Nomogram based on virtual hyperemic pullback pressure gradients for predicting the suboptimal post-PCI QFR outcome after stent implantation

Growing evidence shows an association between higher post-PCI quantitative flow ratios (QFR) and improved clinical prognosis, however, no models are available to predict suboptimal QFRs (< 0.91) after angiographically successful PCI. This study aims to establish a prediction nomogram for this domain.

This study included 450 vessels derived from 421 consecutive patients enrolled in the PIONEER IV trial, which were randomly assigned in a 1:1 ratio to a training (N = 225) and internal validation (N = 225) set, with external validation performed in 97 vessels from 95 consecutive patients enrolled in the ASET Japan trial. LASSO regression was used for optimal feature selection, and multivariate logistic regression was subsequently utilized to construct the nomogram. The performance of the nomograms was assessed and validated by area under the receiver operating characteristics curve (AUC), calibration curves, decision curve analysis, and clinical impact curves.

The nomogram was constructed incorporating a novel metric, quantitative flow ratio derived pullback pressure gradient (QFR-PPG), alongside four conventional parameters: left anterior descending artery disease, pre-procedural QFR, reference vessel diameter, and percent diameter stenosis. AUCs of the nomogram were 0.866 (95%CI:0.818–0.914), 0.784 (95% CI:0.722–0.847), and 0.781 (95% CI:0.682–0.879) in the training, internal validation and external validation sets, respectively. Bias-corrected curves revealed a strong consistency between actual observations and prediction.

The risk of a suboptimal post-PCI QFR in patients after angiographically successful PCI can be effectively predicted using a nomogram incorporating five variables available pre-PCI, with its performance and clinical predictive value confirming its utility in helping clinicians with decision-making and planning revascularization.

Registered on clinicaltrial.gov (NCT04923191 and NCT05117866).