Anonymous Comparison of Various Angiography-Derived Fractional Flow Reserve Software With Pressure-Derived Physiological Assessment

Ninomiya, Kai; Serruys, Patrick W.; Kotoku, Nozomi; Zhou, Jinying; Kageyama, Shigetaka; Masuda, Shinichiro; Revaiah, Pruthvi C; Wang, Bo; He, Xinming; Tsai, Tsung-Ying; Kageyama, Momoko; Sevestre, Emelyne; Sharif, Faisal; Garg, Scot; Escaned, Javier; Patel, Manesh R.; Onuma, Yoshinobu

doi:10.1016/j.jcin.2023.04.026

Cited by 18 publications

(3 citation statements)

References 26 publications

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“…The CatLet score calculation can be completed solely based on the results of coronary angiography and during the procedure within 3–5 min for a trained interventionist and without additional costs, which is in sharp contrast with the most prevalently used pressure wire FFR or its derivatives ( 25 - 28 ). A previous study showed that those with ≥3 abnormal myocardial segments had a reduced 6-year survival rate ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve

Wang,

Xu,

et al. 2024

Quant Imaging Med Surg

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Background Pressure wire fractional flow reserve (FFR) and its derivatives, such as quantitative flow ratio (QFR), computational pressure flow-derived FFR (caFFR), coronary angiography-derived FFR (FFR angio ), and computed tomography-derived FFR (FFR CT ), have been validated for identifying functionally significant stenosis and guiding revascularization strategy. The limitations of using these methods include the side effects of hyperemia-induced agents, additional costs, and vulnerability to microvascular resistance. FFR is related both to the degree of a stenotic coronary artery and to its subtended myocardial territory. Coronary Artery Tree Description and Lesion Evaluation (CatLet) score (also known as Hexu ) is a product of the degree of a stenosis and the weighting of the affected coronary artery (myocardial territory). Hence, we hypothesized that the CatLet score could predict hemodynamically significant coronary stenosis. Methods We retrospectively enrolled consecutive patients with stable coronary artery disease. They attended Sichuan Science City Hospital with at least one lesion of 50-90% diameter stenosis in a coronary artery of 2 mm or larger. FFR measurement was obtained during invasive coronary angiography. The CatLet score was obtained by multiplying a fixed value of 2.0 (for non-occlusive lesions) and the weight of the affected coronary artery. The primary endpoint was the CatLet score’s diagnostic accuracy in identifying hemodynamically significant coronary stenosis, with a pressure wire FFR of ≤0.80 being used as reference. Results We analyzed the FFR and CatLet scores from 206 vessels in 175 patients with stable coronary disease and intermediate coronary lesions. The per-vessel analysis revealed an overall good correlation between the CatLet score and the FFR [r=–0.61; 95% confidence interval (95% CI): –0.69 to –0.52; P<0.01]. We also noted a significant CatLet score-FFR correlation for each of the left anterior descending artery (LAD), left circumflex (LCX), and right coronary artery (RCA). With a CatLet score ≥10 as a predictor of FFR ≤0.80, the overall diagnostic accuracy included a sensitivity of 78.80% (95% CI: 67.00–87.90%), a specificity of 85.00% (95% CI: 78.00–90.50%), a positive likelihood ratio of 5.25, a negative likelihood ratio of 0.25, and an area under the curve of 0.90 (95% CI: 0.85–0.94). Equivalent vessel-specific results were also achieved for each of the LAD, LCX, and RCA. Conclusions The CatLet score, solely based on visual estimation of the results of coronary angiography, demonstrated good diagnostic performance with respect to myocardial ischemia. Its clinical values in guiding revascularization warrant further investigation.

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Section: Discussionmentioning

confidence: 99%

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve

Wang,

Xu,

et al. 2024

Quant Imaging Med Surg

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“…An independent comparison study [27] was done for 5 software/methods of angio-FFR estimation on a large dataset. The ROC analysis was performed for each method for detecting the FFR ≤ 0.8 and the AUC values were between 0.73 and 0.75, which are lower than the previously diagnostic performances reported in their validation studies.…”

Section: Machine Learning In Stenosis Classificationmentioning

confidence: 99%

Assessment of the functional severity of coronary lesions from optical coherence tomography based on ensembled learning

Tache,

Hatfaludi,

Puiu

et al. 2023

BioMed Eng OnLine

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Background Atherosclerosis is one of the most frequent cardiovascular diseases. The dilemma faced by physicians is whether to treat or postpone the revascularization of lesions that fall within the intermediate range given by an invasive fractional flow reserve (FFR) measurement. The paper presents a monocentric study for lesions significance assessment that can potentially cause ischemia on the large coronary arteries. Methods A new dataset is acquired, comprising the optical coherence tomography (OCT) images, clinical parameters, echocardiography and FFR measurements collected from 80 patients with 102 lesions, with stable multivessel coronary artery disease. Having the ground truth given by the invasive FFR measurement, the dataset is challenging because almost 40% of the lesions are in the gray zone, having an FFR value between 0.75 and 0.85. Twenty-six features are extracted from OCT images, clinical characteristics, and echocardiography and the most relevant are identified by examining the models’ accuracy. An ensembled learning is performed for solving the binary classification problem of lesion significance considering the leave-one-out cross-validation approach. Results Ensemble models are designed from the multi-features voting from 5 features models by prediction aggregation with a maximum accuracy of 81.37% and a maximum area under the curve score (AUC) of 0.856. Conclusions The proposed explainable supervised learning-based lesion classification is a new method that can be improved by training with a larger multicenter dataset for further designing a tool for guiding the decision making of the clinician for the cases outside the gray zone and for the other situation extra clinical information about the lesion is needed.

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“…However, it is still uncertain whether imaging-based FFR could completely take the place of wire-based FFR, considering the lower diagnostic accuracy in real-world practice compared with that in the earlier phase validation studies. 6 Furthermore, the clinical value of this technology would also be determined by its utility in managing the subset of complex lesions in coronary artery disease, as the temporal trends in patients who need revascularization exhibited increased anatomic lesion complexity. 7,8 To date, few studies have handled elucidating the clinical utility of imaging-based FFR in complex coronary lesions.…”

mentioning

confidence: 99%

Imaging-Based FFR: How Many Steps Toward Replacement for Wire-Based FFR on A Journey of A Thousand Miles

Warisawa,

Akasaka

2024

Circ: Cardiovascular Imaging

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Anonymous Comparison of Various Angiography-Derived Fractional Flow Reserve Software With Pressure-Derived Physiological Assessment

Cited by 18 publications

References 26 publications

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve

Assessment of the functional severity of coronary lesions from optical coherence tomography based on ensembled learning

Imaging-Based FFR: How Many Steps Toward Replacement for Wire-Based FFR on A Journey of A Thousand Miles

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