The Clinical Significance of Physiological Assessment of Residual Ischemia After Percutaneous Coronary Intervention

Impact of Relative Improvement in Quantitative Flow Ratio on Clinical Outcomes After Percutaneous Coronary Intervention - A Subanalysis of the PANDA III Trial

BACKGROUND

The clinical impact of relative improvements in coronary physiology in patients receiving percutaneous coronary intervention (PCI) for coronary artery disease (CAD) remains undetermined.Methods and Results: The quantitative flow ratio (QFR) recovery ratio (QRR) was calculated in 1,424 vessels in the PANDA III trial as (post-PCI QFR-pre-PCI QFR)/(1-pre-PCI QFR). The primary endpoint was the 2-year vessel-oriented composite endpoint (VOCE; a composite of vessel-related cardiac death, vessel-related non-procedural myocardial infarction, and ischemia-driven target vessel revascularization). Study vessels were dichotomously stratified according to the optimal QRR cut-off value. During the 2-year follow-up, 41 (2.9%) VOCEs occurred. Low (<0.86) QRR was associated with significantly higher rates of 2-year VOCEs than high (≥0.86) QRR (6.6% vs. 1.4%; adjusted hazard ratio [aHR] 5.05; 95% confidence interval [CI] 2.53-10.08; P<0.001). Notably, among vessels with satisfactory post-procedural physiological results (post-PCI QFR >0.89), low QRR also conferred an increased risk of 2-year VOCEs (3.7% vs. 1.4%; aHR 3.01; 95% CI 1.30-6.94; P=0.010). Significantly better discriminant and reclassification performance was observed after integrating risk stratification by QRR and post-PCI QFR to clinical risk factors (area under the curve 0.80 vs. 0.71 [P=0.010]; integrated discrimination improvement 0.05 [P<0.001]; net reclassification index 0.64 [P<0.001]).

CONCLUSIONS

Relative improvement of coronary physiology assessed by QRR showed applicability in prognostication. Categorical classification of coronary physiology could provide information for risk stratification of CAD patients.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation

OBJECTIVES

To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFR_CT) in patients who have undergone stents implantation.

METHODS

Firstly, the feasibility of FFR_CT in stented vessels was validated. The diagnostic performance of FFR_CT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFR_CT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFR_CT measurements (FFR_CT, ΔFFR_CT, ΔFFR_CT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFR_CT. The primary endpoint was major adverse cardiovascular events (MACE).

RESULTS

Per-patient accuracy of FFR_CT was 0.85 in identifying hemodynamically ISR. FFR_CT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFR_CT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032-1.177]; p = 0.004) and follow-up ΔFFR_CT/length (HR, 1.014 [95% CI, 1.006-1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594-0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846.

CONCLUSIONS

Noninvasive ML-based FFR_CT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients.

KEY POINTS

• Machine-learning-based FFR_CT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation. • Follow-up △FFR_CT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFR_CT in patients with moderate-to-high or high risk needs to be further studied. • FFR_CT might refine the clinical pathway of patients with stent implantation to invasive catheterization.