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NCT04657900
Predicting Patient-level New Onset Atrial Fibrillation
trial testing Observational in Atrial Fibrillation in 140,000 participants. Completed in 31 October 2023.
31 October 2023
Quick facts
| Lead sponsor | University of Leeds |
|---|---|
| Status | Completed |
| Study type | OBSERVATIONAL |
| Enrollment | 140,000 |
| Start date | 2 November 2020 |
| Primary completion | 31 October 2023 |
| Estimated completion | 31 October 2023 |
| Sites | 1 location across United Kingdom |
Drugs / interventions tested
- Observational — full drug profile →
Conditions studied
- Atrial Fibrillation — all drugs for Atrial Fibrillation →
Sponsor
University of Leeds
Who can join
18 and older, any sex, with Atrial Fibrillation. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
2 peer-reviewed publications reference this trial (live from Europe PMC):
-
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.
Nadarajah R, Wu J, Frangi AF, Hogg D, et al · · 2021 · cited 20× · PMID 34728455 · DOI 10.1136/bmjopen-2021-052887 -
What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key.
Nadarajah R, Wu J, Frangi AF, Hogg D, et al · · 2022 · cited 4× · PMID 34940849 · DOI 10.1093/ehjqcco/qcab094
Verify or expand the search:
- PubMed search for NCT04657900
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Currently open trials in the same condition.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT04657900 (US National Library of Medicine, public domain)
- Publications: Europe PMC API search by NCT ID, retrieved 10 June 2026
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by University of Leeds
- Last refreshed: 8 May 2024
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT04657900.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing