CT FFR for Ischemia-Specific CAD With a New Computational Fluid Dynamics Algorithm

Tang, Chunbo; Liu, Chun Yu; Lu, Minghui; Schoepf, U. Joseph; Tesche, Christian; Bayer, Richard R.; Hudson, H. Todd; Zhang, Xiao Lei; Li, Jian Hua; Wang, Yi Ning; Zhou, Chang Sheng; Zhang, Jia Yin; Yu, Mengchao; Hou, Yang; Zheng, Minjuan; Zhang, Bo; Zhang, Dai‐Min; Yi, Yan; Ren, Yuan; Li, Chen Wei; Zhao, Xiaoyan; Lu, Guang Ming; Hu, Xinghao; Xu, Lei; Zhang, Long Jiang

doi:10.1016/j.jcmg.2019.06.018

Cited by 104 publications

(77 citation statements)

References 33 publications

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“…Several techniques for computing fractional flow reserve (FFR) based on images acquired from coronary computed tomography angiography (CCTA) have been developed [1] , [2] , [3] , [4] , [5] , [6] , demonstrating incremental diagnostic value in large-scale multicenter studies. However, the specificity and positive predictive value (PPV) tended to be low compared to the sensitivity and negative predictive value (NPV) [4] , [5] , [6] .…”

Section: Introductionmentioning

confidence: 99%

Comparison of diagnostic performance in on-site based CT-derived fractional flow reserve measurements

Nozaki

Fujimoto

Aoshima

et al. 2021

IJC Heart & Vasculature

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Background Computed tomography fractional flow reserve (CT-FFR), which can be acquired on-site workstation using fluid structure interaction during the multiple optimal diastolic phase, has an incremental diagnostic value over conventional coronary computed tomography angiography (CCTA). However, the appropriate location for CT-FFR measurement remains to be clarified. Method A total of 115 consecutive patients with 149 vessels who underwent CCTA showing 30–90% stenosis with invasive FFR within 90 days were retrospectively analyzed. CT-FFR values were measured at three points: 1 and 2 cm distal to the target lesion (CT-FFR 1cm, 2cm ) and the vessel terminus (CT-FFR lowest ). The diagnostic accuracies of CT-FFR ≤ 0.80 for detecting hemodynamically significant stenosis, defined as invasive FFR ≤ 0.80, were compered. Result Fifty-five vessels (36.9%) had invasive FFR ≤ 0.80. The accuracy and AUC for CT-FFR 1cm and 2cm were comparable, while the AUC for CT-FFR lowest was significantly lower than CT-FFR 1cm and 2cm . (lowest/1cm, 2 cm = 0.68 (95 %CI 0.63–0.73) vs 0.79 (0.72–0.86, p = 0.006), 0.80 (0.73–0.87, p = 0.002)) The sensitivity and negative predictive value of CT-FFR lowest were 100%. The reclassification rates from positive CT-FFR lowest to negative CT-FFR 1cm and 2cm were 55.7% and 54.2%, respectively. Conclusion The diagnostic performance of CT-FFR was comparable when measured at 1-to-2 cm distal to the target lesion, but significantly higher than CT-FFR lowest . The lesion-specific CT-FFR could reclassify false positive cases in patients with positive CT-FFR lowest , while all patients with negative CT-FFR lowest were diagnosed as negative by invasive FFR.

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Section: Introductionmentioning

confidence: 99%

Comparison of diagnostic performance in on-site based CT-derived fractional flow reserve measurements

Nozaki

Fujimoto

Aoshima

et al. 2021

IJC Heart & Vasculature

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“…The highest AUC of ML-based cFFR was 0.84. A more recent CT-FFR study (μCT-FFR) using transluminal attenuation gradient (TAG) to define boundary conditions also showed good results, with the diagnostic accuracy of 91% with sensitivity of 89% and specificity of 91% [25]. Besides CFD-based approaches, Gao et.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve

Jiang

Pan

Hu³

et al. 2021

BioMed Eng OnLine

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Background Fractional flow reserve (FFR) is a widely used gold standard to evaluate ischemia-causing lesions. A new method of non-invasive approach, termed as AccuFFRct, for calculating FFR based on coronary computed tomography angiography (CCTA) and computational fluid dynamics (CFD) has been proposed. However, its diagnostic accuracy has not been validated. Objectives This study sought to present a novel approach for non-invasive computation of FFR and evaluate its diagnostic performance in patients with coronary stenosis. Methods A total of 54 consecutive patients with 78 vessels from a single center who underwent CCTA and invasive FFR measurement were retrospectively analyzed. The CT-derived FFR values were computed using a novel CFD-based model (AccuFFRct, ArteryFlow Technology Co., Ltd., Hangzhou, China). Diagnostic performance of AccuFFRct and CCTA in detecting hemodynamically significant coronary artery disease (CAD) was evaluated using the invasive FFR as a reference standard. Results Diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for AccuFFRct in detecting FFR ≤ 0.8 on per-patient basis were 90.7, 89.5, 91.4, 85.0 and 94.1%, respectively, while those of CCTA were 38.9, 100.0, 5.71, 36.5 and 100.0%, respectively. The correlation between AccuFFRct and FFR was good (r = 0.76 and r = 0.65 on per-patient and per-vessel basis, respectively, both p < 0.0001). Area under the curve (AUC) values of AccuFFRct for identifying ischemia per-patient and per-vessel basis were 0.945 and 0.925, respectively. There was much higher accuracy, specificity and AUC for AccuFFRct compared with CCTA. Conclusions AccuFFRct computed from CCTA images alone demonstrated high diagnostic performance for detecting lesion-specific ischemia, it showed superior diagnostic power than CCTA and eliminated the risk of invasive tests, which could be an accurate and time-efficient computational tool for diagnosing ischemia and assisting clinical decision-making.

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“…The analysis of non-invasive FFRCT was performed with a semi-automated software, and the clinical feasibility of it had been demonstrated before [7]. FFRCT had shown a higher diagnostic performance compared with CCTA, mainly contributed to an improvement in the speci city and positive predictive value, especially with a substantial 41% reduction in false positive ndings [19].…”

Section: Discussionmentioning

confidence: 99%

“…Evaluation of FFRCT was performed by an operator (BLINDED, 10 years in cardiac CT) using a dedicated software (uCT-FFR version 1.5, United Imaging Healthcare, Shanghai, China). FFRCT values were derived from CCTA data as following steps: 1) anatomic model reconstruction; 2) centerline de nition; 3) boundary condition; and 4) uCT-FFR calculation [7]. Another radiologist (BLINDED, 15 years in CT postprocessing) reviewed the post-processed images to verify the value of FFRCT was properly calculated.…”

Section: Image Analysismentioning

confidence: 99%

Distribution of FFRCT in Single Obstructive Coronary Stenosis and Predictors for Major Adverse Cardiac Events: A Propensity Score Matching Study

Jin

Jin²,

et al. 2021

Preprint

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Background: Fractional flow reserve derived from computed tomography (FFRCT) has been demonstrated significantly improved identification of lesion-specific ischemia compared with coronary computed tomography angiography (CCTA). It remains unclear the distribution of FFRCT values in obstructive stenosis between patients who received percutaneous coronary intervention (PCI) or not in routine clinical practice, as well as its association with clinical outcome. This study aims to reveal the distribution of FFRCT value in patients with single obstructive coronary artery stenosis, and explored the independent factors for predicting major adverse cardiac events (MACE). Methods: This was a retrospective study of adults with non-ST-segment elevation acute coronary syndrome undergoing FFRCT assessment by using CCTA data from January 1, 2016 to December 31, 2020. Propensity score matching (PSM) method was used to account for patient selection bias. The risk factors for predicting MACE were evaluated by a Cox proportional hazards regression analysis. Results: Overall, 655 patients with single obstructive (³ 50%) stenosis shown on CCTA were enrolled and divided into PCI group (376 cases) and conservative group (279 cases) according to treatment. The PSM cohort analysis demonstrated that the difference in history of unstable angina, CCSC and FFRCT between PCI group (188 cases) and conservative group (315 cases) was statistically significant, with all P values < 0.05, while the median follow-up time between them was no statistically significant (24 months vs 22.5months, P = 0.912). The incidence of MACE in PCI group and conservative group were 14.9% (28/188) and 23.5% (74/315) respectively, P = 0.020. Multivariate analysis of Cox proportional hazards regression revealed that history of unstable angina (adjusted odds ratio (adjOR), 3.165; 95% confidence interval (CI), 2.087-4.800; P < 0.001), FFRCT £ 0.8 (OR, 1.632;95% CI, 1.095-2.431; P = 0.016), and PCI therapy (OR, 0.481; 95% CI, 0.305-0.758) were the independent factors for MACE. Conclusions: History of unstable angina and FFRCT value of £ 0.8 were the independent risk factors for MACE, while PCI therapy was the independent protective factor for MACE.

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CT FFR for Ischemia-Specific CAD With a New Computational Fluid Dynamics Algorithm

Cited by 104 publications

References 33 publications

Comparison of diagnostic performance in on-site based CT-derived fractional flow reserve measurements

Comparison of diagnostic performance in on-site based CT-derived fractional flow reserve measurements

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve

Distribution of FFRCT in Single Obstructive Coronary Stenosis and Predictors for Major Adverse Cardiac Events: A Propensity Score Matching Study

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