Author + information
- Received October 18, 2013
- Revision received November 21, 2013
- Accepted November 27, 2013
- Published online April 1, 2014.
- Bjarne L. Nørgaard, MD, PhD∗∗ (, )
- Jonathon Leipsic, MD, PhD†,
- Sara Gaur, MD∗,
- Sujith Seneviratne, MBBS‡,
- Brian S. Ko, MBBS, PhD‡,
- Hiroshi Ito, MD, PhD§,
- Jesper M. Jensen, MD, PhD∗,
- Laura Mauri, MD, PhD‖,
- Bernard De Bruyne, MD, PhD¶,
- Hiram Bezerra, MD, PhD#,
- Kazuhiro Osawa, MD§,
- Mohamed Marwan, MD, PhD∗∗,
- Christoph Naber, MD, PhD††,
- Andrejs Erglis, MD, PhD‡‡,
- Seung-Jung Park, MD, PhD§§,
- Evald H. Christiansen, MD, PhD∗,
- Anne Kaltoft, MD, PhD∗,
- Jens F. Lassen, MD, PhD∗,
- Hans Erik Bøtker, MD, DMSci∗,
- Stephan Achenbach, MD, PhD∗∗,
- NXT Trial Study Group
- ∗Department of Cardiology, Aarhus University Hospital Skejby, Aarhus, Denmark
- †Department of Radiology, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- ‡MonashHeart, Monash Medical Center and Monash University, Victoria, Australia
- §Department of Cardiology, Okayama University Hospital, Okayama, Japan
- ‖Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- ¶Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium
- #Department of Cardiology, Harrington Heart and Vascular Institute, University Hospitals, Cleveland, Ohio
- ∗∗Department of Cardiology, Erlangen University Hospital, Erlangen, Germany
- ††Department of Cardiology and Angiology, Elisabeth-Krankenhaus Essen, Essen, Germany
- ‡‡Latvian Centre of Cardiology, Pauls Stradins Clinical University Hospital, Riga, Latvia
- §§Heart Institute, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
- ↵∗Reprint requests and correspondence:
Dr. Bjarne Linde Nørgaard, Department of Cardiology, Aarhus University Hospital Skejby, Skejby DK-8200 Aarhus N, Denmark.
Objectives The goal of this study was to determine the diagnostic performance of noninvasive fractional flow reserve (FFR) derived from standard acquired coronary computed tomography angiography (CTA) datasets (FFRCT) for the diagnosis of myocardial ischemia in patients with suspected stable coronary artery disease (CAD).
Background FFR measured during invasive coronary angiography (ICA) is the gold standard for lesion-specific coronary revascularization decisions in patients with stable CAD. The potential for FFRCT to noninvasively identify ischemia in patients with suspected CAD has not been sufficiently investigated.
Methods This prospective multicenter trial included 254 patients scheduled to undergo clinically indicated ICA for suspected CAD. Coronary CTA was performed before ICA. Evaluation of stenosis (>50% lumen reduction) in coronary CTA was performed by local investigators and in ICA by an independent core laboratory. FFRCT was calculated and interpreted in a blinded fashion by an independent core laboratory. Results were compared with invasively measured FFR, with ischemia defined as FFRCT or FFR ≤0.80.
Results The area under the receiver-operating characteristic curve for FFRCT was 0.90 (95% confidence interval [CI]: 0.87 to 0.94) versus 0.81 (95% CI: 0.76 to 0.87) for coronary CTA (p = 0.0008). Per-patient sensitivity and specificity (95% CI) to identify myocardial ischemia were 86% (95% CI: 77% to 92%) and 79% (95% CI: 72% to 84%) for FFRCT versus 94% (86 to 97) and 34% (95% CI: 27% to 41%) for coronary CTA, and 64% (95% CI: 53% to 74%) and 83% (95% CI: 77% to 88%) for ICA, respectively. In patients (n = 235) with intermediate stenosis (95% CI: 30% to 70%), the diagnostic accuracy of FFRCT remained high.
Conclusions FFRCT provides high diagnostic accuracy and discrimination for the diagnosis of hemodynamically significant CAD with invasive FFR as the reference standard. When compared with anatomic testing by using coronary CTA, FFRCT led to a marked increase in specificity. (HeartFlowNXT–HeartFlow Analysis of Coronary Blood Flow Using Coronary CT Angiography [HFNXT]; NCT01757678)
- computational fluid dynamics
- coronary CT angiography
- fractional flow reserve
- invasive coronary angiography
Invasive coronary angiography (ICA) is the established clinical standard for detecting coronary artery disease (CAD). The correlation between angiographic and physiological stenosis severity, however, is poor (1,2). Because coronary physiology trumps anatomy for clinical outcome (3,4), current guidelines for the management of stable CAD recommend documenting ischemia by using a noninvasive functional test (e.g., single photon emission computed tomography, stress echocardiography, cardiac magnetic resonance [cMR]) before considering ICA or coronary revascularization (4,5). Shortcomings of current noninvasive diagnostic strategies in CAD, however, are apparent from the frequently inaccurate selection of patients for ICA (6). Fractional flow reserve (FFR), which assesses the ratio of flow across a stenosis to putative flow in the absence of a stenosis, has been shown to be a powerful tool for detecting lesion-specific myocardial ischemia. Several randomized trials have shown that an FFR threshold of 0.80 distinguishes patients and coronary lesions that will benefit from coronary revascularization (7) from those that will not (8). Consequently, FFR is the accepted reference standard for assessing the functional significance of CAD in a lesion-specific manner (4). Noninvasive anatomic assessment by coronary computed tomography angiography (CTA) is being increasingly used as an accurate tool for detecting or excluding CAD (9–12). Although the absence of coronary stenoses according to coronary CTA is associated with an excellent prognosis and obviates the need for any further diagnostic evaluation (11), the correlation of stenoses detected by using coronary CTA to downstream myocardial ischemia is poor (13). Recently, a method using computational fluid dynamics to calculate coronary blood flow, pressure, and FFR based on routinely acquired coronary CTA datasets at rest (FFRCT) has been described (14–16). FFRCT combines anatomic and functional information to enable appropriate therapeutic decision making. This method has tremendous potential because noninvasive determination of FFR may be a unique, useful test to differentiate individuals who will or will not benefit from revascularization, and thus FFRCT has the potential of being a reliable gatekeeper to the pathway of ICA.
The aim of the present study was to assess the diagnostic performance of FFRCT by using invasive FFR as the reference standard. Compared with previous studies (15,16), a substantially refined version of the FFRCT technology was used, and the emphasis on coronary CTA acquisition quality was strengthened.
The rationale and design of the NXT (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps) study have been described previously (17). The study was designed to characterize FFRCT diagnostic accuracy in patients suspected of having CAD by using invasive FFR as the reference standard, and it reflects improvements in FFRCT technology (updated proprietary software with quantitative image quality analysis, improved image segmentation, refined physiological models, and increased automation), as well as emphasis on the coronary CTA image acquisition protocol to reflect current guidelines (18). The study protocol was approved at each of the 10 participating centers (Online Appendix) by the local institutional review board. All study subjects provided written informed consent.
Coronary CTA performed <60 days before scheduled nonemergent ICA was required for inclusion. As pre-specified (17), the first 100 patients included in the study had no requirements with regard to coronary stenosis severity. Beginning with patient 101, at least 1 stenosis with luminal diameter reduction between 30% and 90% in a vessel segment ≥2 mm in diameter according to coronary CTA was required. Exclusion criteria included previous coronary intervention or coronary bypass surgery; contraindications to beta-blocking agents, nitroglycerin, or adenosine; suspected acute coronary syndrome; previous myocardial infarction <30 days before coronary CTA or between coronary CTA and ICA; and body mass index >35 kg/m2.
Coronary CTA acquisition and analysis
Coronary CTA was performed by using single- or dual-source computed tomography (CT) scanners with a minimum of 64 detector rows (temporal resolution 75 to 175 ms). Both prospective triggering and retrospective gating were used for scan acquisitions. Filtered back-projection was used for image reconstruction to ensure the broadest applicability of our findings given that this is the current clinical standard of care. Laboratories followed quality standards as defined in guidelines (18). Oral and/or intravenous beta-blockers were administered targeting a heart rate of <60 beats/min, and sublingual nitrates were administered to ensure coronary vasodilation. Data acquisition was performed with 100-kV tube voltage in patients weighing ≤70 kg and 120 kV in subjects weighing >70 kg. Coronary CTA images were transmitted to a central FFRCT core laboratory (HeartFlow, Inc., Redwood City, California), where image artifacts (e.g., motion, noise, contrast, blooming) were independently evaluated by using a predefined scoring system for selection of cases appropriate for FFRCT analysis (17,19). Experienced local investigators evaluated luminal diameter stenosis in each coronary artery segment ≥2 mm in diameter by using an 18-segment coronary model before ICA (18). The strategy of stenosis quantification was at the discretion of the local investigator. Significant obstruction was defined as stenosis >50% in a major epicardial coronary artery segment ≥2 mm in diameter.
ICA and FFR measurement
ICA was performed according to standard practice (20). Angiograms were transferred to the angiography/FFR core laboratory (Harrington Heart and Vascular Institute, University Hospitals, Cleveland, Ohio) for quantitative angiography analysis by independent, blinded readers. Per protocol, measurement of FFR (PressureWire, St. Jude Medical, St. Paul, Minnesota) was performed during ICA in at least 1 vessel segment with diameter ≥2 mm and stenosis ≥30% (17). Consistent with previous studies (1), segments showing angiographic total or subtotal occlusion were assigned an FFR value of 0.50. The angiography/FFR core laboratory evaluated the complete FFR tracings for achievement of steady-state maximal hyperemia, pressure drift, and other artifacts that could compromise FFR interpretation.
Using the most recent generation of FFRCT analysis software, analysis was performed in a blinded fashion at HeartFlow (Fig. 1) (14,17). For each patient, a quantitative 3-dimensional anatomic model of the aortic root and epicardial coronary arteries was generated from coronary CTA images. Coronary blood flow and pressure were computed under conditions simulating maximal hyperemia. The results provide FFRCT throughout the coronary arterial tree. Occluded arteries were assigned FFRCT values of 0.50 (1). The FFRCT analysis required 1 to 4 h per examination depending on the CT image quality and atherosclerotic disease burden.
Integration of coronary CTA and FFR data
The angiography/FFR core laboratory received a blank 3-dimensional computer model of the coronary anatomy from the FFRCT core laboratory; on this model, they indicated location(s) corresponding to the FFR pressure sensor location(s) at the time of FFR measurement(s). The blinded integration core laboratory (HeartFlow) received the FFR measurement location indication(s) and reported corresponding FFRCT data. Integration of FFR and FFRCT data occurred only after all FFRCT analyses for all patients were complete.
Endpoints and statistical analysis
The primary study endpoint was per-patient diagnostic performance as assessed by the area under the receiver-operating characteristic curve (AUC) of FFRCT (≤0.80) versus coronary CTA (stenosis >50%) for the diagnosis of hemodynamically significant stenosis (FFR ≤0.80) in patients with coronary CTA stenosis of 30% to 90%. FFR ≤0.80 was the reference standard. Secondary endpoints included assessment of diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FFRCT and coronary CTA for all patients, patients with intermediate stenoses (30% to 70%), vessel-based analyses, and comparison of accuracy measures of FFRCT, coronary CTA, and ICA by using invasive FFR as the reference standard. Furthermore, diagnostic performance was assessed on a per-patient basis by using a stenosis threshold of 70% for coronary CTA and ICA and in the setting of a high Agatston score (threshold 400). Sample size calculations with assumptions have been described previously (17). Pre-test likelihood of CAD was determined by using the updated Diamond-Forrester risk score algorithm (4,21). AUC comparisons in patients with stenoses 30% to 90% were performed on per-patient and per-vessel levels according to the method described by DeLong et al. (22). Diagnostic accuracy, sensitivity, specificity, PPV, and NPV were calculated as simple proportions with 95% confidence intervals (CIs). Nonevaluable segments on coronary CTA in vessels >2 mm were assumed not to be stenotic. A patient was considered positive for the presence of ischemia if any evaluable vessel ≥2 mm in diameter had an FFR value ≤0.80. Similarly, a patient was considered negative if no vessel ≥2 mm in diameter had an FFR value ≤0.80. The same rules applied for the assessment of FFRCT. Patient-level comparison of diagnostic performance characteristics (accuracy, sensitivity, specificity, PPV, and NPV) was performed by using the McNemar's test for paired samples or percentile bootstrap with 100,000 resamples as appropriate. To account for potential correlation between multiple vessels in the same subject, the generalized estimating equation method with an exchangeable correlation structure was used to compare paired samples at a per-vessel level. All analyses were performed by using SAS version 9.3 (SAS Institute, Inc., Cary, North Carolina).
Among 365 patients undergoing study screening between September 2012 and August 2013, a total of 8 patients were excluded immediately after coronary CTA, and 47 (13%) did not pass the CT image quality acceptance criteria (Fig. 2). After coronary CTA acceptance, 56 patients were not eligible for inclusion; Figure 2 provides the reasons for their exclusion. Thus, 254 patients were available for intention-to-diagnose analyses. Three subjects did have a coronary stenosis between 30% and 90%. Hence, 251 subjects formed the basis for the primary endpoint analysis. Comparison of FFR and FFRCT was performed in 484 vessels. FFR was directly measured in 468 vessels, and an FFR value of 0.50 was assigned to 16 occluded vessels (3%). In 22 (4.5%) vessels, a nondiagnostic segment on coronary CTA was found proximal to the segment in which the pressure wire was placed. Baseline characteristics of the study cohort are listed in Table 1, and characteristics related to coronary CTA acquisition are provided in Table 2. The mean Agatston score (measured in 214 patients) was 302. The mean interval between coronary CTA and ICA was 18 days (range 1 to 55 days). Serious adverse events included 1 patient with coronary dissection during invasive FFR measurement requiring percutaneous coronary intervention and 2 patients with transient cerebral ischemic attacks after ICA and invasive FFR measurement. The per-patient and per-vessel characteristics of coronary CTA, ICA, FFRCT, and FFR are presented in Table 3.
Diagnostic performance of FFRCT, coronary CTA, and ICA for diagnosis of ischemia
Per-patient and per-vessel AUC for FFRCT were 0.90 (95% CI: 0.87 to 0.94) and 0.93 (0.91 to 0.95), respectively. Diagnostic accuracy, sensitivity, specificity, PPV, and NPV for FFRCT on a per-patient basis were 81%, 86%, 79%, 65%, and 93%, respectively (Table 4). On a per-vessel basis, the values were 86%, 84%, 86%, 61%, and 95%. In analyses restricted to patients with intermediate stenoses ranging from 30% to 70% (n = 235), these values were 80%, 85%, 79%, 63%, and 92% (Table 5). There was good direct correlation of per-vessel FFRCT to FFR (Pearson's correlation coefficient 0.82; p < 0.001), with a slight underestimation of FFRCT compared with FFR (Fig. 3). Figure 4 displays representative cases of patients with coronary obstructions without ischemia or with ischemia. Measures of diagnostic performance for coronary CTA and ICA are shown in Table 4. Raising the per-patient diameter stenosis threshold for anatomic test positivity to 70% resulted in lower sensitivity and higher specificity: 70% (95% CI: 60% to 79%) and 84% (95% CI: 79% to 90%) for coronary CTA. For ICA, the respective values were 35% (95% CI: 25% to 46%) and 100% (95% CI cannot be calculated). In patients with an Agatston score >400, accuracy, sensitivity, and specificity of FFRCT were 75% (95% CI: 62% to 84%), 88% (95% CI: 64% to 97%), and 69% (95% CI: 54% to 81%) versus 44% (95% CI: 31% to 56%), 94% (95% CI: 79% to 100%), and 23% (95% CI: 11% to 37%) for coronary CTA, respectively.
Diagnostic performance of FFRCT versus coronary CTA for diagnosis of ischemia
The AUC of FFRCT was higher than for coronary CTA interpretation on both a per-patient and a per-vessel level (Fig. 5). Per-patient and per-vessel diagnostic accuracy, specificity, and PPV for FFRCT were higher than for coronary CTA (Table 4). These findings were consistent also in patients with intermediate coronary stenoses (Table 5). FFRCT correctly reclassified 68% of patients with coronary CTA false-positive findings and 67% of those with coronary CTA false-positive vessels as true negative findings (Fig. 6).
This large study convincingly found that noninvasive determination of FFR is possible on the basis of standard coronary CTA datasets acquired under resting conditions with no additional radiation, contrast, or medication. In patients suspected of having CAD, FFRCT exhibited a very high diagnostic performance compared with invasively measured FFR. Particularly noteworthy was the high specificity of FFRCT, which was markedly better than in a previous evaluation of FFRCT (16). Moreover, compared with coronary CTA, FFRCT led to a marked reduction in false-positive results. Of note is the fact that patients with stenoses in the intermediate range (30% to 70%) comprised >90% of the overall study population. In clinical practice, such patients present a particular challenge because the relationship between angiographic stenosis severity and ischemia is poor (1,13).
The results of our study expand on findings and clinical implications from previous studies of FFRCT (15,16). The pilot DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) trial enrolled 103 subjects, validated against invasive FFR, and reported a per-vessel sensitivity of 88% and a specificity of 82% (15). The more recent DeFACTO (Determination of Fractional Flow Reserve by Anatomic Computed Tomographic Angiography) trial enrolled 252 patients, validated against FFR, and demonstrated a per-patient sensitivity of 90% but a specificity of only 54%, which did not meet the pre-specified primary endpoint of a lower 95% CI border for diagnostic accuracy of >70% (16).
The improved diagnostic performance of FFRCT in the present study, in particular with regard to specificity, importantly reflects substantial refinements in FFRCT technology and physiological modeling (14,17), as well as an increased focus on CT image quality (17) and adherence to official recommendations regarding coronary CTA acquisition (18). Refinements in technology evaluated in this study included improved automated image processing methods to more accurately identify the luminal boundary and improved physiological models to reduce the bias observed in previous studies (15,16). In particular, improvements in physiological models of microcirculatory resistance were implemented that demonstrated substantial improvement in diagnostic performance when evaluated retrospectively by using data from the DISCOVER-FLOW and DeFACTO studies. The importance of CT image quality and patient preparation in coronary CTA has been well established (18). The diagnostic performance of both coronary CTA and FFRCT have been shown to improve with adherence to established best practices for image acquisition, particularly by heart rate control and use of pre-scan nitroglycerin (23). In the DISCOVER-FLOW and DeFACTO trials, adherence to coronary CTA acquisition guidelines was neither required nor controlled. In contrast, in the present study, strict adherence to these guidelines was mandatory. Accordingly, >99% of the patients in this study received sublingual nitrates before coronary CTA compared with only 75% of patients in the DeFACTO trial (16). In addition, the present study used local site reading of coronary CTA stenosis degree to provide a real-world scenario and wider generalizability of the findings (24). Moreover, as indicated by differences in the proportion of FFR measurements ≤0.80 (21% of vessels in this study compared with 37% in the DeFACTO study), pre-test likelihood of CAD may have been different between the 2 studies. Notably, the pre-test probability of significant CAD in this study was intermediate (4,21) and thus reflective of the type of patients in whom noninvasive imaging is best used (4).
Established noninvasive functional diagnostic tests, such as single photon emission computed tomography, cMR, or stress echocardiography, do not directly visualize the coronary arteries or assess the functional significance of individual coronary lesions; however, they do provide meaningful information on prognosis and thus are recommended in guidelines for the diagnostic evaluation of symptomatic patients (4). The ability of these tests to correctly identify ischemia-producing stenoses by using FFR as the reference standard has been evaluated in several 1- and 2-center studies (25–27). In 1 study, cMR was compared with FFR ≤0.80 in 103 patients with suspected angina, with high reported per-vessel diagnostic performance (i.e., sensitivity of 82% and specificity of 98%) (25). However, FFR was directly measured in only 207 vessels (or 69% of the 300 coronary segments included in this analysis). Another study compared cMR with FFR ≤0.80 in 67 patients with multivessel disease and reported a per-vessel sensitivity of 61% and a specificity of 69%, with direct measurement of FFR in 154 vessels (77%) supplying 201 vascular territories included in the analysis (26). A third study investigating the diagnostic performance of dobutamine stress echocardiography in 70 patients measured FFR in all 70 vessels included in the analysis and reported a sensitivity of 48% and a specificity of 73% (27). Inclusion of a sizable number of vessels with assigned rather than measured FFR values may introduce error because vessels with normal or minimal disease according to CT or angiography may have measured FFR ≤0.80 (2,13), and severely stenosed vessels by angiography may have measured FFR >0.80 (1,13). In the present, large, multicenter study, FFR values were directly measured in 97% of the 484 vessels included in the analysis, and per-vessel sensitivity and specificity for FFRCT were 84% and 86%, respectively.
In an era of rising healthcare costs, great attention is placed on cost-effectiveness of procedures. Current clinical algorithms for therapeutic decision making in stable CAD often require 2 separate tests for assessment of coronary anatomy and ischemia (4,5). Despite extensive use of noninvasive testing, ICA continues to play a major role in diagnostic pathways (4–6). As a result of inadequate diagnostic discrimination with the use of noninvasive tests, up to 60% of subjects suspected of having CAD and referred for ICA do not have obstructive vessel narrowing (6), and the proportion without functionally significant myocardial ischemia would be expected to be even higher. A single noninvasive test with high diagnostic performance for both anatomy and lesion-specific ischemia would provide a major advantage in assessment of CAD. Our data firmly establish that myocardial ischemia is unlikely in patients tested negative by using FFRCT (NPV of 93%). Moreover, FFRCT revealed substantially improved specificity compared with coronary CTA. The present findings supporting the potential role of coronary CTA with FFRCT as a reliable gatekeeper to ICA and revascularization, together with the worldwide, ever-expanding access to coronary CTA (12), may have major health and economic implications. Indeed, simulation analyses based on historical data indicate that use of FFRCT to guide selection of ICA and revascularization may reduce costs and improve clinical outcomes in patients suspected of having CAD (28). The ongoing multicenter PLATFORM (Prospective Longitudinal Trial of FFRCT: Outcome and Resource Impacts; NCT01943903) trial compares the effect of FFRCT-guided testing versus standard diagnostic evaluation on clinical outcomes, resource utilization, costs, and quality of life in patients suspected of CAD.
In the present study, 13% of patients were judged to have nonevaluable coronary CTA images on the basis of a pre-defined image quality score (17,19). This proportion may diminish with further improvement of CT acquisition techniques and refinement of the FFRCT technology. The number of patients eligible for study inclusion (having ICA performed after coronary CTA) but not recruited was not recorded; therefore, site-level selection bias on the basis of coronary CTA findings cannot be excluded. However, the pre-test risk of CAD, as well as the spectrum and prevalence of disease (e.g., with regard to Agatston scores, as well as coronary CTA and FFR positive results), in this trial support the generalizability of the findings. Patients with acute coronary syndromes and previous coronary intervention or bypass surgery were excluded from the present study. Thus, generalizability of FFRCT to these specific populations of patients with CAD is unknown.
This study found that FFRCT has high diagnostic performance compared with invasively measured FFR, identifying patients with hemodynamically relevant obstructions with high sensitivity and specificity. Compared with anatomic interpretation by using coronary CTA, FFRCT led to a marked increase in diagnostic specificity. The addition of FFRCT to coronary CTA may allow for a comprehensive anatomic and functional assessment of CAD in a manner potentially promoting beneficial clinical and cost outcomes, which remain to be definitively proven in appropriately designed prospective trials.
For the participating study centers, please see the online version of this article.
Funding for the study was provided by HeartFlow, Inc. Dr. Mauri has received research grants from Abbott Vascular, Boston Scientific, Cordis, Medtronic, HeartFlow, Eli Lilly, Bristol-Myers Squibb, Daiichi Sankyo, and Sanofi-aventis; and serves as a consultant for St. Jude Medical, Biotronik, and Medtronic. Dr. Achenbach has received research grants from Siemens, Guerbet, and Abbott; and is a consultant for Siemens, Biotronik, and HeartFlow. Dr. Christiansen has received research grants from St. Jude Medical, Boston Scientific, Radi, Terumo, and Volcano. Dr. Leipsic has received research grants from GE Healthcare; and serves as a consultant for Edwards Lifesciences and HeartFlow. Dr. Seneviratne has given lectures at meetings organized by Toshiba. Dr. Nørgaard has received research grants from Edwards Lifesciences. All other authors have reported that they have no relationships relevant to the contents of this paper to di disclose.
- Abbreviations and Acronyms
- area under the receiver-operating characteristic curve
- coronary artery disease
- confidence interval
- computed tomography
- computed tomography angiography
- cardiac magnetic resonance
- fractional flow reserve
- fractional flow reserve derived from coronary computed tomography angiography datasets
- invasive coronary angiography
- negative predictive value
- positive predictive value
- Received October 18, 2013.
- Revision received November 21, 2013.
- Accepted November 27, 2013.
- American College of Cardiology Foundation
- Tonino P.A.,
- Fearon W.F.,
- De Bruyne B.,
- et al.
- De Bruyne B.,
- Hersbach F.,
- Pijls N.H.,
- et al.
- Hachamovitch R.,
- Hayes S.W.,
- Friedman J.D.,
- Cohen I.,
- Berman D.S.
- Montalescot G.,
- Sechtem U.,
- Achenbach S.,
- et al.
- Wijns W.,
- Kolh P.,
- Danchin N.,
- et al.
- Muller O.,
- Mangiacapra F.,
- Ntalianis A.,
- et al.
- Meijboom W.B.,
- Meijs M.F.,
- Schuijf J.D.,
- et al.
- Budoff M.J.,
- Dowe D.,
- Jollis J.G.,
- et al.
- Hulten E.A.,
- Carbonaro S.,
- Petrillo S.P.,
- Mitchell J.D.,
- Villines T.C.
- Shaw L.J.,
- Marwick T.H.,
- Zoghbi W.A.,
- et al.
- Meijboom W.B.,
- Van Mieghem C.A.,
- van Pelt N.,
- et al.
- Taylor C.A.,
- Fonte T.A.,
- Min J.K.
- Koo B.K.,
- Erglis A.,
- Doh J.H.,
- et al.
- Norgaard B.L.,
- Leipsic J.,
- Sengupta S.,
- et al.
- Genders T.S.,
- Steverberg E.W.,
- Alkadhi H.,
- et al.
- Leipsic J.,
- Yang T.H.,
- Thompson A.,
- et al.
- Watkins S.,
- McGeoch R.,
- Lyne J.,
- et al.
- Jung P.H.,
- Rieber J.,
- Störk S.,
- et al.
- Hlatky M.A.,
- Saxena A.,
- Koo B.K.,
- Erglis A.,
- Zarins C.K.,
- Min J.K.