METHODS: The present study included 812 patients in the ABSORB EXTEND study in which a total of 215 diabetic patients were treated with Absorb BVS. In addition, 882 diabetic patients treated with EES in pooled data from the SPIRIT clinical program (SPIRIT II, SPIRIT III and SPIRIT IV trials) were used for comparison by applying propensity score matching using 29 different variables. The primary endpoint was ischemia driven major adverse cardiac events (ID-MACE), including cardiac death, myocardial infarction (MI), and ischemia driven target lesion revascularization (ID-TLR).

RESULTS: After 2 years, the ID-MACE rate was 6.5% in the Absorb BVS vs. 8.9% in the Xience group (P = 0.40). There was no difference for MACE components or definite/probable device thrombosis (HR: 1.43 [0.24,8.58]; P = 0.69). The occurrence of MACE was not different for both diabetic status (insulin- and non-insulin-requiring diabetes) in all time points up to the 2-year follow-up for the Absorb and Xience groups.

AIMS: This study aimed to examine the influence of vessel volume on bolus thermodilution measurements.

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).

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.

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.