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• W4384207893 abstract "Introduction and objectives: A new computed tomography-derived fractional flow reserve (CT-FFR) technique with a “coarse-to-fine subpixel” algorithm has been developed to generate precise lumen contours. The aim of this study was to assess the diagnostic performance of this new CT-FFR algorithm for discriminating lesion-specific ischemia using wire-based FFR ≤ 0.80 as the reference standard in patients with coronary artery disease. Methods: This prospective, multicenter study screened 330 patients undergoing coronary CT angiography (CCTA) and invasive FFR (median interval 2 days) from 6 tertiary hospitals. CT-FFR was evaluated in a blinded fashion with a “coarse-to-fine subpixel” algorithm for lumen contour. Results: Between March 2019 and May 2020, we included 316 patients with 324 vessels. There was a good correlation between CT-FFR and invasive FFR (r = 0.76, P < .001). The diagnostic sensitivity, specificity, and accuracy on a per-vessel level were 95.3%, 89.8%, and 92.0% for CT-FFR, and 96.4%, 26.4%, and 53.1% for CCTA > 50% stenosis, respectively. CT-FFR showed improved discrimination of ischemia compared with CCTA alone overall (AUC, 0.95 vs 0.74, P < .001) and in intermediate (AUC, 0.96 vs 0.62, P < .001) and “gray zone” lesions (AUC, 0.88 vs 0.61, P < .001). The diagnostic specificity, accuracy, and AUC for CT-FFR (71.9%, 82.8%, and 0.84) outperformed CCTA (9.4%, 48.3%, and 0.66) in patients or in vessels with severe calcification (all P < .05). Conclusions: CT-FFR with a new “coarse-to-fine subpixel” algorithm showed high performance in identifying hemodynamically significant stenosis. The diagnostic performance of CT-FFR was superior to that of CCTA in intermediate lesions, “gray zone” lesions, and severely calcified lesions. Clinical Trial Register: NCT04731285 Introducción y objetivos: Se ha desarrollado una nueva técnica basada en tomografía computarizada para la evaluación de la reserva fraccional de flujo (TC-RFF) con un algoritmo de subpíxel «de grueso a fino» para generar contornos luminales precisos. El objetivo de este estudio es evaluar el rendimiento diagnóstico de este nuevo algoritmo de TC-RFF para discriminar la isquemia específica de lesión utilizando la evaluación invasiva de la RFF ≤ 0,80 como referencia en pacientes con enfermedad coronaria. Métodos: Este estudio prospectivo y multicéntrico evaluó a 330 pacientes sometidos a angiografía coronaria no invasiva con TC (ACTC) y evaluación invasiva de la RFF (mediana del intervalo, 2 días) en 6 hospitales terciarios. La TC-RFF se evaluó a ciegas con un algoritmo de subpíxel «de grueso a fino» para la evaluación de la luz. Resultados: Entre marzo de 2019 y mayo de 2020, se incluyó a un total de 316 pacientes con 324 vasos. Hubo una buena correlación entre la TC-RFF y la evaluación invasiva de la RFF (r = 0,76; p < 0,001). La sensibilidad, la especificidad y la exactitud diagnóstica por vaso fueron, respectivamente, del 95,3, el 89,8 y el 92,0% para la TC-RFF y del 96,4, el 26,4 y el 53,1% para la ACTC para las estenosis > 50%. La TC-RFF mostró mejor discriminación de la isquemia que la ACTC sola en general (ABC = 0,95 frente a ABC = 0,74; p < 0,001) y en lesiones intermedias (ABC = 0,96 frente a ABC = 0,62; p < 0,001) y en «zona gris» (ABC = 0,88 frente a ABC = 0,61; p < 0,001). La especificidad, la exactitud y el ABC diagnóstica de la TC-RFF (el 71,9%, el 82,8% y 0,84) superaron las de la ACTC (el 9,4%, el 48,3% y 0,66) en pacientes o vasos con calcificación grave (todos, p < 0,05). Conclusiones: La TC-RFF con un algoritmo de subpíxel «de grueso a fino» proporcionó un alto rendimiento en la identificación de estenosis hemodinámicamente significativas. El rendimiento diagnóstico de la TC-RFF fue superior al de la ACTC en lesiones intermedias, de «zona gris» y con calcificación grave." @default.
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• W4384207893 date "2023-07-01" @default.
• W4384207893 modified "2023-10-16" @default.
• W4384207893 title "Diagnostic accuracy of CT-FFR with a new coarse-to-fine subpixel algorithm in detecting lesion-specific ischemia: a prospective multicenter study" @default.
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• W4384207893 doi "https://doi.org/10.1016/j.rec.2023.05.008" @default.
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• W4384207893 hasPublicationYear "2023" @default.
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