Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis
Fractional flow reserve based on coronary CT angiography (CT-FFR) is gaining importance for non-invasive hemodynamic assessment of coronary artery disease (CAD). We evaluated the on-site CT-FFR with a machine learning algorithm (CT-FFRML) for the detection of hemodynamically significant coronary artery stenosis in comparison to the invasive reference standard of instantaneous wave free ratio (iFR®).
This study evaluated patients with CAD who had a clinically indicated coronary computed tomography angiography (cCTA) and underwent invasive coronary angiography (ICA) with iFR®-measurements. Standard cCTA studies were acquired with third-generation dual-source computed tomography and analyzed with on-site prototype CT-FFRML software.
We enrolled 40 patients (73% males, mean age 67 ± 12 years) who had iFR®-measurement and CT-FFRML calculation. The mean calculation time of CT-FFRML values was 11 ± 2 min. The CT-FFRML algorithm showed, on per-patient and per-lesion level, respectively, a sensitivity of 92% (95% CI 64–99%) and 87% (95% CI 59–98%), a specificity of 96% (95% CI 81–99%) and 95% (95% CI 84–99%), a positive predictive value of 92% (95% CI 64–99%), and 87% (95% CI 59–98%), and a negative predictive value of 96% (95% CI 81–99%) and 95% (95% CI 84–99%). The area under the receiver operating characteristic curve for CT-FFRML on per-lesion level was 0.97 (95% CI 0.91–1.00). Per lesion, the Pearson’s correlation between the CT-FFRML and iFR® showed a strong correlation of r = 0.82 (p < 0.0001; 95% CI 0.715–0.920).
On-site CT-FFRML correlated well with the invasive reference standard of iFR® and allowed for the non-invasive detection of hemodynamically significant coronary stenosis.
KeywordsCoronary artery disease Coronary CT angiography Fractional flow reserve derived from coronary computed tomography angiography Instantaneous wave-free ratio Invasive coronary angiography Myocardial ischemia
Coronary artery disease
Coronary computed tomography angiography
Fractional flow reserve derived from coronary computed tomography angiography
Fractional flow reserve derived from coronary computed tomography angiography based on machine learning algorithm
European Society of Cardiology
Fractional flow reserve
Invasive coronary angiography
Instantaneous wave free ratio
Supported by Siemens Healthineers for providing CT-FFRML software for research purposes, which is currently not commercially available. Furthermore, the authors would like to thank Philips Volcano Corporation (Koninklijke Philips N.V. Amsterdam, The Netherland) for their support.
UJS receives institutional research support and/or honoraria for consulting and speaking from Astellas, Bayer, Bracco, Elucid BioImaging, GE, Guerbet, HeartFlow, and Siemens. SB receives research support from Philips Volcano. All other authors declare that they have no financial disclosures. The presented CT-FFRML software is provided by Siemens and is currently not commercially available.
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