Diagnostic accuracy of noninvasive fractional flow reserve derived from computed tomography angiography in ischemia-specific coronary artery stenosis and indeterminate lesions: results from a multicenter study in China

To determine the diagnostic performance of a novel computational fluid dynamics (CFD)-based algorithm for in situ CT-FFR in patients with ischemia-induced coronary artery stenosis. Additionally, we investigated whether the diagnostic accuracy of CT-FFR differs significantly across the spectrum of di...

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Published inFrontiers in cardiovascular medicine Vol. 10; p. 1236405
Main Authors Ding, Yaodong, Li, Quan, Zhang, Yang, Tang, Yida, Zhang, Haitao, Yang, Qing, Shou, Xiling, Ye, Yicong, Zhao, Xiliang, Ye, Yi, Zhang, Chao, Liu, Yuqi, Zeng, Yong
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 02.10.2023
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ISSN2297-055X
2297-055X
DOI10.3389/fcvm.2023.1236405

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Summary:To determine the diagnostic performance of a novel computational fluid dynamics (CFD)-based algorithm for in situ CT-FFR in patients with ischemia-induced coronary artery stenosis. Additionally, we investigated whether the diagnostic accuracy of CT-FFR differs significantly across the spectrum of disease and analyzed the influencing factors that contribute to misdiagnosis.BackgroundTo determine the diagnostic performance of a novel computational fluid dynamics (CFD)-based algorithm for in situ CT-FFR in patients with ischemia-induced coronary artery stenosis. Additionally, we investigated whether the diagnostic accuracy of CT-FFR differs significantly across the spectrum of disease and analyzed the influencing factors that contribute to misdiagnosis.Coronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR were performed on 324 vessels from 301 patients from six clinical medical centers. Local investigators used CCTA and ICA to conduct assessments of stenosis, and CT-FFR calculations were performed in the core laboratory. For CCTA and ICA, CT-FFR ≤ 0.8 and a stenosis diameter ≥ 50% were identified as lesion-specific ischemia. Univariate logistic regression models were used to assess the effect of features on discordant lesions (false negative and false positive) in different CT-FFR categories. The diagnostic performance of CT-FFR was analyzed using an invasive FFR ≤ 0.8 as the gold standard.MethodsCoronary computed tomography angiography (CCTA), invasive coronary angiography (ICA), and FFR were performed on 324 vessels from 301 patients from six clinical medical centers. Local investigators used CCTA and ICA to conduct assessments of stenosis, and CT-FFR calculations were performed in the core laboratory. For CCTA and ICA, CT-FFR ≤ 0.8 and a stenosis diameter ≥ 50% were identified as lesion-specific ischemia. Univariate logistic regression models were used to assess the effect of features on discordant lesions (false negative and false positive) in different CT-FFR categories. The diagnostic performance of CT-FFR was analyzed using an invasive FFR ≤ 0.8 as the gold standard.The Youden index indicated an optimal threshold of 0.80 for CT-FFR to identify functionally ischemic lesions. On a per-patient basis, the diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for CT-FFR were 96% (91%-98%), 92% (87%-96%), 94% (90%-96%), 91% (85%-95%), and 96% (92%-99%), respectively. The diagnostic efficacy of CT-FFR was higher than that of CCTA without the influence of calcification. Closer to the cut point, there was less certainty, with the agreement between the invasive FFR and the CT-FFR being at its lowest in the CT-FFR range of 0.7-0.8. In all lesions, luminal stenosis ≥ 50% significantly affected the risk of reduced false negatives (FN) and false positives (FP) results by CT-FFR, irrespective of the association with calcified plaque.ResultsThe Youden index indicated an optimal threshold of 0.80 for CT-FFR to identify functionally ischemic lesions. On a per-patient basis, the diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) for CT-FFR were 96% (91%-98%), 92% (87%-96%), 94% (90%-96%), 91% (85%-95%), and 96% (92%-99%), respectively. The diagnostic efficacy of CT-FFR was higher than that of CCTA without the influence of calcification. Closer to the cut point, there was less certainty, with the agreement between the invasive FFR and the CT-FFR being at its lowest in the CT-FFR range of 0.7-0.8. In all lesions, luminal stenosis ≥ 50% significantly affected the risk of reduced false negatives (FN) and false positives (FP) results by CT-FFR, irrespective of the association with calcified plaque.In summary, CT-FFR based on the new parameter-optimized CFD model has a better diagnostic performance than CTA for lesion-specific ischemia. The presence of calcified plaque has no significant effect on the diagnostic performance of CT-FFR and is independent of the degree of calcification. Given the range of applicability of our software, its use at a CT-FFR of 0.7-0.8 requires caution and must be considered in the context of multiple factors.ConclusionsIn summary, CT-FFR based on the new parameter-optimized CFD model has a better diagnostic performance than CTA for lesion-specific ischemia. The presence of calcified plaque has no significant effect on the diagnostic performance of CT-FFR and is independent of the degree of calcification. Given the range of applicability of our software, its use at a CT-FFR of 0.7-0.8 requires caution and must be considered in the context of multiple factors.
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Reviewed by: Simran Sharma, Erasmus Medical Center, Netherlands Zoltan Ruzsa, University of Szeged, Hungary
Abbreviations 3D, three-dimensional; AI, artificial intelligence; AS, Agatston scores; AUC, characteristic curve; CABG, coronary artery bypass grafting; CAC, coronary artery calcification; CACS, coronary artery calcification score; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CFD, computational fluid dynamics; CFD, computational fluid dynamics; CI, confidence interval; CTA, computed tomography; CT-FFR, computed tomography-derived fractional flow reserve; DS, diameter stenosis; ECG, electrocardiogram; FFR, flow reserve fraction; FN, false negatives; FP, false positives; ICA, invasive coronary angiography; ICD, implantable cardioverter defibrillator; IQR, interquartile range; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic curve; SD, standard deviation.
Edited by: Alexander Hirsch, Erasmus Medical Center, Netherlands
ISSN:2297-055X
2297-055X
DOI:10.3389/fcvm.2023.1236405