Physiological Assessment with iFR prior to FFR Measurement in Left Main Disease.

Despite guideline-based recommendation of the interchangeable use of instantaneous wave-free ratio (iFR) and fractional flow reserve (FFR) to guide revascularization decision-making, iFR/FFR could demonstrate different physiological or clinical outcomes in some specific patient or lesion subsets. Therefore, we sought to investigate the impact of difference between iFR and FFR-guided revascularization decision-making on clinical outcomes in patients with left main disease (LMD). In this international multicenter registry of LMD with physiological interrogation, we identified 275 patients in whom physiological assessment was performed with both iFR/FFR. Major adverse cardiovascular event (MACE) was defined as a composite of death, non-fatal myocardial infarction, and ischemia-driven target lesion revascularization. The receiver-operating characteristic analysis was performed for both iFR/FFR to predict MACE in respective patients in whom revascularization was deferred and performed. In 153 patients of revascularization deferral, MACE occurred in 17.0% patients. The optimal cut-off values of iFR and FFR to predict MACE were 0.88 (specificity:0.74; sensitivity:0.65) and 0.76 (specificity:0.81; sensitivity:0.46), respectively. The area under the curve (AUC) was significantly higher for iFR than FFR (0.74; 95%CI 0.62-0.85 vs. 0.62; 95%CI 0.48-0.75; p = 0.012). In 122 patients of coronary revascularization, MACE occurred in 13.1% patients. The optimal cut-off values of iFR and FFR were 0.92 (specificity:0.93; sensitivity:0.25) and 0.81 (specificity:0.047; sensitivity:1.00), respectively. The AUCs were not significantly different between iFR and FFR (0.57; 95%CI 0.40-0.73 vs. 0.46; 95%CI 0.31-0.61; p = 0.43). While neither baseline iFR nor FFR was predictive of MACE in patients in whom revascularization was performed, iFR-guided deferral seemed to be safer than FFR-guided deferral.

Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment.

Aims: Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity. Methods and results: A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, P < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, P < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console). Conclusion: An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.

Phasic flow patterns of right versus left coronary arteries in patients undergoing clinical physiological assessment.

BACKGROUND: Coronary blood flow in humans is known to be predominantly diastolic. Small studies in animals and humans suggest that this is less pronounced or even reversed in the right coronary artery (RCA). AIMS: This study aimed to characterise the phasic patterns of coronary flow in the left versus right coronary arteries of patients undergoing invasive physiological assessment. METHODS: We analysed data from the Iberian-Dutch-English Collaborators (IDEAL) study. A total of 482 simultaneous pressure and flow measurements from 301 patients were included in our analysis. RESULTS: On average, coronary flow was higher in diastole both at rest and during hyperaemia in both the RCA and LCA (mean diastolic-to-systolic velocity ratio [DSVR] was, respectively, 1.85±0.70, 1.76±0.58, 1.53±0.34 and 1.58±0.43 for LCArest, LCAhyp, RCArest and RCAhyp, p<0.001 for between-vessel comparisons). Although the type of RCA dominance affected the DSVR magnitude (RCAdom=1.55±0.35, RCAco-dom=1.40±0.27, RCAnon-dom=1.35; standard deviation not reported as n=3), systolic flow was very rarely predominant (DSVR was greater than or equal to 1.00 in 472/482 cases [97.9%] overall), with equal prevalence in the LCA. Stenosis severity or microvascular dysfunction had a negligible impact on DSVR in both the RCA and LCA (DSVR x hyperaemic stenosis resistance R2 =0.018, p=0.03 and DSVR x coronary flow reserve R2 <0.001, p=0.98). CONCLUSIONS: In patients with coronary artery disease undergoing physiological assessment, diastolic flow predominance is seen in both left and right coronary arteries. Clinical interpretation of coronary physiological data should therefore not differ between the left and the right coronary systems.

Cost-effectiveness analysis of percutaneous coronary intervention for single-vessel coronary artery disease: an economic evaluation of the ORBITA trial.

OBJECTIVE: To evaluate the cost-effectiveness of percutaneous coronary intervention (PCI) compared with placebo in patients with single-vessel coronary artery disease and angina despite anti-anginal therapy. DESIGN: A cost-effectiveness analysis comparing PCI with placebo. A Markov model was used to measure incremental cost-effectiveness, in cost per quality-adjusted life-years (QALYs) gained, over 12 months. Health utility weights were estimated using responses to the EuroQol 5-level questionnaire, from the Objective Randomised Blinded Investigation with optimal medical Therapy of Angioplasty in stable angina trial and UK preference weights. Costs of procedures and follow-up consultations were derived from Healthcare Resource Group reference costs and drug costs from the National Health Service (NHS) drug tariff. Probabilistic sensitivity analysis was undertaken to test the robustness of results to parameter uncertainty. Scenario analyses were performed to test the effect on results of reduced pharmaceutical costs in patients undergoing PCI, and the effect of patients crossing over from placebo to PCI due to refractory angina within 12 months. SETTING: Five UK NHS hospitals. PARTICIPANTS: 200 adult patients with stable angina and angiographically severe single-vessel coronary artery disease on anti-anginal therapy. INTERVENTIONS: At recruitment, patients received 6 weeks of optimisation of medical therapy for angina after which they were randomised to PCI or a placebo procedure. OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER) expressed as cost (in £) per QALY gained for PCI compared with placebo. RESULTS: The estimated ICER is £90 218/QALY gained when using PCI compared with placebo in patients receiving medical treatment for angina due to single-vessel coronary artery disease. Results were robust under sensitivity analyses. CONCLUSIONS: The ICER for PCI compared with placebo, in patients with single-vessel coronary artery disease and angina on anti-anginal medication, exceeds the threshold of £30 000 used by the National Institute of Health and Care Excellence when undertaking health technology assessment for the NHS context.Trial registration: The ORBITA study is registered with ClinicalTrials.gov, number NCT02062593.