PERSPECTIVE article
Front. Cardiovasc. Med.
Sec. Coronary Artery Disease
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1687923
This article is part of the Research TopicCoronary Physiology in the Spotlight: Advancing Diagnosis and Treatment in CAD and Microvascular DiseaseView all 8 articles
Rethinking the Bland-Altman plot when quantifying Virtual Coronary Physiology
Provisionally accepted
- The University of Sheffield, Sheffield, United Kingdom
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Angiography-derived, or 'virtual' fractional flow reserve (vFFR) is beginning to replace invasive assessment in some catheterisation laboratories. Despite its incorporation into clinical guidelines, recent clinical outcomes data have cast doubt over its effectiveness relative to invasive assessment. These somewhat unexpected trial results are underpinned by poorer than anticipated agreement between invasive and vFFR. In particular, the widespread use of traditional Bland-Altman analysis fails to account for the phenomenon of worsening agreement at lower FFR values which hinders comparison between studies. We propose a novel approach using quantile regression to derive overall bias limits of agreement (LOA) to better characterise agreement across the spectrum of coronary disease. This new method may improve understanding of optimal vFFR clinical applications and addresses common statistical deficiencies in current validation practices.
Keywords: Coronary Artery Disease, FFR, virtual physiology, Bland-Altman, agreement
Received: 18 Aug 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Taylor, Saxton, Halliday, Gunn and Morris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Daniel Taylor, daniel.taylor@sheffield.ac.uk
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