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. 2024 Aug 14;15(1):208.
doi: 10.1186/s13244-024-01702-y.

The effectiveness of coronary computed tomography angiography and functional testing for the diagnosis of obstructive coronary artery disease: results from the individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT)

Peter Schlattmann #  1 , Viktoria Wieske #  2 , Keno K Bressem  2 , Theresa Götz  1 , Georg M Schuetz  2 , Daniele Andreini  3 , Gianluca Pontone  3 , Hatem Alkadhi  4 , Jörg Hausleiter  5 , Elke Zimmermann  2 , Bernhard Gerber  6 , Abbas A Shabestari  7 , Matthijs F L Meijs  8 , Akira Sato  9 , Kristian A Øvrehus  10 , Shona M M Jenkins  11 , Juhani Knuuti  12 , Ashraf Hamdan  13 , Bjørn A Halvorsen  14 , Vladimir Mendoza-Rodriguez  15 , Johannes Rixe  16 , Yung-Liang Wan  17 , Christoph Langer  18 , Sebastian Leschka  19 , Eugenio Martuscelli  20 , Said Ghostine  21 , Jean-Claude Tardif  22 , Alejandra Rodríguez Sánchez  2 , Robert Haase #  2 , Marc Dewey #  23   24   25
Affiliations

The effectiveness of coronary computed tomography angiography and functional testing for the diagnosis of obstructive coronary artery disease: results from the individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT)

Peter Schlattmann et al. Insights Imaging. .

Abstract

Aim: To determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD).

Methods and results: Two-thousand nine-hundred twenty symptomatic stable chest pain patients were included in the international Collaborative Meta-Analysis of Cardiac CT consortium to compare CTA with exercise electrocardiography (exercise-ECG) and single-photon emission computed tomography (SPECT) for diagnosis of CAD defined as ≥ 50% diameter stenosis by invasive coronary angiography (ICA) as reference standard. Generalised linear mixed models were used for calculating the diagnostic accuracy of each diagnostic test including non-diagnostic results as dependent variables in a logistic regression model with random intercepts and slopes. Covariates were the reference standard ICA, the type of diagnostic method, and their interactions. CTA showed significantly better diagnostic performance (p < 0.0001) with a sensitivity of 94.6% (95% CI 92.7-96) and a specificity of 76.3% (72.2-80) compared to exercise-ECG with 54.9% (47.9-61.7) and 60.9% (53.4-66.3), SPECT with 72.9% (65-79.6) and 44.9% (36.8-53.4), respectively. The positive predictive value of CTA was ≥ 50% in patients with a clinical pretest probability of 10% or more while this was the case for ECG and SPECT at pretest probabilities of ≥ 40 and 28%. CTA reliably excluded obstructive CAD with a post-test probability of below 15% in patients with a pretest probability of up to 74%.

Conclusion: In patients with stable chest pain, CTA is more effective than functional testing for the diagnosis as well as for reliable exclusion of obstructive CAD. CTA should become widely adopted in patients with intermediate pretest probability.

Systematic review registration: PROSPERO Database for Systematic Reviews-CRD42012002780.

Critical relevance statement: In symptomatic stable chest pain patients, coronary CTA is more effective than functional testing for diagnosis and reliable exclusion of obstructive CAD in intermediate pretest probability of CAD.

Key points: Coronary computed tomography angiography showed significantly better diagnostic performance (p < 0.0001) for diagnosis of coronary artery disease compared to exercise-ECG and SPECT. The positive predictive value of coronary computed tomography angiography was ≥ 50% in patients with a clinical pretest probability of at least 10%, for ECG ≥ 40%, and for SPECT 28%. Coronary computed tomography angiography reliably excluded obstructive coronary artery disease with a post-test probability of below 15% in patients with a pretest probability of up to 74%.

Keywords: Computed tomography angiography; Diagnostic accuracy; Exercise-ECG; Functional stress testing; Single-photon emission computed tomography.

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Conflict of interest statement

P.S. and M.D. had support from the joint programme of the German Research Foundation and the German Federal Ministry of Education and Research for the submitted work; P.S. has support from the German Research Foundation, grants from the European Union and grants from Bayer Pharma AG; V.W. reports grant support from the FP7 Program of the European Commission for the randomised multicenter DISCHARGE trial (603266-2, HEALTH-2012.2.4.-2). GMS reports grants from the German Federal Ministry of Education and Research (BMBF), during the conduct of the study; G.P. reports grants from General Electric and is on the speakers bureau for Medtronic and Bracco; J.H. is on the speakers bureau for Abbott Vascular and Edwards Life Sciences; B.G. reports that the Cliniques St Luc UCL holds a master research agreement with Philips Medical Systems; A.S. reports personal fees from General Electric and Toshiba; J.K. reports grants from CardiRad and personal fees from GE Healthcare; M.D. has received grant support from the FP7 Program of the European Commission for the randomised multicenter DISCHARGE trial (603266-2, HEALTH-2012.2.4.-2). He also received grant support from the German Research Foundation (DFG) in the Heisenberg Program (DE 1361/14-1), a graduate program on quantitative biomedical imaging (BIOQIC, GRK 2260/1), for fractal analysis of myocardial perfusion (DE 1361/18-1), the Priority Programme Radiomics for the investigation of coronary plaque and coronary flow (DE 1361/19-1 (428222922) and 20-1 (428223139) in SPP 2177/1). He also received funding from the Berlin University Alliance (GC_SC_PC 27) and from the Digital Health Accelerator of the Berlin Institute of Health. Prof. Dewey has received lecture fees from Canon, Guerbet. Prof. Dewey is the European Society of Radiology (ESR) Research Chair (2019–2022) and the opinions expressed in this article are the author’s own and do not represent the view of ESR. Per the guiding principles of ESR, the work as Research Chair is on a voluntary basis and only remuneration of travel expenses occurs. Prof. Dewey is also the editor of Cardiac CT, published by Springer Nature, and offers hands-on courses on CT imaging (www.ct-kurs.de). Institutional master research agreements exist with Siemens, General Electric, Philips, and Canon. The terms of these arrangements are managed by the legal department of Charité—Universitätsmedizin Berlin. Professor Dewey holds a joint patent with Florian Michallek on dynamic perfusion analysis using fractal analysis (PCT/EP2016/071551). K.K.B., T.G., D.A., H.A., E.Z., A.A.S., M.F.L.M., K.A.O., S.M.M.J., A.H., B.A.H., V.M.-R., J.R., Y.-L.W., C.L., S.L., E.M., S.G., J.-C.T., A.R.S., R.H. have nothing to disclose.

Figures

Fig. 1
Fig. 1
Flow chart for study selection. Part of the study flow referring to the COME-CCT main analysis paper as published by Haase et al [70]. In this subanalysis of the international COME-CCT Consortium, only patients with functional testing data were included, and studies for pooled analysis were only available if at least 5% of the patients of each of the 31 included studies received functional testing in order to avoid inclusion bias
Fig. 2
Fig. 2
Analysis of diagnostic performance for CTA, Exercise-ECG, SPECT. The lines represent the positive and negative predictive values of CAD after a positive (solid lines) or negative (dashed lines) diagnostic test result for obstructive (obstructive) coronary artery disease defined as a patient with at least 50% coronary diameter stenosis. CTA was significantly more accurate than exercise-ECG and SPECT. Predictive values including 95% confidence intervals for all three tests are provided in Appendix Figs. 3–5
Fig. 3
Fig. 3
Similar diagnostic performance of CTA, Exercise-ECG, and SPECT after excluding studies with risk of bias. Similar diagnostic performance as shown in Fig. 2 after including all individual-patient data, is found in this analysis in which studies with a high risk of bias [7, 28, 33, 36, 40, 43, 44, 48] were excluded and only studies with low risk of bias were included (details on the risk of bias assessment is shown in Appendix Table 2)
Fig. 4
Fig. 4
Cross-hair comparison of CTA, Exercise-ECG, SPECT of per-study sensitivity, and false-positive rate. The lines represent 95% confidence intervals for sensitivity and false-positive rate based on the per-study data for CT, exercise-ECG, and SPECT. The per-study forest plots for all three tests and the results of all individual studies are also shown in Appendix Figs. 5–8
See this image and copyright information in PMC

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