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On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm - Clinical Effectiveness in a Retrospective Multicenter Cohort.
- Source :
-
Circulation journal : official journal of the Japanese Circulation Society [Circ J] 2019 Jun 25; Vol. 83 (7), pp. 1563-1571. Date of Electronic Publication: 2019 Jun 08. - Publication Year :
- 2019
-
Abstract
- Background: This study evaluated the diagnostic capability of on-site coronary computed tomography-derived computational fractional flow reserve (CT-FFR) determinations for detecting coronary artery disease (CAD), as assessed by invasive fractional flow reserve (FFR).<br />Methods and results: Seventy-four patients with coronary artery calcium scores <1,500 who underwent coronary CT angiography (CTA) and invasive FFR measurements within 90 days were retrospectively reviewed. CT-FFR was computed using a prototype machine-learning (ML) algorithm in 91 vessels; 47 vessels of 42 patients were determined to have significant CAD (FFR ≤0.8). Correlation between CT-FFR and FFR was good (r=0.786, P<0.001). Per-vessel area under the curve was significantly larger for CT-FFR (0.907, 95% confidence interval: 0.828-0.958) than for CTA stenosis ≥50% (0.595, 0.487-0.697) or ≥70% (0.603, 0.495-0.705) (both P<0.001). Standard coronary CTA classifications recommended further functional tests in 57 patients with moderate or worse stenosis on CTA. CT-FFR analysis (mean analysis time: 16.4±7.5 min) corrected the standard coronary CTA classification in 18 of 74 patients and confirmed it in 45 of 74 patients. Thus, the per-patient diagnostic accuracy of the classifications was improved from 66% (54-77%) to 85% (75-92%).<br />Conclusions: On-site CT-FFR based on a ML algorithm can provide good diagnostic performance for detecting hemodynamically significant CAD, suggesting the high value of coronary CTA for selected patients in clinical practice.
Details
- Language :
- English
- ISSN :
- 1347-4820
- Volume :
- 83
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Circulation journal : official journal of the Japanese Circulation Society
- Publication Type :
- Academic Journal
- Accession number :
- 31178524
- Full Text :
- https://doi.org/10.1253/circj.CJ-19-0163