Rethinking the Bland-Altman plot when quantifying virtual coronary physiology

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 and 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.