ArticleHeart (British Cardiac Society)2023
Prediction of short-term atrial fibrillation risk using primary care electronic health records.
Article in Heart (British Cardiac Society), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06444711 (A Randomised Clinical Trial for OPTIMISation of Cardio-renal-metabolic-pulmonary Disease Guideline Adherence in High Risk Community Dwelling Individuals and Evaluation of Outcomes), which is not on this map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Randomised Clinical Trial for OPTIMISation of Cardio-renal-metabolic-pulmonary Disease Guideline Adherence in High Risk Community Dwelling Individuals and Evaluation of Outcomes
Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Non-antiarrhythmic pharmacotherapy in cardio-renal-metabolic disease and incident atrial fibrillation: a trial meta-analysis.European heart journal · 2026Pooled it
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Trial
- Association of the EHRA/HRS/APHRS/LHRS definition of atrial cardiomyopathy with incident atrial fibrillation, heart failure, and stroke.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Article
- [The role of psychosocial risk factors in the development and course of atrial fibrillation].Herzschrittmachertherapie & Elektrophysiologie · 2026Review
- Interaction between clinical and genetic risk of atrial fibrillation in the danish diet, cancer and health cohort.Open heart · 2026Article
- Stratifying prognosis in heart failure patients with reduced ejection fraction and atrial cardiomyopathy.ESC heart failure · 2026Article
- Short-Term Arrhythmia Prediction Using AI Based on Daily Data From Implantable Devices: Multicenter Prospective Observational Study.JMIR cardio · 2026Observational
- Battling complexity with complexity: why multi-modal artificial intelligence may be the key to atrial fibrillation prediction.European heart journal. Digital health · 2026Article
- Recent-onset atrial fibrillation: challenges and opportunities.European heart journal · 2026Review
- Machine Learning for Early Detection and Prevention of Disease Using Electronic Health Records.Methods in molecular biology (Clifton, N.J.) · 2026Review
- The predictive power of left atrial reservoir strain for long-term atrial fibrillation in the Atherosclerosis Risk in Communities study.Heart rhythm · 2025Article
- Incidence and severity of aortic stenosis according to machine learning predicted risk of atrial fibrillation.Scientific reports · 2025Article
- Dynamic Increase of the CJournal of the American Heart Association · 2025Article
- Identification, characterisation and outcomes of pre-atrial fibrillation in heart failure with reduced ejection fraction.ESC heart failure · 2025Article
- Are Machine Learning methods effective in detecting undiagnosed atrial fibrillation in primary care settings using electronic health records? A systematic review.PLOS digital health · 2025Article
- Article
- Systematic screening for atrial fibrillation with non-invasive devices: a systematic review and meta-analysis.The Lancet regional health. Europe · 2025Article
- The Diagnostic and Predictive Potential of miR-328 in Atrial Fibrillation: Insights from a Spontaneously Hypertensive Rat Model.International journal of molecular sciences · 2025Article
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Authors and funding
13 authors at 5 institutions in 4 countries.
Funding
Abstract
objectiveAtrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)).
methodsWe used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA
resultsOf 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA
conclusionsFIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.