Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease

• Published in: European radiology 2020-11, Vol.30 (11), p.5841-5851
• Main Authors: Qiao, Hong Yan, Tang, Chun Xiang, Schoepf, U. Joseph, Tesche, Christian, Bayer, Richard R., Giovagnoli, Dante A, Todd Hudson, H., Zhou, Chang Sheng, Yan, Jing, Lu, Meng Jie, Zhou, Fan, Lu, Guang Ming, Jiang, Jian Wei, Zhang, Long Jiang
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computed Tomography; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary Artery Disease - diagnosis; Coronary Artery Disease - physiopathology; Coronary Artery Disease - therapy; Decision Making; Diagnostic Radiology; Disease Management; Female; Fractional Flow Reserve, Myocardial - physiology; Humans; Imaging; Internal Medicine; Interventional Radiology; Machine Learning; Male; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Predictive Value of Tests; Radiology; Retrospective Studies; Severity of Illness Index; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-06964-w

Objectives This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFR CT ) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD). Methods One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFR CT values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFR CT and severe stenosis on qualitative CCTA and ICA were also evaluated. Results After FFR CT results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4–48 months), FFR CT ≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11); p < 0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73), p = 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14, p = 0.045). Reserving ICA and revascularization for vessels with positive FFR CT could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions. Conclusions This study indicated ML-based FFR CT had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFR CT may direct therapeutic decision-making with the potential to improve efficiency of ICA. Key Points • ML-based FFR CT shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA. • FFR CT noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD. • Reserving ICA and revascularization for vessels with positive FFR CT may reduce the normalcy rate of ICA and improve its efficiency.