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The International Journal of Cardiovascular Imaging

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30.07.2018 | Original Paper

Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR

verfasst von: Xiuhua Hu, Minglei Yang, Lu Han, Yujiao Du

Erschienen in: The International Journal of Cardiovascular Imaging | Ausgabe 12/2018

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Abstract

To explore the diagnostic performance of a machine-learning-based (ML-based) computed fractional flow reserve (cFFR) derived from coronary computed tomography angiography (CCTA) in identifying ischemia-causing lesions verified by invasive FFR in catheter coronary angiography (ICA). We retrospectively studied 117 intermediate coronary artery lesions [40–80% diameter stenosis (DS)] from 105 patients (mean age 62 years, 32 female) who had undergone invasive FFR. CCTA images were used to compute cFFR values on the workstation. DS and the myocardium jeopardy index (MJI) of coronary stenosis were also assessed with CCTA. The diagnostic performance of cFFR was evaluated, including its correlation with invasive FFR and its diagnostic accuracy. Then, its performance was compared to that of combined DS and MJI. Of the 117 lesions, 36 (30.8%) had invasive FFR ≤ 0.80; 22 cFFR were measured as true positives and 74 cFFR as true negatives. The average time of cFFR assessment was 18 ± 7 min. The cFFR correlated strongly to invasive FFR (Spearman’s coefficient 0.665, p < 0.01). When diagnosing invasive FFR ≤ 0.80, the accuracy of cFFR was 82% with an AUC of 0.864, which was significantly higher than that of DS (accuracy 75%, AUC 0.777, p = 0.013). The AUC of cFFR was not significantly different from that of combined DS and MJI (0.846, p = 0.743). cFFR ≤ 0.80 based on CCTA showed good diagnostic performance for detecting ischemia-producing lesions verified by invasive FFR. The short calculation time required renders cFFR promising for clinical use.

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Titel: Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR
verfasst von: Xiuhua Hu
Minglei Yang
Lu Han
Yujiao Du
Publikationsdatum: 30.07.2018
Verlag: Springer Netherlands
Erschienen in: The International Journal of Cardiovascular Imaging / Ausgabe 12/2018
Print ISSN: 1569-5794
Elektronische ISSN: 1875-8312
DOI: https://doi.org/10.1007/s10554-018-1419-9

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Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR

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