scholarly journals Predictive Value of Resting Pd/Pa for Fractional Flow Reserve Assessed with Monorail Pressure Microcatheter in Real-World Practice

2019 ◽  
Vol 4 (2) ◽  
pp. 113-120
Author(s):  
Keng Tat Koh ◽  
Asri Said ◽  
Khaw Chee Sin ◽  
Oon Yen Yee ◽  
Erwin Mulia ◽  
...  
Keyword(s):  
Fractional Flow Reserve ◽  
Real World ◽  
Predictive Value ◽  
Fractional Flow ◽  
Flow Reserve

scholarly journals Comparison of Resting Full-Cycle Ratio and Fractional Flow Reserve in a German Real-World Cohort

2021 ◽  
Vol 8 ◽  
Author(s):  
Hendrik Wienemann ◽  
Annika Meyer ◽  
Victor Mauri ◽  
Till Baar ◽  
Matti Adam ◽  
...  
Keyword(s):  
Fractional Flow Reserve ◽  
Real World ◽  
Predictive Value ◽  
Gold Standard ◽  
Morphological Characteristics ◽  
Quantitative Coronary Angiography ◽  
Coronary Intervention ◽  
University Hospital ◽  
Fractional Flow ◽  
Flow Reserve

Objective: The aim of this study was to evaluate non-hyperemic resting pressure ratios (NHPRs), especially the novel “resting full-cycle ratio” (RFR; lowest pressure distal to the stenosis/aortic pressure during the entire cardiac cycle), compared to the gold standard fractional flow reserve (FFR) in a “real-world” setting.Methods: The study included patients undergoing coronary pressure wire studies at one German University Hospital. No patients were excluded based on any baseline or procedural characteristics, except for insufficient quality of traces. The diagnostic performance of four NHPRs vs. FFR ≤ 0.80 was tested. Morphological characteristics of stenoses were analyzed by quantitative coronary angiography.Results: 617 patients with 712 coronary lesions were included. RFR showed a significant correlation with FFR (r = 0.766, p < 0.01). Diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of RFR were 78% (95% confidence interval = 75; 81), 72% (65; 78), 81% (77; 84), 63% (57; 69), and 86% (83; 89). Relevant predictors for discordance of RFR ≤ 0.89/FFR > 0.8 were LAD lesions, peripheral artery disease, age, female sex and non-focal stenoses. Predictors for discordance of RFR > 0.89/FFR ≤ 0.8 included non-LCX lesions, percent diameter stenosis and previous percutaneous coronary intervention in the target vessel. RFR and all other NHPRs were highly correlated with each other.Conclusion: All NHPRs have a similar correlation with the gold standard FFR and may facilitate the acceptance and implementation of physiological assessments of lesion severity. However, we found ~20% discordant results between NHPRs and FFR in our “all-comers” German cohort.

Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes

2021 ◽  
pp. 028418512098397
Author(s):  
Yang Li ◽  
Hong Qiu ◽  
Zhihui Hou ◽  
Jianfeng Zheng ◽  
Jianan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unstable Angina ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Flow Reserve ◽  
Sensitivity Specificity ◽  
Tomographic Angiography

Background Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR). Purpose To examine the ability of a DL-based CT-FFR in detecting hemodynamic changes of stenosis. Material and Methods This study included 73 patients (85 vessels) who were suspected of coronary artery disease (CAD) and received CCTA followed by invasive FFR measurements within 90 days. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristics curve (AUC) were compared between CT-FFR and CCTA. Thirty-nine patients who received drug therapy instead of revascularization were followed for up to 31 months. Major adverse cardiac events (MACE), unstable angina, and rehospitalization were evaluated and compared between the study groups. Results At the patient level, CT-FFR achieved 90.4%, 93.6%, 88.1%, 85.3%, and 94.9% in accuracy, sensitivity, specificity, PPV, and NPV, respectively. At the vessel level, CT-FFR achieved 91.8%, 93.9%, 90.4%, 86.1%, and 95.9%, respectively. CT-FFR exceeded CCTA in these measurements at both levels. The vessel-level AUC for CT-FFR also outperformed that for CCTA (0.957 vs. 0.599, P < 0.0001). Patients with CT-FFR ≤0.8 had higher rates of rehospitalization (hazard ratio [HR] 4.51, 95% confidence interval [CI] 1.08–18.9) and MACE (HR 7.26, 95% CI 0.88–59.8), as well as a lower rate of unstable angina (HR 0.46, 95% CI 0.07–2.91). Conclusion CT-FFR is superior to conventional CCTA in differentiating functional myocardial ischemia. In addition, it has the potential to differentiate prognoses of patients with CAD.

P6175Prediction of coronary revascularization by coronary computed tomography angiography derived fractional flow reserve - different algorithms for interpretation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K T Madsen ◽  
K T Veien ◽  
B L Noergaard ◽  
P Larsen ◽  
L Deibjerg ◽  
...  
Keyword(s):  
Chest Pain ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Coronary Revascularization ◽  
Standard Of Care ◽  
Fractional Flow ◽  
Coronary Cta ◽  
Flow Reserve ◽  
Stable Chest Pain

Abstract Introduction Coronary CT angiography (CTA) derived fractional flow reserve (FFRct) is increasingly used for guiding referral to invasive procedures in patients with stable chest pain. However, optimal interpretation of FFRct-analysis in terms of location and threshold of applied FFRct-values is unclear. Purpose To evaluate the clinical performance of various vessel-specific physiological FFRct derived measures of ischemia for prediction of standard of care guided coronary revascularization in patients with stable chest pain and coronary artery disease as determined by coronary CTA. Methods Retrospective study in patients with stable chest pain referred for coronary angiography based on coronary CTA. Standard acquired coronary CTA data sets were transmitted for core-laboratory analysis at HeartFlow. Any FFRct value in the major coronary arteries ≥1.8 mm in diameter, including side branches, were registered. Lesions were categorized as positive for ischemia using 6 different algorithms: Lowest in vessel FFRct-value (1) ≤0.75 or (2) ≤0.80; 2 cm distal-to-lesion FFRct-value (3) ≤0.75 or (4) ≤0.80; ΔFFRct (5) ≥0.06 or a combination of 2 and 5. The personnel responsible for downstream patient management had no information regarding FFRct test results. Results A total of 172 patients were included. Revascularization was performed in 62 (35%) patients. The diagnostic performance of different FFRct algorithms for predicting standard of care guided coronary revascularization is shown in the Table. Revascularization Predictions by FFRct N=172 Diagnostic performance FFRCT false negative FFRCT false positive Values given as (%) No. of revasc vessels No. of abnormal vessels FFRCT Algorithm Sens Spec PPV NPV Acc 1 2 3 1 2 3 Distal FFRCT ≤0.75 77 68 58 84 72 12 2 0 29 5 1 Distal FFRCT ≤0.80 92 43 48 90 61 5 0 0 40 20 3 Lesion-specific FFRCT ≤0.75 68 86 74 83 80 17 3 0 12 3 0 Lesion-specific FFRCT ≤0.80 82 78 68 89 80 10 2 0 21 3 1 ΔFFRCT ≥0.06 98 36 47 98 59 1 0 0 51 19 0 Combinationa 92 54 53 92 67 5 0 0 39 12 0 aDistal FFRCT ≤0.80 and ΔFFRCT ≥0.06. Sens = sensitivity; Spec = specificity; PPV = positive predictive value; NPV = negative predictive value; Acc = accuracy; FFRCT = fractional flow reserve derived from coronary CTA; ΔFFRCT = difference between FFRCT-value immediately proximal and distal to lesion; Revasc = revascularized. Conclusion The diagnostic performance of FFRct in terms of predicting standard of care guided coronary revascularization is dependent on the applied algorithm for interpretation of the FFRct-analysis.

AS-263: Fractional Flow Reserve and Long-Term Outcomes in the Real World

2010 ◽  
Vol 105 (9) ◽  
pp. 112A
Author(s):  
Aniket Puri ◽  
Michael Liang ◽  
Suresh Perera ◽  
Kirsty Abercrombie ◽  
Gerard Devlin
Keyword(s):  
Fractional Flow Reserve ◽  
Real World ◽  
Fractional Flow ◽  
Long Term Outcomes ◽  
The Real ◽  
Flow Reserve

scholarly journals TCT-331 Real-World Clinical Experience of Fractional Flow Reserve Assessed With a Rapid Exchange Pressure Sensor on a MicroCatheter

2014 ◽  
Vol 64 (11) ◽  
pp. B96
Author(s):  
Thierry Lefevre ◽  
Philippe Guyon ◽  
Stephan Fichtlscherer ◽  
Thomas Munzel ◽  
Volker Schächinger ◽  
...  
Keyword(s):  
Clinical Experience ◽  
Fractional Flow Reserve ◽  
Real World ◽  
Pressure Sensor ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Rapid Exchange

Representation Rates Following Negative Fractional Flow Reserve: Real-World Experience

2018 ◽  
Vol 27 ◽  
pp. S475
Author(s):  
C. Grisanti ◽  
M. Savage ◽  
K. Lam ◽  
N. Gaikwad ◽  
D. Walters
Keyword(s):  
Fractional Flow Reserve ◽  
Real World ◽  
Fractional Flow ◽  
Flow Reserve

scholarly journals Hybrid Instantaneous Wave-Free Ratio–Fractional Flow Reserve versus Fractional Flow Reserve in the Real World

2017 ◽  
Vol 4 ◽  
Author(s):  
Kara Shuttleworth ◽  
Kristina Smith ◽  
Jonathan Watt ◽  
Jamie A. L. Smith ◽  
Stephen J. Leslie
Keyword(s):  
Fractional Flow Reserve ◽  
Real World ◽  
Fractional Flow ◽  
The Real ◽  
Flow Reserve

scholarly journals Accuracy of computational pressure-fluid dynamics applied to coronary angiography to derive fractional flow reserve: FLASH FFR

2019 ◽  
Vol 116 (7) ◽  
pp. 1349-1356 ◽  
Author(s):  
Jianping Li ◽  
Yanjun Gong ◽  
Weimin Wang ◽  
Qing Yang ◽  
Bin Liu ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Coronary Artery ◽  
Coronary Angiography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Characteristic Curve ◽  
Fractional Flow ◽  
Performance Goal ◽  
Flow Reserve

Abstract Aims Conventional fractional flow reserve (FFR) is measured invasively using a coronary guidewire equipped with a pressure sensor. A non-invasive derived FFR would eliminate risk of coronary injury, minimize technical limitations, and potentially increase adoption. We aimed to evaluate the diagnostic performance of a computational pressure-flow dynamics derived FFR (caFFR), applied to coronary angiography, compared to invasive FFR. Methods and results The FLASH FFR study was a prospective, multicentre, single-arm study conducted at six centres in China. Eligible patients had native coronary artery target lesions with visually estimated diameter stenosis of 30–90% and diagnosis of stable or unstable angina pectoris. Using computational pressure-fluid dynamics, in conjunction with thrombolysis in myocardial infarction (TIMI) frame count, applied to coronary angiography, caFFR was measured online in real-time and compared blind to conventional invasive FFR by an independent core laboratory. The primary endpoint was the agreement between caFFR and FFR, with a pre-specified performance goal of 84%. Between June and December 2018, matched caFFR and FFR measurements were performed in 328 coronary arteries. Total operational time for caFFR was 4.54 ± 1.48 min. caFFR was highly correlated to FFR (R = 0.89, P = 0.76) with a mean bias of −0.002 ± 0.049 (95% limits of agreement −0.098 to 0.093). The diagnostic performance of caFFR vs. FFR was diagnostic accuracy 95.7%, sensitivity 90.4%, specificity 98.6%, positive predictive value 97.2%, negative predictive value 95.0%, and area under the receiver operating characteristic curve of 0.979. Conclusions Using wire-based FFR as the reference, caFFR has high accuracy, sensitivity, and specificity. caFFR could eliminate the need of a pressure wire, technical error and potentially increase adoption of physiological assessment of coronary artery stenosis severity. Clinical Trial Registration URL: http://www.chictr.org.cn Unique Identifier: ChiCTR1800019522.

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