by up to two thirds in those with AF who are at higher risk of stroke, there is a compelling argument for the earlier detection of AF. To that end, opportunistic screening for AF (pulse palpation followed by ECG in patients with an irregular pulse) in patients aged 65 years and over is now recommended in national and international guidelines. International guidelines also recommend the use of a 12 lead ECG and ambulatory rhythm monitors (within increasing duration according to perceived risk of AF), escalating to implantable leadless AF recorders in patients with suspected but undiagnosed AF - and each with implications for healthcare costs and patient satisfaction. Whilst there are promising results from systematic screening of elderly populations for AF using self-operated devices, presently there is no recommendation in the United Kingdom (UK) for population-wide systematic screening for AF because it is not yet clear if those identified as at risk would benefit from early diagnosis. Indeed, research is needed to understand better the detection rates, diagnostic accuracy, outcomes of such programs, as well as to define in what sub-populations AF screening would offer the greatest patient and public health value.
The identification of AF has important patient and clinical ramifications. Those patients at higher risk of stroke (CHADSVASC score ≥ 2) without a contra-indication should be offered stroke prophylaxis with an oral anticoagulant. Moreover, most patients with AF will have stroke risk factors, making them eligible for an oral anticoagulant, and many will have concomitant cardiovascular disease (such as hypertension, valvular heart disease or heart failure) making them eligible for further investigation or treatment. Equally, in those with AF who are low risk for stroke (and therefore do not qualify for oral anticoagulation), surveillance for increasing stroke risk is advisable. Predicting precisely if and when a person will have new onset AF may allow phenotype and temporal-specific (thus more effective) screening, as well as identify putative risk markers for AF aetiology. For example, patients presently in sinus rhythm, but at higher risk of stroke and predicted to develop AF at a specific time-point in the future may benefit from screening for AF nearer the forecasted date. Equally, modifiable risk factors for the development of AF and for risk of stroke may be proactively addressed in light of knowledge of higher risk of new onset AF, and new risk factors studied for causality. Other possible research opportunities may include the study of patients who do not have and are not predicted to have AF, and the evaluation of lifestyle, device technology and pharmacotherapeutic strategies to reduce the risk of AF in patients at high predicted risk of new onset AF.
To date, a number of AF risk prediction tools have been developed, including those from the CHARGE-AF consortium, Framingham Heart Study, the CHADS score, the CHADSVASC score and the CHEST score, among others. The CHEST score (structural heart disease, heart failure, age ≥ 75 years, coronary artery disease, hyperthyroidism, Chronic Obstructive Pulmonary Disease (COPD), and hypertension) derived from 471,446 subjects from the Chinese Yunnan Insurance Database and validated in 451,199 subjects from the Korean National Health Insurance Service was found to predict future incident AF. Of the 4764 participants in the Framingham Heart Study, age, sex, body-mass index, systolic blood pressure, treatment for hypertension, the time from the onset of the P wave to the start of the ventricular depolarization (QRS) complex (PR interval), clinically significant cardiac murmur, and heart failure were found using survival modelling to be components of a score predicting incident AF at 10 years. However, each of the studies to date are limited by one or more of, their use of geographically remote data, historical data, small datasets, lack of temporal information, crude risk modelling with consequent suboptimal model performance and/or predictor variables not readily available to the General Practitioner. Understandably, none have reached widespread clinical practice. Artificial intelligence facilitates the use of vast quantities of event data and the associated temporal information (such as that in primary care datasets), handles large numbers of predictors with automatic variable selection techniques, accommodates nonlinearities and interactions among variables, enables a live learning approach (whereby the prediction model is automatically updated), and can use population-wide data to predict if and when there will be new onset AF for an individual. A range of Artificial Intelligence (AI) techniques have been applied to EHR data and have demonstrated better diagnostic and prediction power over traditional statistical approaches in large scale EHR data. Yet, as highlighted recently, it is important to identify models that are clinically useful. For example, a study which developed an AI-enabled ECG algorithm that predicted AF from ECGs with normal sinus rhythm, whilst an important step forward may not be applicable in the community setting where routine ECGs are not always available. Thus, developing a predictive algorithm for new onset AF from routine primary care electronic health records data using AI techniques could offer the opportunity for early translation to clinical practice. The investigators will develop and validate a deep neural networks learning model, utilising large scale linked electronic health records (EHR) from primary care, to predict the risk of new AF. The prediction algorithm will be trained and tested for its accuracy and robustness in predicting future AF events using Clinical Practice Research Datalink (CPRD)-Global initiative for chronic Obstructive Lung Disease (GOLD), and will be externally validated using similar databases CPRD-AURUM but at different geographic locations. The new predictive algorithm will be compared against a range of classic machine learning techniques as well as traditional statistical predictive modelling methods. Pending a successful model of improving the predicting accuracy of at least 5% compared with existing models, the algorithm could be made readily available through free to use software.
Atrial fibrillation (AF) is a leading cardiovascular health problem. It is the most common sustained cardiac arrhythmia, affecting 1-2% of the population of Europe and the USA, with a lifetime risk of one in four in the general population. It has an increasing prevalence as the population ages. Consequently, these estimates are likely to increase, and presently are underestimated given that AF may long remain undiagnosed. AF incurs 1-3% of healthcare expenditure as a result of stroke, sudden death, heart failure, unplanned hospitalisation, and associated complications. The resultant emerging AF epidemic and its associated costly complications (including, but not limited to stroke, depression, heart failure, acute coronary syndrome, cognitive decline and unscheduled hospitalisation) has ensured that AF is now a major threat to healthy longevity. The early diagnosis of AF, ideally before manifestation of the first complication, remains a major public health challenge. While for some patients AF may present with symptomatic palpitations, for others the first diagnosis of AF may be when they present to healthcare professionals with stroke, acute cardiac decompensation or co-morbidity exacerbation - a stage that is unnecessarily late in the disease trajectory. This is because many patients with AF may not have AF-associated symptoms. Given that nearly one third of patients admitted to a stroke ward have AF at the time of their admission to hospital and that oral anticoagulants reduce the risk of stroke by up to two thirds in those with AF who are at higher risk of stroke, there is a compelling argument for the earlier detection of AF. To that end, opportunistic screening for AF (pulse palpation followed by ECG in patients with an irregular pulse) in patients aged 65 years and over is now recommended in national and international guidelines. International guidelines also recommend the use of a 12 lead ECG and ambulatory rhythm monitors (within increasing duration according to perceived risk of AF), escalating to implantable leadless AF recorders in patients with suspected but undiagnosed AF - and each with implications for healthcare costs and patient satisfaction. Whilst there are promising results from systematic screening of elderly populations for AF using self-operated devices, presently there is no recommendation in the United Kingdom (UK) for population-wide systematic screening for AF because it is not yet clear if those identified as at risk would benefit from early diagnosis. Indeed, research is needed to understand better the detection rates, diagnostic accuracy, outcomes of such programs, as well as to define in what sub-populations AF screening would offer the greatest patient and public health value.
The identification of AF has important patient and clinical ramifications. Those patients at higher risk of stroke (CHADSVASC score ≥ 2) without a contra-indication should be offered stroke prophylaxis with an oral anticoagulant. Moreover, most patients with AF will have stroke risk factors, making them eligible for an oral anticoagulant, and many will have concomitant cardiovascular disease (such as hypertension, valvular heart disease or heart failure) making them eligible for further investigation or treatment. Equally, in those with AF who are low risk for stroke (and therefore do not qualify for oral anticoagulation), surveillance for increasing stroke risk is advisable. Predicting precisely if and when a person will have new onset AF may allow phenotype and temporal-specific (thus more effective) screening, as well as identify putative risk markers for AF aetiology. For example, patients presently in sinus rhythm, but at higher risk of stroke and predicted to develop AF at a specific time-point in the future may benefit from screening for AF nearer the forecasted date. Equally, modifiable risk factors for the development of AF and for risk of stroke may be proactively addressed in light of knowledge of higher risk of new onset AF, and new risk factors studied for causality. Other possible research opportunities may include the study of patients who do not have and are not predicted to have AF, and the evaluation of lifestyle, device technology and pharmacotherapeutic strategies to reduce the risk of AF in patients at high predicted risk of new onset AF.
To date, a number of AF risk prediction tools have been developed, including those from the CHARGE-AF consortium, Framingham Heart Study, the CHADS score, the CHADSVASC score and the CHEST score, among others. The CHEST score (structural heart disease, heart failure, age ≥ 75 years, coronary artery disease, hyperthyroidism, Chronic Obstructive Pulmonary Disease (COPD), and hypertension) derived from 471,446 subjects from the Chinese Yunnan Insurance Database and validated in 451,199 subjects from the Korean National Health Insurance Service was found to predict future incident AF. Of the 4764 participants in the Framingham Heart Study, age, sex, body-mass index, systolic blood pressure, treatment for hypertension, the time from the onset of the P wave to the start of the ventricular depolarization (QRS) complex (PR interval), clinically significant cardiac murmur, and heart failure were found using survival modelling to be components of a score predicting incident AF at 10 years. However, each of the studies to date are limited by one or more of, their use of geographically remote data, historical data, small datasets, lack of temporal information, crude risk modelling with consequent suboptimal model performance and/or predictor variables not readily available to the General Practitioner. Understandably, none have reached widespread clinical practice. Artificial intelligence facilitates the use of vast quantities of event data and the associated temporal information (such as that in primary care datasets), handles large numbers of predictors with automatic variable selection techniques, accommodates nonlinearities and interactions among variables, enables a live learning approach (whereby the prediction model is automatically updated), and can use population-wide data to predict if and when there will be new onset AF for an individual. A range of Artificial Intelligence (AI) techniques have been applied to EHR data and have demonstrated better diagnostic and prediction power over traditional statistical approaches in large scale EHR data. Yet, as highlighted recently, it is important to identify models that are clinically useful. For example, a study which developed an AI-enabled ECG algorithm that predicted AF from ECGs with normal sinus rhythm, whilst an important step forward may not be applicable in the community setting where routine ECGs are not always available. Thus, developing a predictive algorithm for new onset AF from routine primary care electronic health records data using AI techniques could offer the opportunity for early translation to clinical practice. The investigators will develop and validate a deep neural networks learning model, utilising large scale linked electronic health records (EHR) from primary care, to predict the risk of new AF. The prediction algorithm will be trained and tested for its accuracy and robustness in predicting future AF events using Clinical Practice Research Datalink (CPRD)-Global initiative for chronic Obstructive Lung Disease (GOLD), and will be externally validated using similar databases CPRD-AURUM but at different geographic locations. The new predictive algorithm will be compared against a range of classic machine learning techniques as well as traditional statistical predictive modelling methods. Pending a successful model of improving the predicting accuracy of at least 5% compared with existing models, the algorithm could be made readily available through free to use software.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Atrial Fibrillation
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
Observational Model Type
COHORT
Study Time Perspective
RETROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
All eligible patients
Observational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM.
Observational
Intervention Type
OTHER
Observational - no intervention given
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Observational
Observational - no intervention given
Intervention Type
OTHER
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* Diagnosed AF after 1 January 2009 (Identified using Read codes (for the CPRD patient profile) and ICD-10 codes (for HES events)
* In Clinical Practice Research Datalink -Global initiative for chronic Obstructive Lung Disease (CPRD-GOLD) and eligible for data linkage.
* Have at least 1-year follow-up in the period between 1st Jan 1998 and 31st December 2018.
* In Clinical Practice Research Datalink -Global initiative for chronic Obstructive Lung Disease (CPRD-GOLD) and eligible for data linkage.
* Have at least 1-year follow-up in the period between 1st Jan 1998 and 31st December 2018.
Exclusion Criteria
* Under 18 at date of the first registration in CPRD
* Diagnosed with AF before 1st Jan 1998
* In CPRD-GOLD and not eligible for data linkage
* Has less than one year follow up in CPRD
* Diagnosed with AF before 1st Jan 1998
* In CPRD-GOLD and not eligible for data linkage
* Has less than one year follow up in CPRD
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
British Heart Foundation
OTHER
Sponsor Role
collaborator
University of Leeds
OTHER
Sponsor Role
lead
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Dr Christopher Gale
Professor of Cardiovascular Medicine
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Christopher P Gale, PhD
Role: PRINCIPAL_INVESTIGATOR
University of Leeds
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
University of Leeds
Leeds, West Yorkshire, United Kingdom
Site Status
Countries
Review the countries where the study has at least one active or historical site.
United Kingdom
References
Explore related publications, articles, or registry entries linked to this study.
Conen D. Epidemiology of atrial fibrillation. Eur Heart J. 2018 Apr 21;39(16):1323-1324. doi: 10.1093/eurheartj/ehy171. No abstract available.
Reference Type
RESULT
Kirchhof P. The future of atrial fibrillation management: integrated care and stratified therapy. Lancet. 2017 Oct 21;390(10105):1873-1887. doi: 10.1016/S0140-6736(17)31072-3. Epub 2017 Apr 28.
Reference Type
RESULT
Fuster V, Ryden LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA, Halperin JL, Le Heuzey JY, Kay GN, Lowe JE, Olsson SB, Prystowsky EN, Tamargo JL, Wann S, Smith SC Jr, Jacobs AK, Adams CD, Anderson JL, Antman EM, Halperin JL, Hunt SA, Nishimura R, Ornato JP, Page RL, Riegel B, Priori SG, Blanc JJ, Budaj A, Camm AJ, Dean V, Deckers JW, Despres C, Dickstein K, Lekakis J, McGregor K, Metra M, Morais J, Osterspey A, Tamargo JL, Zamorano JL; American College of Cardiology/American Heart Association Task Force on Practice Guidelines; European Society of Cardiology Committee for Practice Guidelines; European Heart Rhythm Association; Heart Rhythm Society. ACC/AHA/ESC 2006 Guidelines for the Management of Patients with Atrial Fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Revise the 2001 Guidelines for the Management of Patients With Atrial Fibrillation): developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society. Circulation. 2006 Aug 15;114(7):e257-354. doi: 10.1161/CIRCULATIONAHA.106.177292. No abstract available.
Reference Type
RESULT
Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, Ernst S, Van Gelder IC, Al-Attar N, Hindricks G, Prendergast B, Heidbuchel H, Alfieri O, Angelini A, Atar D, Colonna P, De Caterina R, De Sutter J, Goette A, Gorenek B, Heldal M, Hohloser SH, Kolh P, Le Heuzey JY, Ponikowski P, Rutten FH; ESC Committee for Practice Guidelines. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Europace. 2010 Oct;12(10):1360-420. doi: 10.1093/europace/euq350. No abstract available.
Reference Type
RESULT
Kirchhof P, Auricchio A, Bax J, Crijns H, Camm J, Diener HC, Goette A, Hindricks G, Hohnloser S, Kappenberger L, Kuck KH, Lip GY, Olsson B, Meinertz T, Priori S, Ravens U, Steinbeck G, Svernhage E, Tijssen J, Vincent A, Breithardt G. Outcome parameters for trials in atrial fibrillation: executive summary. Eur Heart J. 2007 Nov;28(22):2803-17. doi: 10.1093/eurheartj/ehm358. Epub 2007 Sep 25.
Reference Type
RESULT
Friberg L, Rosenqvist M, Lindgren A, Terent A, Norrving B, Asplund K. High prevalence of atrial fibrillation among patients with ischemic stroke. Stroke. 2014 Sep;45(9):2599-605. doi: 10.1161/STROKEAHA.114.006070. Epub 2014 Jul 17.
Reference Type
RESULT
Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation: a major contributor to stroke in the elderly. The Framingham Study. Arch Intern Med. 1987 Sep;147(9):1561-4.
Reference Type
RESULT
January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC Jr, Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW; ACC/AHA Task Force Members. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. Circulation. 2014 Dec 2;130(23):e199-267. doi: 10.1161/CIR.0000000000000041. Epub 2014 Mar 28. No abstract available.
Reference Type
RESULT
Aronson D, Shalev V, Katz R, Chodick G, Mutlak D. Risk Score for Prediction of 10-Year Atrial Fibrillation: A Community-Based Study. Thromb Haemost. 2018 Sep;118(9):1556-1563. doi: 10.1055/s-0038-1668522. Epub 2018 Aug 13.
Reference Type
RESULT
Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB, Sinner MF, Sotoodehnia N, Fontes JD, Janssens AC, Kronmal RA, Magnani JW, Witteman JC, Chamberlain AM, Lubitz SA, Schnabel RB, Agarwal SK, McManus DD, Ellinor PT, Larson MG, Burke GL, Launer LJ, Hofman A, Levy D, Gottdiener JS, Kaab S, Couper D, Harris TB, Soliman EZ, Stricker BH, Gudnason V, Heckbert SR, Benjamin EJ. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013 Mar 18;2(2):e000102. doi: 10.1161/JAHA.112.000102.
Reference Type
RESULT
Chamberlain AM, Agarwal SK, Folsom AR, Soliman EZ, Chambless LE, Crow R, Ambrose M, Alonso A. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk in Communities [ARIC] study). Am J Cardiol. 2011 Jan;107(1):85-91. doi: 10.1016/j.amjcard.2010.08.049.
Reference Type
RESULT
Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB Sr, Newton-Cheh C, Yamamoto JF, Magnani JW, Tadros TM, Kannel WB, Wang TJ, Ellinor PT, Wolf PA, Vasan RS, Benjamin EJ. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009 Feb 28;373(9665):739-45. doi: 10.1016/S0140-6736(09)60443-8.
Reference Type
RESULT
Schnabel RB, Aspelund T, Li G, Sullivan LM, Suchy-Dicey A, Harris TB, Pencina MJ, D'Agostino RB Sr, Levy D, Kannel WB, Wang TJ, Kronmal RA, Wolf PA, Burke GL, Launer LJ, Vasan RS, Psaty BM, Benjamin EJ, Gudnason V, Heckbert SR. Validation of an atrial fibrillation risk algorithm in whites and African Americans. Arch Intern Med. 2010 Nov 22;170(21):1909-17. doi: 10.1001/archinternmed.2010.434.
Reference Type
RESULT
Li YG, Pastori D, Farcomeni A, Yang PS, Jang E, Joung B, Wang YT, Guo YT, Lip GYH. A Simple Clinical Risk Score (C2HEST) for Predicting Incident Atrial Fibrillation in Asian Subjects: Derivation in 471,446 Chinese Subjects, With Internal Validation and External Application in 451,199 Korean Subjects. Chest. 2019 Mar;155(3):510-518. doi: 10.1016/j.chest.2018.09.011. Epub 2018 Oct 4.
Reference Type
RESULT
Kolek MJ, Graves AJ, Xu M, Bian A, Teixeira PL, Shoemaker MB, Parvez B, Xu H, Heckbert SR, Ellinor PT, Benjamin EJ, Alonso A, Denny JC, Moons KG, Shintani AK, Harrell FE Jr, Roden DM, Darbar D. Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records. JAMA Cardiol. 2016 Dec 1;1(9):1007-1013. doi: 10.1001/jamacardio.2016.3366.
Reference Type
RESULT
Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29;375(13):1216-9. doi: 10.1056/NEJMp1606181. No abstract available.
Reference Type
RESULT
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.
Reference Type
RESULT
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium; Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, Bult P, Beca F, Beck AH, Wang D, Khosla A, Gargeya R, Irshad H, Zhong A, Dou Q, Li Q, Chen H, Lin HJ, Heng PA, Hass C, Bruni E, Wong Q, Halici U, Oner MU, Cetin-Atalay R, Berseth M, Khvatkov V, Vylegzhanin A, Kraus O, Shaban M, Rajpoot N, Awan R, Sirinukunwattana K, Qaiser T, Tsang YW, Tellez D, Annuscheit J, Hufnagl P, Valkonen M, Kartasalo K, Latonen L, Ruusuvuori P, Liimatainen K, Albarqouni S, Mungal B, George A, Demirci S, Navab N, Watanabe S, Seno S, Takenaka Y, Matsuda H, Ahmady Phoulady H, Kovalev V, Kalinovsky A, Liauchuk V, Bueno G, Fernandez-Carrobles MM, Serrano I, Deniz O, Racoceanu D, Venancio R. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017 Dec 12;318(22):2199-2210. doi: 10.1001/jama.2017.14585.
Reference Type
RESULT
Shah NH, Milstein A, Bagley PhD SC. Making Machine Learning Models Clinically Useful. JAMA. 2019 Oct 8;322(14):1351-1352. doi: 10.1001/jama.2019.10306. No abstract available.
Reference Type
RESULT
Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018 Apr 3;319(13):1317-1318. doi: 10.1001/jama.2017.18391. No abstract available.
Reference Type
RESULT
Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019 Sep 7;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0. Epub 2019 Aug 1.
Reference Type
RESULT
Sultan AA, West J, Grainge MJ, Riley RD, Tata LJ, Stephansson O, Fleming KM, Nelson-Piercy C, Ludvigsson JF. Development and validation of risk prediction model for venous thromboembolism in postpartum women: multinational cohort study. BMJ. 2016 Dec 5;355:i6253. doi: 10.1136/bmj.i6253.
Reference Type
RESULT
Rahimian F, Salimi-Khorshidi G, Payberah AH, Tran J, Ayala Solares R, Raimondi F, Nazarzadeh M, Canoy D, Rahimi K. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records. PLoS Med. 2018 Nov 20;15(11):e1002695. doi: 10.1371/journal.pmed.1002695. eCollection 2018 Nov.
Reference Type
RESULT
Huang Z, Dong W, Duan H, Liu J. A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records. IEEE Trans Biomed Eng. 2018 May;65(5):956-968. doi: 10.1109/TBME.2017.2731158. Epub 2017 Jul 24.
Reference Type
RESULT
Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24. Erratum In: Stat Med. 2019 Dec 30;38(30):5672. doi: 10.1002/sim.8409.
Reference Type
RESULT
Nadarajah R, Wu J, Frangi AF, Hogg D, Cowan C, Gale C. Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence. BMJ Open. 2021 Nov 2;11(11):e052887. doi: 10.1136/bmjopen-2021-052887.
Reference Type
DERIVED
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
120029
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.
Identification of the Metabolic Signature of Atrial Fibrillation for Personalized Prevention
NCT06735001
RECRUITING
NA
"De Novo" Atrial Fibrillation in Patients With Heart Failure: Incidence; Predictors and Relevance.
NCT04755010
UNKNOWN
Tracking Atrial Fibrillation After Intensive Care Admission
NCT05229211
RECRUITING
Leveraging AI-ECG Technology for Early Notification and Tracking of AF Development
NCT06847932
RECRUITING
NA
Remote Monitoring of AF Recurrence Using mHealth Technology
NCT05037136
COMPLETED
Natural History and Patient Journey in Atrial Fibrillation: a Nationwide Linked Electronic Health Records Study of 5.6 Million Individuals.
NCT04786366
UNKNOWN
Biatrial Global High-density Electroanatomical Mapping of Atrial Fibrillation
NCT03812601
COMPLETED
Precision Medicine in Ischemic Stroke and Atrial Fibrillation
NCT04637087
COMPLETED
NA
REVEAL AF: Incidence of AF in High Risk Patients
NCT01727297
COMPLETED
NA
Risk Profile for Patients With Atrial Fibrillation
NCT01510210
ACTIVE_NOT_RECRUITING
Artificial Intelligence Models to Predict Clinically Relevant Cardiovascular Outcomes
NCT06847100
COMPLETED
Machine Learning in Atrial Fibrillation
NCT05371405
RECRUITING
Secondary Prevention of Atrial Fibrilation
NCT03259893
TERMINATED
NA
Atrial Fibrillation Health Literacy and Information Technology Trial
NCT04075994
COMPLETED
NA
Sensitivity and Specificity of a Mobile Lead-one ECG Like Device for the Detection of Atrial Fibrillation (AF)
NCT03524625
UNKNOWN
Atrial Fibrillation Data Linkage Non-Interventional Study
NCT05810727
COMPLETED
Connected Cardiology to Control Cardiac Rythm
NCT06091514
NOT_YET_RECRUITING
NA
Personalised Ablation Strategies in AF
NCT05633303
RECRUITING
NA
Management of New-Onset Postoperative Atrial Fibrillation
NCT01395836
COMPLETED
Left Atrial Strain Values as an Early Predictor of Atrial Fibrillation
NCT06417112
COMPLETED
Utilizing Novel Dipole Density Capabilities to Objectively Visualize the Etiology of Rhythms in Atrial Fibrillation
NCT02825992
COMPLETED
NA
Subclinical Atrial Fibrillation Home Monitoring in Hypertensive Patients
NCT07058831
RECRUITING
Multimodal Cardiac Imaging Registry in Patients with Atrial Fibrillation
NCT06584266
RECRUITING
Realistic Computational Electrophysiology Simulations for the Targetted Treatment of Atrial Fibrillation
NCT05057507
ACTIVE_NOT_RECRUITING
Prospective Validation Study of AI-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation
NCT05725187
ENROLLING_BY_INVITATION