Key result
The FIND-HF model demonstrated good prediction performance for incident heart failure diagnosis within one year across multiple international electronic health record cohorts (AUC 0.72-0.85).
Why the study?
Late diagnosis of HF is associated with worse outcomes, prompting the need for a scalable tool to identify individuals at high risk of undiagnosed HF using routine EHR data.
Does the FIND-HF model accurately predict incident heart failure diagnosis within one year in adults aged ≥40 years without known heart failure using routine electronic health records?
Cohort (n=18,792,646)
Yes
Does the FIND-HF model accurately predict incident heart failure diagnosis within one year in adults aged ≥40 years without known heart failure using routine electronic health records?
The FIND-HF model is a scalable, EHR-based tool that accurately predicts incident heart failure within one year, potentially streamlining clinical pathways by ruling out low-risk cases and targeting diagnostics for high-risk patients.
Answers are grounded in this paper and cite their sources.
Supports scalable EHR-based HF risk stratification; leaves open prospective validation before clinical use.
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.
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Nakao et al. (2026) conducted a cohort in Undiagnosed Heart Failure (n=18,792,646). FIND-HF risk stratification model vs. Low FIND-HF risk was evaluated on Incident heart failure diagnosis within one year (AUC 0.79). The FIND-HF model demonstrated good prediction performance for incident heart failure diagnosis within one year across multiple international electronic health record cohorts (AUC 0.72-0.85).
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