Evidence map›Paper›PMID 39143443›Full record

ArticleInsights into imaging2024

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, Viktoria Wieske, Keno K Bressem, Theresa Götz, Georg M Schuetz, Daniele Andreini, Gianluca Pontone, Hatem Alkadhi, Jörg Hausleiter, Elke Zimmermann and 20 more

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors.

Peter Schlattmann *Institute of Medical Statistics, Computer Sciences, and Data Science, University Hospital of Friedrich Schiller University Jena, Jena, Germany.
Viktoria Wieske *Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Keno K BressemDepartment of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Theresa GötzInstitute of Medical Statistics, Computer Sciences, and Data Science, University Hospital of Friedrich Schiller University Jena, Jena, Germany.
Georg M SchuetzDepartment of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Daniele AndreiniCentro Cardiologico Monzino, IRCCS, Milan, Italy.
Gianluca PontoneCentro Cardiologico Monzino, IRCCS, Milan, Italy.
Hatem AlkadhiInstitute of Diagnostic and Interventional Radiology University Hospital Zurich, Zurich, Switzerland.
Jörg HausleiterMaximilians-University of Munich, Munich, Germany.
Elke ZimmermannDepartment of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Bernhard GerberDepartment of Cardiology, Clinique Universitaire St Luc, Institut de Recherche Clinique et Expérimentale, Brussels, Belgium.
Abbas A ShabestariModarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Matthijs F L MeijsDepartment of Cardiology, University Medical Centre Utrecht, Utrecht, The Netherlands.
Akira SatoCardiovascular Division, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
Kristian A ØvrehusDepartment of Cardiology, Odense University Hospital, Odense, Denmark.
Shona M M JenkinsGlasgow Royal Infirmary and Stobhill Hospital, Glasgow, UK.
Juhani KnuutiTurku University Hospital and University of Turku, Turku, Finland.
Ashraf HamdanDepartment of Cardiovascular Imaging, Department of Cardiology, Rabin Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Bjørn A HalvorsenDepartment of Cardiology, Ostfold Hospital Trust, Grålum, Norway.
Vladimir Mendoza-RodriguezDepartment of Cardiology, National Institute of Cardiology and Cardiovascular Surgery, Havana, Cuba.
Johannes RixeDepartment of Cardiology and Electrophysiology, Jung Stilling Hospital Siegen, Siegen, Germany.
Yung-Liang WanMedical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Chang Gung Memorial Hospital at Linkou, Taoyaun City, Taiwan.
Christoph LangerKardiologisch-Angiologische Praxis, Herzzentrum Bremen, Bremen, Germany.
Sebastian LeschkaDepartment of Radiology, Kantonsspital St Gallen, St Gallen, Switzerland.
Eugenio MartuscelliDepartment of Internal Medicine, University of Rome Tor Vergata, Rome, Italy.
Said GhostineDepartment of Cardiology, Centre Chirurgical Marie Lannelongue, Le Plessis Robinson, France.
Jean-Claude TardifMontreal Heart Institute, Université de Montréal, Montréal, QC, Canada.
Alejandra Rodríguez SánchezDepartment of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Robert Haase *Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Marc Dewey *Department of Radiology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany. dewey@charite.de.ORCID http://orcid.org/0000-0002-4402-2733

Funding

Bundesministerium für Bildung und Forschung 01KG1110
6 · The paper itself

Abstract

aimTo determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD). METHODS AND

resultsTwo-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%.

conclusionIn 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%.

Indexed as

Computed tomography angiographyDiagnostic accuracyExercise-ECGFunctional stress testingSingle-photon emission computed tomography

Identifiers

PMID39143443
PMCPMC11324632

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.