Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 11/2020

01-11-2020 | CT Angiography | Computed Tomography

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

Authors: Hong Yan Qiao, Chun Xiang Tang, U. Joseph Schoepf, Christian Tesche, Richard R. Bayer 2nd, Dante A Giovagnoli, H. Todd Hudson Jr, Chang Sheng Zhou, Jing Yan, Meng Jie Lu, Fan Zhou, Guang Ming Lu, Jian Wei Jiang, Long Jiang Zhang

Published in: European Radiology | Issue 11/2020

Login to get access

Abstract

Objectives

This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFRCT) 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). FFRCT values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFRCT and severe stenosis on qualitative CCTA and ICA were also evaluated.

Results

After FFRCT 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), FFRCT ≤ 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 FFRCT could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions.

Conclusions

This study indicated ML-based FFRCT had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFRCT 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.
Appendix
Available only for authorised users
Literature
1.
Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477CrossRef
2.
Meijboom WB, Van Mieghem CA, Van PN et al (2008) Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina. J Am Coll Cardiol 52:636–643CrossRef
3.
Toth G, Hamilos M, Pyxaras S et al (2014) Evolving concepts of angiogram: fractional flow reserve discordances in 4000 coronary stenoses. Eur Heart J 35:2831–2838CrossRef
4.
De Bruyne B, Pijls NH, Kalesan B et al (2012) Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease. N Engl J Med 367:991–1001CrossRef
5.
Johnson NP, Toth GG, Lai D et al (2014) Prognostic value of fractional flow reserve: linking physiologic severity to clinical outcomes. J Am Coll Cardiol 64:1641–1654CrossRef
6.
Fearon WF, Nishi T, De Bruyne B et al (2018) Clinical outcomes and cost-effectiveness of fractional flow reserve-guided percutaneous coronary intervention in patients with stable coronary artery disease: three-year follow-up of the FAME 2 trial (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation). Circulation 137:480–487CrossRef
7.
Pothineni NV, Shah NS, Rochlani Y et al (2016) U.S. Trends in inpatient utilization of fractional flow reserve and percutaneous coronary intervention. J Am Coll Cardiol 67:732–733CrossRef
8.
Zhuang BY, Wang SL, Zhao SH, Lu M (2020) Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol 30:712–725CrossRef
9.
Rajani R, Webb J, Marciniak A, Preston R (2015) Comparative efficacy testing - fractional flow reserve by coronary computed tomography for the evaluation of patients with stable chest pain. Int J Cardiol 183:173–177CrossRef
10.
Norgaard BL, Leipsic J, Gaur S et al (2014) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 63:1145–1155CrossRef
11.
Eftekhari A, Min J, Achenbach S et al (2017) Fractional flow reserve derived from coronary computed tomography angiography: diagnostic performance in hypertensive and diabetic patients. Eur Heart J Cardiovasc Imaging 18:1351–1360CrossRef
12.
Yu MM, Lu ZG, Shen CX et al (2019) The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFRCT, or high-risk plaque features? Eur Radiol 29:3647–3657CrossRef
13.
Coenen A, Kim YH, Kruk M et al (2018) Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging 11:e007217CrossRef
14.
von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ et al (2018) Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol 29:2378–2387CrossRef
15.
Hicks KA, Tcheng JE, Bozkurt B (2015) 2014 ACC/AHA key data elements and definitions for cardiovascular endpoint events in clinical trials: a report of the American college of cardiology/American heart association task force on clinical data standards (writing committee to develop cardiovascular endpoints data standards). J Nucl Cardiol 22:1041–1144 1
16.
Zhou F, Tang CX, Schoepf UJ et al (2019) Fractional flow reserve derived from CCTA may have a prognostic role in myocardial bridging. Eur Radiol 29:3017–3026
17.
Curzen NP, Nolan J, Zaman AG, Norgaard BL, Rajani R (2016) Does the routine availability of CT-derived FFR influence management of patients with stable chest pain compared to CT angiography alone?: the FFRCT RIPCORD Study. JACC Cardiovasc Imaging 9:1188–1194CrossRef
18.
Tesche C, Vliegenthart R, Duguay TM et al (2017) Coronary computed tomographic angiography-derived fractional flow reserve for therapeutic decision making. Am J Cardiol 120:2121–2127CrossRef
19.
Park SJ, Kang SJ, Ahn JM et al (2012) Visual-functional mismatch between coronary angiography and fractional flow reserve. JACC Cardiovasc Interv 5:1029–1036CrossRef
20.
Liu X, Wang YB, Zhang HY et al (2019) Evaluation of fractional flow reserve in patients with stable angina: can CT compete with angiography? Eur Radiol 29:3669–3677CrossRef
21.
Siogkas PK, Anagnostopoulos CD, Liga R et al (2019) Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve. Eur Radiol 29:2117–2126CrossRef
22.
Levine GN, Bates ER, Blankenship JC et al (2011) 2011 ACCF/AHA/SCAI Guideline for percutaneous coronary intervention: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines and the Society for Cardiovascular Angiography and Interventions. Circulation 124:2574–2609CrossRef
23.
Neumann FJ, Sousa-Uva M, Ahlsson A et al (2019) 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J 40:87–165CrossRef
24.
Moss AJ, Williams MC, Newby DE, Nicol (2017) The updated NICE Guidelines: cardiac CT as the first-line test for coronary artery disease. Curr Cardiovasc Imaging Rep 10:15CrossRef
25.
Patel MR, Dai D, Hernandez AF et al (2014) Prevalence and predictors of nonobstructive coronary artery disease identified with coronary angiography in contemporary clinical practice. Am Heart J 167:846–852CrossRef
26.
Vavalle JP, Shen L, Broderick S, Shaw LK, Douglas PS (2016) Effect of the presence and type of angina on cardiovascular events in patients without known coronary artery disease referred for elective coronary angiography. JAMA Cardiol 1:232–234CrossRef
27.
Douglas PS, Hoffmann U, Patel MR et al (2015) Outcomes of anatomical versus functional testing for coronary artery disease. N Engl J Med 372:1291–1300CrossRef
28.
Jensen JM, Botker HE, Mathiassen ON et al (2018) Computed tomography derived fractional flow reserve testing in stable patients with typical angina pectoris: influence on downstream rate of invasive coronary angiography. Eur Heart J Cardiovasc Imaging 19:405–414CrossRef
29.
Douglas PS, Gianluca P, Hlatky MA et al (2015) Clinical outcomes of fractional flow reserve by computed tomographic angiography-guided diagnostic strategies vs. usual care in patients with suspected coronary artery disease: the prospective longitudinal trial of FFRCT: outcome and resource impacts study. Eur Heart J 36:3359–3367CrossRef
30.
Nielsen LH, Botker HE, Sorensen HT et al (2017) Prognostic assessment of stable coronary artery disease as determined by coronary computed tomography angiography: a Danish multicentre cohort study. Eur Heart J 38(6):413–421
31.
Finck T, Hardenberg J, Will A et al (2019) 10-year follow-up after coronary computed tomography angiography in patients with suspected coronary artery disease. JACC Cardiovasc Imaging 12:1330–1338CrossRef
32.
Ciccarelli G, Barbato E, Toth GG et al (2018) Angiography versus hemodynamics to predict the natural history of coronary stenoses: fractional flow reserve versus angiography in multivessel evaluation 2 substudy. Circulation 137:1475–1485CrossRef
33.
Ihdayhid AR, Norgaard BL, Gaur S et al (2019) Prognostic value and risk continuum of noninvasive fractional flow reserve derived from coronary CT angiography. Radiology 292:343–351CrossRef
34.
Norgaard BL, Terkelsen CJ, Mathiassen ON et al (2019) Coronary CT angiographic and flow reserve-guided management of patients with stable ischemic heart disease. J Am Coll Cardiol 72:2123–2134CrossRef
35.
Patel MR, Norgaard BL, Fairbairn TA et al (2020) 1-year impact on medical practice and clinical outcomes of FFRCT: the ADVANCE registry. JACC Cardiovasc Imaging 13:97–105CrossRef
36.
Tang CX, Wang YN, Zhou F et al (2019) Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: a multi-center study and meta-analysis. Eur J Radiol 116:90–97CrossRef
Metadata
Title
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
Authors
Hong Yan Qiao
Chun Xiang Tang
U. Joseph Schoepf
Christian Tesche
Richard R. Bayer 2nd
Dante A Giovagnoli
H. Todd Hudson Jr
Chang Sheng Zhou
Jing Yan
Meng Jie Lu
Fan Zhou
Guang Ming Lu
Jian Wei Jiang
Long Jiang Zhang
Publication date
01-11-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06964-w

Other articles of this Issue 11/2020

European Radiology 11/2020 Go to the issue

Magnetic Resonance

Evaluation of chronic carotid artery occlusion by non-contrast 3D-MERGE MR vessel wall imaging: comparison with 3D-TOF-MRA, contrast-enhanced MRA, and DSA

Chest

Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms

Correction

Correction to: Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging

Chest

Acute pulmonary embolism in non-hospitalized COVID-19 patients referred to CTPA by emergency department

Neuro

Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions

Musculoskeletal

The association between patellar tendon stiffness measured with shear-wave elastography and patellar tendinopathy—a case-control study