Association between fractional flow reserve and Duke treadmill score in patients with single-vessel disease

2017 ◽  
pp. 877-883 ◽  
Author(s):  
Süleyman Kalaycı ◽  
Belma Kalaycı ◽  
Ekrem Şahan ◽  
Asiye Ayça Ayyılmaz
Keyword(s):  
Fractional Flow Reserve ◽  
Fractional Flow ◽  
Vessel Disease ◽  
Single Vessel ◽  
Flow Reserve ◽  
Duke Treadmill Score ◽  
Single Vessel Disease

Abstract 3332: Fractional Flow Reserve (FFR) And MIBI SPECT: A comparison In Patients With Multi-vessel Coronary Disease

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Narbeh Melikian ◽  
Pieter De Bondt ◽  
Thomas Cuisset ◽  
Eric Wyffels ◽  
Jozef Bartunek ◽  
...  
Keyword(s):  
Fractional Flow Reserve ◽  
Multivessel Disease ◽  
Vascular Territory ◽  
Weak Correlation ◽  
Fractional Flow ◽  
Vessel Disease ◽  
Segment Model ◽  
Flow Reserve ◽  
Perfusion Defects ◽  
Mibi Spect

INTRODUCTION In patients with angiographic 3 vessel disease only 29% have been reported to have perfusion defects. However the association between physiological evidence for ischemia per vessel derived from fractional flow reserve (FFR) and MIBI SPECT (SPECT) in multivessel disease (MVD) is unknown. We comapred this association in patients with MVD. METHOD In 84 vascular territories in 28 patients (mean age 63.9±9.8 years, LV ejection fraction 69±12%) with angiographic MVD (>50% stenosis in at least 2 vessels) results of SPECT (rest / stress adenosine) were compared to FFR measurements in each coronary vessel. AHA semi-quantitative 5 point scorning system using a 17-segment model was used to report SPECT scans. A FFR <0.80 was taken as evidence for ischemia. RESULTS MIBI was positive in at least 1 territory in 19 (67%) and FFR <0.80 in at least 1 territory in 23 (82%) of patients. 7 (25%) patients with no perfusion defect on SPECT had FFR <0.80 in at least 2 territories. The association between MIBI and FFR in each patient and per vascular territory are summarised below. Per patient there was no concordance between SPECT and FFR (Kappa −0.11) and per vascular territory there was poor concordance between SPECT and FFR (Kappa 0.23). In 8 (29%) patients both SPECT and FFR detected identical ischemic territories [mean number of ischemic territories: 1.5±0.9 for both; P=1.00]. In the remaining 20 patients in comparison to FFR, SPECT either underestimated [9 (32%) patients − mean number of ischemic territories, SPECT: 0.3±0.7, FFR: 1.9±0.6; P=0.001] or overestimated [11 (39%) patients -mean number of ischemic territories, MIBI: 2.0±1.0, FFR: 1.0±0.9; P=0.02] the number of ischemic territories. There was a weak correlation between severity of ischemia as assessed by SPECT in each vascular territory and the actual FFR value (r=0.34; P=0.001). CONCLUSION In patients with multivessel disease, the concordance between SPECT and FFR to localise hemodynamically significant stenosis is poor. Per Patient and per vascular territory comparison of MIBI SPECT and FFR

Abstract 14304: Assessing the Association of Adherence to Fractional Flow Reserve Treatment Thresholds and Outcomes of Patients With Coronary Artery Disease

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Maneesh Sud ◽  
Lu Han ◽  
Maria Koh ◽  
Peter Austin ◽  
Michael E Farkouh ◽  
...  
Keyword(s):  
Medical Therapy ◽  
Fractional Flow Reserve ◽  
Coronary Intervention ◽  
Routine Practice ◽  
Cardiac Events ◽  
Fractional Flow ◽  
St Segment Elevation ◽  
Single Vessel ◽  
Flow Reserve

Background: Although fractional flow reserve (FFR) thresholds have been established to guide the use of percutaneous coronary intervention (PCI) or medical therapy, little is known about the adherence to FFR thresholds for PCI in clinical practice and their association with clinical outcomes. Methods: Adults undergoing FFR assessment in a single vessel (excluding ST-segment elevation myocardial infarction [MI]) from April 1, 2013 to March 31, 2018 in Ontario, Canada were included. Patients were divided into two cohorts based on FFR ≤ 0.80 (ischemic) and > 0.80 (non-ischemic). Inverse probability of treatment weighting was used to balance confounders between patients treated with PCI vs. no PCI in each cohort. The primary outcome was major adverse cardiac events (MACE) defined by death, MI, unstable angina, or urgent revascularization. Results: We identified 9,106 patients who underwent single-vessel FFR measurement. Among the 2,693 patients with an ischemic FFR (mean age 65, 27.0% female), 75.3% of patients received PCI and 24.7% were treated only with medical therapy. Over a median follow-up of 2.6 years in the ischemic cohort, PCI was associated with a 20% lower rate of MACE compared to no PCI (24.0% vs. 31.6%; hazard ratio [HR]: 0.80, 95% CI: 0.66-0.96). However, among 6,413 patients with a non-ischemic FFR (mean age 66, 38.9% female), 12.6% received PCI and 87.4% were treated only with medical therapy. Over a median follow-up of 2.8 years in the non-ischemic cohort, PCI was associated with a 42% higher rate of MACE compared to no PCI (25.6% vs. 17.6%; HR: 1.42, 95% CI: 1.18-1.70). The increased rate of MACE was driven mainly by MI (HR 1.67, 95% CI: 1.20-2.31) but not death (HR 0.99, 95% CI: 0.72-1.35). Conclusions: In routine practice, we found 1 in 4 patients did not receive PCI for ischemic lesions while 1 in 8 received PCI for non-ischemic lesions. Performing PCI procedures according to recommended FFR cutoffs was associated with lower rates of clinical events.

scholarly journals Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve

2020 ◽  
Vol 234 (11) ◽  
pp. 1337-1350 ◽  
Author(s):  
Jason M Carson ◽  
Neeraj Kavan Chakshu ◽  
Igor Sazonov ◽  
Perumal Nithiarasu
Keyword(s):  
Neural Network ◽  
Fractional Flow Reserve ◽  
Virtual Patient ◽  
Fractional Flow ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Single Vessel ◽  
Flow Reserve ◽  
Patient Database ◽  
Real Patients

Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients.

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