Journal article
Scalable risk stratification of undiagnosed heart failure using routine health data and its association with imaging phenotypes and outcomes
Scientific reports
07/11/2026
DOI: 10.1038/s41598-026-60673-w
PMID: 42436241
Abstract
Late diagnosis of Heart failure (HF) is associated with worse outcomes. We aimed to develop a scalable tool to identify those at high risk of undiagnosed HF using routine electronic health records (EHR). We developed and internally validated a logistic regression (FIND-HF) model for incident HF diagnosis within one year in United Kingdom primary care EHRs (CPRD-Aurum, n=3 520 186), with good prediction performance (area under the receiver operating characteristic curve (AUC) 0.79), equal to more complex modelling techniques. We externally validated FIND-HF in United Kingdom (CPRD-GOLD, n=570 850, AUC 0.72), Japan (JMDC, n=6 820 694, AUC 0.73), United States of America (Epic Cosmos, n=7 710 398, AUC 0.78), and Taiwan (NTUH, n=170 518, AUC 0.85). In a cohort who had undergone HF diagnostics an optimised FIND-HF threshold had a positive predictive value of 21.4% and a negative predictive value of 96.9%. Amongst patients with HF who had undergone cardiac magnetic resonance imaging, high FIND-HF risk compared with low FIND-HF risk as reference, was associated with increased risk of a primary composite outcome of heart failure hospitalisation or cardiovascular death and more advanced adverse remodelling including lower left ventricular ejection fraction. FIND-HF is a scalable EHR-based model which has the potential to help rule out undiagnosed HF in low risk cases, whilst high risk cases are associated with more advanced cardiac dysfunction and worse prognosis.
Details
- Title: Subtitle
- Scalable risk stratification of undiagnosed heart failure using routine health data and its association with imaging phenotypes and outcomes
- Creators
- Yoko M Nakao - University of LeedsRamesh Nadarajah - University of LeedsFarag Shuweihdi - University of LeedsChristopher J Hayward - University of LeedsMichihiko Goto - University of IowaJung-Chi Hsu - National Taiwan University HospitalMohammad Haris - University of LeedsBen Hurdus - University of LeedsOsama Tariq - Leeds Teaching Hospitals NHS TrustTemar Habtezghi - Leeds Teaching Hospitals NHS TrustAnna Helbitz - University of LeedsAli Wahab - University of LeedsLan Mu - Queen Mary University of LondonKazuhiro Nakao - University of LeedsPeter Swoboda - University of LeedsAmitava Banerjee - University College LondonMark C Petrie - British Heart FoundationClare J Taylor - University of BirminghamKoji Kawakami - Kyoto UniversityJianhua Wu - Queen Mary University of LondonChris P Gale - University of Leeds
- Resource Type
- Journal article
- Publication Details
- Scientific reports
- DOI
- 10.1038/s41598-026-60673-w
- PMID
- 42436241
- NLM abbreviation
- Sci Rep
- ISSN
- 2045-2322
- eISSN
- 2045-2322
- Publisher
- Springer Nature
- Language
- English
- Electronic publication date
- 07/11/2026
- Academic Unit
- Internal Medicine
- Record Identifier
- 9985183419302771
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