AIMS: This study aimed to examine the influence of vessel volume on bolus thermodilution measurements.
METHODS: We prospectively included patients with angina with non-obstructive coronary arteries (ANOCA) undergoing bolus and continuous thermodilution assessments. All patients underwent coronary CT angiography to extract vessel volume. Coronary microvascular dysfunction was defined as coronary flow reserve (CFR)
OBJECTIVES: To validate the accuracy of virtual FFR pullback curves compared to wire-based FFR pullbacks and to assess their clinical utility using patient-reported outcomes.
METHODS: Pooled analysis of two prospective studies, including patients with hemodynamically significant (FFR ≤ 0.80) coronary artery disease (CAD). Virtual and wire-based FFR pullbacks were compared to assess the accuracy of virtual pullbacks to characterize CAD as focal or diffuse. Pullbacks were analyzed visually and quantitatively using the pullback pressure gradient (PPG). Patients underwent PCI, and the Seattle Angina Questionnaire (SAQ) was administered at 3-month follow-up.
RESULTS: A total of 298 patients (300 vessels) with both virtual and wire-based pullbacks who underwent PCI were included in the analysis. The mean age was 61.8 ± 8.8, and 15% were female. The agreement on the visual adjudication of the CAD pattern was fair (Cohen's Kappa: 0.31, 95% confidence interval: 0.18-0.45). The mean PPG were 0.65 ± 0.18 from virtual pullbacks and 0.65 ± 0.13 from wire-based pullbacks (r = 0.68, mean difference 0, limits of agreement -0.27 to 0.28). At follow-up, patients with high virtual PPG (>0.67) had higher SAQ angina frequency scores (i.e., less angina) than those with low virtual PPG (SAQ scores 92.0 ± 14.3 vs. 85.5 ± 23.1, p = 0.022).
CONCLUSION: Virtual FFR pullback curves showed moderate agreement with wire-based FFR pullbacks. Nonetheless, patients with focal disease based on virtual PPG reported greater improvement in angina after PCI.
OBJECTIVES: This study aims to characterize patients with ANOCA by measuring their minimal microvascular resistance and to examine the pattern of vascular remodeling associated with these measurements.
METHODS: The authors prospectively included patients with ANOCA undergoing continuous thermodilution assessment. Lumen volume and vessel-specific myocardial mass were quantified using coronary computed tomography angiography (CTA). CMD was defined as coronary flow reserve <2.5 and high minimal microvascular resistance as >470 WU.
RESULTS: A total of 153 patients were evaluated; 68 had CMD, and 22 of them showed high microvascular resistance. In patients with CMD, coronary flow reserve was 1.9 ± 0.38 vs 3.2 ± 0.81 in controls (P < 0.001). Lumen volume was significantly correlated with minimal microvascular resistance (r = -0.59 [95% CI: -0.45 to -0.71]; P < 0.001). In patients with CMD and high microvascular resistance, lumen volume was 40% smaller than in controls (512.8 ± 130.3 mm3 vs 853.2 ± 341.2 mm3; P < 0.001). Epicardial lumen volume assessed by coronary CTA was independently associated with minimal microvascular resistance (P < 0.001). The predictive capacity of lumen volume from coronary CTA for detecting high microvascular resistance showed an area under the curve of 0.79 (95% CI: 0.69-0.88).
CONCLUSIONS: Patients with CMD and high minimal microvascular resistance have smaller epicardial vessels than those without CMD. Coronary CTA detected high minimal microvascular resistance with very good diagnostic capacity. Coronary CTA could potentially aid in the diagnostic pathway for patients with ANOCA.
AIMS: The aim of this study was to evaluate the capacity of FFR-CT and CCTA to rule out significant lesions in high-risk NSTE-ACS patients, using ICA with invasive FFR as the gold standard.
METHODS: High-risk NSTE-ACS patients admitted to 4 European centres were enrolled in this single-arm, prospective core lab-adjudicated study. Patients underwent CCTA with FFR-CT analysis, followed by ICA with invasive FFR.
RESULTS: Out of the 250 initially planned NSTE-ACS patients, 168 were included, of whom 151 (92%) had sufficient CCTA image quality to undergo CCTA and FFR-CT analysis. The median high-sensitivity troponin T level at 1 hour post-hospitalisation was 5.3 (interquartile range: 1.8-18.6) times the upper reference limit. At the patient level, the diagnostic performance of FFR-CT was numerically higher as compared to CCTA though not statistically significant (sensitivity: 94% vs 93%, specificity: 63% vs 54%, positive predictive value: 83% vs 79%, negative predictive value: 85% vs 80% and accuracy: 83% vs 79%; p=0.58), suggesting an enhanced capability to avoid unnecessary ICA. At the lesion level, the ability of FFR-CT to detect significant lesions was significantly better than that of CCTA (receiver operating characteristic curves: 0.84 vs 0.65 respectively; p<0.01).
CONCLUSIONS: In patients with high-risk NSTE-ACS, FFR-CT offers better diagnostic accuracy - though not statistically significant - and a higher ability to rule out haemodynamically significant stenoses as compared to CCTA. This indicates that FFR-CT can reduce unnecessary invasive procedures by more accurately identifying patients requiring further intervention.
AIM: The primary objective is to determine the predictive capacity of the PPG for post-PCI FFR.
METHODS: This prospective, large-scale, controlled, investigator-initiated, multicenter study is enrolling patients with at least 1 lesion in a major epicardial vessel with a distal FFR ≤ 0.80 intended to be treated by PCI. The study will include 982 subjects. A standardized physiological assessment will be performed pre-PCI, including the online calculation of PPG from FFR pullbacks performed manually. PPG quantifies the CAD pattern by combining several parameters from the FFR pullback curve. Post-PCI physiology will be recorded using a standardized protocol with FFR pullbacks. We hypothesize that PPG will predict optimal PCI results (post-PCI FFR ≥ 0.88) with an area under the ROC curve (AUC) ≥ 0.80. Secondary objectives include patient-reported and clinical outcomes in patients with focal vs. diffuse CAD defined by the PPG. Clinical follow-up will be collected for up to 36 months, and an independent clinical event committee will adjudicate events.
RESULTS: Recruitment is ongoing and is expected to be completed in the second half of 2023.
CONCLUSION: This international, large-scale, prospective study with pre-specified powered hypotheses will determine the ability of the preprocedural PPG index to predict optimal revascularization assessed by post-PCI FFR. In addition, it will evaluate the impact of PPG on treatment decisions and the predictive performance of PPG for angina relief and clinical outcomes.
METHODS: This prospective, investigator-initiated, single-arm, multicenter study enrolled patients with at least one epicardial lesion with an FFR ≤0.80 scheduled for PCI. Manual FFR pullbacks were used to calculate PPG. The primary outcome of optimal revascularization was defined as an FFR ≥0.88 after PCI.
RESULTS: A total of 993 patients with 1044 vessels were included. The mean FFR was 0.68±0.12, PPG 0.62±0.17, and the post-PCI FFR was 0.87±0.07. PPG was significantly correlated with the change in FFR after PCI (r=0.65 [95% CI, 0.61-0.69]; P<0.001) and demonstrated excellent predictive capacity for optimal revascularization (area under the receiver operating characteristic curve, 0.82 [95% CI, 0.79-0.84]; P<0.001). FFR alone did not predict revascularization outcomes (area under the receiver operating characteristic curve, 0.54 [95% CI, 0.50-0.57]). PPG influenced treatment decisions in 14% of patients, redirecting them from PCI to alternative treatment modalities. Periprocedural myocardial infarction occurred more frequently in patients with low PPG (<0.62) compared with those with focal disease (odds ratio, 1.71 [95% CI, 1.00-2.97]).
CONCLUSIONS: Pathophysiologic coronary artery disease patterns distinctly affect the safety and effectiveness of PCI. PPG showed an excellent predictive capacity for optimal revascularization and demonstrated added value compared with an FFR measurement.
REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04789317.