Correction to: Applicability and accuracy of pretest probability calculations implemented in the NICE clinical guideline for decision making about imaging in patients with chest pain of recent onset.

The original version of this article, published on 19 March 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The names of the authors Philipp A. Kaufmann, Ronny Ralf Buechel and Bernhard A. Herzog were presented incorrectly.

Applicability and accuracy of pretest probability calculations implemented in the NICE clinical guideline for decision making about imaging in patients with chest pain of recent onset.

OBJECTIVES: To analyse the implementation, applicability and accuracy of the pretest probability calculation provided by NICE clinical guideline 95 for decision making about imaging in patients with chest pain of recent onset. METHODS: The definitions for pretest probability calculation in the original Duke clinical score and the NICE guideline were compared. We also calculated the agreement and disagreement in pretest probability and the resulting imaging and management groups based on individual patient data from the Collaborative Meta-Analysis of Cardiac CT (CoMe-CCT). RESULTS: 4,673 individual patient data from the CoMe-CCT Consortium were analysed. Major differences in definitions in the Duke clinical score and NICE guideline were found for the predictors age and number of risk factors. Pretest probability calculation using guideline criteria was only possible for 30.8 % (1,439/4,673) of patients despite availability of all required data due to ambiguity in guideline definitions for risk factors and age groups. Agreement regarding patient management groups was found in only 70 % (366/523) of patients in whom pretest probability calculation was possible according to both models. CONCLUSIONS: Our results suggest that pretest probability calculation for clinical decision making about cardiac imaging as implemented in the NICE clinical guideline for patients has relevant limitations. KEY POINTS: • Duke clinical score is not implemented correctly in NICE guideline 95. • Pretest probability assessment in NICE guideline 95 is impossible for most patients. • Improved clinical decision making requires accurate pretest probability calculation. • These refinements are essential for appropriate use of cardiac CT.

Computed tomography versus invasive coronary angiography: design and methods of the pragmatic randomised multicentre DISCHARGE trial.

OBJECTIVES: More than 3.5 million invasive coronary angiographies (ICA) are performed in Europe annually. Approximately 2 million of these invasive procedures might be reduced by noninvasive tests because no coronary intervention is performed. Computed tomography (CT) is the most accurate noninvasive test for detection and exclusion of coronary artery disease (CAD). To investigate the comparative effectiveness of CT and ICA, we designed the European pragmatic multicentre DISCHARGE trial funded by the 7th Framework Programme of the European Union (EC-GA 603266). METHODS: In this trial, patients with a low-to-intermediate pretest probability (10-60 %) of suspected CAD and a clinical indication for ICA because of stable chest pain will be randomised in a 1-to-1 ratio to CT or ICA. CT and ICA findings guide subsequent management decisions by the local heart teams according to current evidence and European guidelines. RESULTS: Major adverse cardiovascular events (MACE) defined as cardiovascular death, myocardial infarction and stroke as a composite endpoint will be the primary outcome measure. Secondary and other outcomes include cost-effectiveness, radiation exposure, health-related quality of life (HRQoL), socioeconomic status, lifestyle, adverse events related to CT/ICA, and gender differences. CONCLUSIONS: The DISCHARGE trial will assess the comparative effectiveness of CT and ICA. KEY POINTS: • Coronary artery disease (CAD) is a major cause of morbidity and mortality. • Invasive coronary angiography (ICA) is the reference standard for detection of CAD. • Noninvasive computed tomography angiography excludes CAD with high sensitivity. • CT may effectively reduce the approximately 2 million negative ICAs in Europe. • DISCHARGE addresses this hypothesis in patients with low-to-intermediate pretest probability for CAD.

Noninvasive evaluation of global and regional left ventricular function using computed tomography and magnetic resonance imaging: a meta-analysis.

OBJECTIVES: To compare the diagnostic accuracy of computed tomography (CT) in the assessment of global and regional left ventricular (LV) function with magnetic resonance imaging (MRI). METHODS: MEDLINE, EMBASE and ISI Web of Science were systematically reviewed. Evaluation included: ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV) and left ventricular mass (LVM). Differences between modalities were analysed using limits of agreement (LoA). Publication bias was measured by Egger's regression test. Heterogeneity was evaluated using Cochran's Q test and Higgins I2 statistic. In the presence of heterogeneity the DerSimonian-Laird method was used for estimation of heterogeneity variance. RESULTS: Fifty-three studies including 1,814 patients were identified. The mean difference between CT and MRI was -0.56 % (LoA, -11.6-10.5 %) for EF, 2.62 ml (-34.1-39.3 ml) for EDV and 1.61 ml (-22.4-25.7 ml) for ESV, 3.21 ml (-21.8-28.3 ml) for SV and 0.13 g (-28.2-28.4 g) for LVM. CT detected wall motion abnormalities on a per-segment basis with 90 % sensitivity and 97 % specificity. CONCLUSIONS: CT is accurate for assessing global LV function parameters but the limits of agreement versus MRI are moderately wide, while wall motion deficits are detected with high accuracy. KEY POINTS: • CT helps to assess patients with coronary artery disease (CAD). • MRI is the reference standard for evaluation of left ventricular function. • CT provides accurate assessment of global left ventricular function.

Extracardiac findings on coronary CT angiography: a systematic review.

UNLABELLED: Coronary computed tomography angiography (CCTA) is of growing importance in noninvasive diagnosis of coronary artery diseases. The CT data allow evaluation not only of coronary arteries but also of adjacent anatomical territories. Our objective was to review, to analyze, and to quantify the spectrum and the prevalence of extracardiac findings (ECF) in CCTA. Therefore, we searched MEDLINE, EMBASE, and ISI Web of Science. Prior to quantitative analysis, we categorized the ECF of all included studies into clinically significant and clinically non-significant findings. First, we calculated the average prevalences of ECF and clinically significant ECF performing a meta-analysis for proportions using the double arcsine transformation. Second, we analyzed the spectrum and location of clinically significant ECF. Third, we identified ECF of acutely life-threatening potential as well as malignancies and calculated their prevalences. Thirteen studies with a total of 11,703 patients were found to meet the inclusion criteria. The average prevalence of overall ECF was 41.0% (95% confidence interval [95% CI]: 27, 56; P < .0001) and 16.0% (95% CI (9, 24; P < .0001) for clinically significant ECF. Clinically significant ECF were most commonly detected in the lungs (50.2%), the abdomen (26.7%), the vessels (13.1%), the mediastinum (3.6%), and in other adjacent anatomical territories (6.4%). The prevalence of acutely life-threatening and malignant ECF accounted for 2.2% (95% CI: 1.9, 2.5; P < .0001) and 0.3% (95% CI: 0.2-0.4; P < .0001), respectively. In conclusion, clinically significant and acutely life-threatening ECF are common. Reading CCTA for ECF may lead to earlier detection of relevant disease. CONCLUSION: Clinically significant and acutely life-threatening ECF are common. Reading CCTA for ECF may lead to earlier detection of relevant disease.

Compliance with STARD checklist among studies of coronary CT angiography: systematic review.

PURPOSE: To determine the compliance of prospective diagnostic accuracy studies investigating computed tomographic (CT) coronary angiography with Standards for Reporting of Diagnostic Accuracy (STARD) guidelines. MATERIALS AND METHODS: Relevant studies were identified by means of a systematic literature search that included the MEDLINE, EMBASE, and Institute for Scientific Information Web of Science databases. Prospective studies that compared CT with conventional coronary angiography for the evaluation of the coronary arteries were included. STARD compliance was assessed by three independent investigators using 21 of the original 25 STARD checklist items. Items with the qualifier "if done" (items 13, 23, and 24) were excluded because they were not applicable to all studies. Owing to the inclusion criteria, all studies fulfilled item 9; therefore, this item was excluded as well. The correlation between the total score and multiple variables was tested with a linear regression model. RESULTS: One hundred thirty studies published in 44 scientific journals were included in the analysis. There was a significant correlation between the year of publication and STARD-adopting versus non-STARD-adopting journals, with the total STARD score based on a linear regression model. Studies published in STARD-adopting journals showed a significantly higher total STARD score than those published in nonadopting journals (15.4 ± 2.7 vs 14.1 ± 2.7; P = .018). Linear regression analysis yielded an increase in the total STARD score of 0.30 points (95% confidence interval: 0.03, 0.57; P = .031) per year. Adequate reporting of individual items varied between 17% (item 20b) and 97% (item 1). CONCLUSION: The overall compliance with reporting guidelines of prospective diagnostic accuracy studies of CT coronary angiography is moderate to good, and STARD-adopting journals have greater STARD compliance than nonadopting journals. Online supplemental material is available for this article.

Individual patient data meta-analysis for the clinical assessment of coronary computed tomography angiography: protocol of the Collaborative Meta-Analysis of Cardiac CT (CoMe-CCT).

BACKGROUND: Coronary computed tomography angiography has become the foremost noninvasive imaging modality of the coronary arteries and is used as an alternative to the reference standard, conventional coronary angiography, for direct visualization and detection of coronary artery stenoses in patients with suspected coronary artery disease. Nevertheless, there is considerable debate regarding the optimal target population to maximize clinical performance and patient benefit. The most obvious indication for noninvasive coronary computed tomography angiography in patients with suspected coronary artery disease would be to reliably exclude significant stenosis and, thus, avoid unnecessary invasive conventional coronary angiography. To do this, a test should have, at clinically appropriate pretest likelihoods, minimal false-negative outcomes resulting in a high negative predictive value. However, little is known about the influence of patient characteristics on the clinical predictive values of coronary computed tomography angiography. Previous regular systematic reviews and meta-analyses had to rely on limited summary patient cohort data offered by primary studies. Performing an individual patient data meta-analysis will enable a much more detailed and powerful analysis and thus increase representativeness and generalizability of the results. The individual patient data meta-analysis is registered with the PROSPERO database (CoMe-CCT, CRD42012002780). METHODS/DESIGN: The analysis will include individual patient data from published and unpublished prospective diagnostic accuracy studies comparing coronary computed tomography angiography with conventional coronary angiography. These studies will be identified performing a systematic search in several electronic databases. Corresponding authors will be contacted and asked to provide obligatory and additional data. Risk factors, previous test results and symptoms of individual patients will be used to estimate the pretest likelihood of coronary artery disease. A bivariate random-effects model will be used to calculate pooled mean negative and positive predictive values as well as sensitivity and specificity. The primary outcome of interest will be positive and negative predictive values of coronary computed tomography angiography for the presence of coronary artery disease as a function of pretest likelihood of coronary artery disease, analyzed by meta-regression. As a secondary endpoint, factors that may influence the diagnostic performance and clinical value of computed tomography, such as heart rate and body mass index of patients, number of detector rows, and administration of beta blockade and nitroglycerin, will be investigated by integrating them as further covariates into the bivariate random-effects model. DISCUSSION: This collaborative individual patient data meta-analysis should provide answers to the pivotal question of which patients benefit most from noninvasive coronary computed tomography angiography and thus help to adequately select the right patients for this test.

Methodological quality of diagnostic accuracy studies on non-invasive coronary CT angiography: influence of QUADAS (Quality Assessment of Diagnostic Accuracy Studies included in systematic reviews) items on sensitivity and specificity.

OBJECTIVES: To evaluate the methodological quality of diagnostic accuracy studies on coronary computed tomography (CT) angiography using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies included in systematic reviews) tool. METHODS: Each QUADAS item was individually defined to adapt it to the special requirements of studies on coronary CT angiography. Two independent investigators analysed 118 studies using 12 QUADAS items. Meta-regression and pooled analyses were performed to identify possible effects of methodological quality items on estimates of diagnostic accuracy. RESULTS: The overall methodological quality of coronary CT studies was merely moderate. They fulfilled a median of 7.5 out of 12 items. Only 9 of the 118 studies fulfilled more than 75 % of possible QUADAS items. One QUADAS item ("Uninterpretable Results") showed a significant influence (P = 0.02) on estimates of diagnostic accuracy with "no fulfilment" increasing specificity from 86 to 90 %. Furthermore, pooled analysis revealed that each QUADAS item that is not fulfilled has the potential to change estimates of diagnostic accuracy. CONCLUSIONS: The methodological quality of studies investigating the diagnostic accuracy of non-invasive coronary CT is only moderate and was found to affect the sensitivity and specificity. An improvement is highly desirable because good methodology is crucial for adequately assessing imaging technologies. KEY POINTS: • Good methodological quality is a basic requirement in diagnostic accuracy studies. • Most coronary CT angiography studies have only been of moderate design quality. • Weak methodological quality will affect the sensitivity and specificity. • No improvement in methodological quality was observed over time. • Authors should consider the QUADAS checklist when undertaking accuracy studies.

Use of 3x2 tables with an intention to diagnose approach to assess clinical performance of diagnostic tests: meta-analytical evaluation of coronary CT angiography studies.

OBJECTIVE: To determine whether a 3 × 2 table, using an intention to diagnose approach, is better than the "classic" 2 × 2 table at handling transparent reporting and non-evaluable results, when assessing the accuracy of a diagnostic test. DESIGN: Based on a systematic search for diagnostic accuracy studies of coronary computed tomography (CT) angiography, full texts of relevant studies were evaluated to determine whether they could calculate an alternative 3 × 2 table. To quantify an overall effect, we pooled diagnostic accuracy values according to a meta-analytical approach. DATA SOURCES: Medline (via PubMed), Embase (via Ovid), and ISI Web of Science electronic databases. ELIGIBILITY CRITERIA: Prospective English or German language studies comparing coronary CT with conventional coronary angiography in all patients and providing sufficient data for a patient level analysis. RESULTS: 120 studies (10,287 patients) were eligible. Studies varied greatly in their approaches to handling non-evaluable findings. We found 26 studies (including 2298 patients) that allowed us to calculate both 2 × 2 tables and 3 × 2 tables. Using a bivariate random effects model, we compared the 2 × 2 table with the 3 × 2 table, and found significant differences for pooled sensitivity (98.2 (95% confidence interval 96.7 to 99.1) v 92.7 (88.5 to 95.3)), area under the curve (0.99 (0.98 to 1.00) v 0.93 (0.91 to 0.95)), positive likelihood ratio (9.1 (6.2 to 13.3) v 4.4 (3.3 to 6.0)), and negative likelihood ratio (0.02 (0.01 to 0.04) v 0.09 (0.06 to 0.15); (P<0.05)). CONCLUSION: Parameters for diagnostic performance significantly decrease if non-evaluable results are included by a 3 × 2 table for analysis (intention to diagnose approach). This approach provides a more realistic picture of the clinical potential of diagnostic tests.

Influence of coronary artery disease prevalence on predictive values of coronary CT angiography: a meta-regression analysis.

OBJECTIVE: To evaluate the impact of coronary artery disease (CAD) prevalence on the predictive values of coronary CT angiography. METHODS: We performed a meta-regression based on a generalised linear mixed model using the binomial distribution and a logit link to analyse the influence of the prevalence of CAD in published studies on the per-patient negative and positive predictive values of CT in comparison to conventional coronary angiography as the reference standard. A prevalence range in which the negative predictive value was higher than 90%, while at the same time the positive predictive value was higher than 70% was considered appropriate. RESULTS: The summary negative and positive predictive values of coronary CT angiography were 93.7% (95% confidence interval [CI] 92.8-94.5%) and 87.5% (95% CI, 86.5-88.5%), respectively. With 95% confidence, negative and positive predictive values higher than 90% and 70% were available with CT for a CAD prevalence of 18-63%. CT systems with >16 detector rows met these requirements for the positive (P < 0.01) and negative (P < 0.05) predictive values in a significantly broader range than systems with ≤16 detector rows. CONCLUSION: It is reasonable to perform coronary CT angiography as a rule-out test in patients with a low-to-intermediate likelihood of disease.

Meta-analysis: noninvasive coronary angiography using computed tomography versus magnetic resonance imaging.

BACKGROUND: Two imaging techniques, multislice computed tomography (CT) and magnetic resonance imaging (MRI), have evolved for noninvasive coronary angiography. PURPOSE: To compare CT and MRI for ruling out clinically significant coronary artery disease (CAD) in adults with suspected or known CAD. DATA SOURCES: MEDLINE, EMBASE, and ISI Web of Science searches from inception through 2 June 2009 and bibliographies of reviews. STUDY SELECTION: Prospective English- or German-language studies that compared CT or MRI with conventional coronary angiography in all patients and included sufficient data for compilation of 2 x 2 tables. DATA EXTRACTION: 2 investigators independently extracted patient and study characteristics; differences were resolved by consensus. DATA SYNTHESIS: 89 and 20 studies (comprising 7516 and 989 patients) assessed CT and MRI, respectively. Bivariate analysis of data yielded a mean sensitivity and specificity of 97.2% (95% CI, 96.2% to 98.0%) and 87.4% (CI, 84.5% to 89.8%) for CT and 87.1% (CI, 83.0% to 90.3%) and 70.3% (CI, 58.8% to 79.7%) for MRI. In studies that included only patients with suspected CAD, sensitivity and specificity of CT were 97.6% (CI, 96.1% to 98.5%) and 89.2% (CI, 86.0% to 91.8%). Covariate analysis yielded a significantly higher sensitivity for CT scanners with more than 16 rows (98.1% [CI, 97.0% to 99.0%]; P < 0.050) than for older-generation scanners (95.6% [CI, 94.0% to 97.0%]). Heart rates less than 60 beats/min during CT yielded significantly better values for sensitivity than did higher heart rates (P < 0.001). LIMITATIONS: Few studies investigated coronary angiography with MRI. Only 5 studies were direct head-to-head comparisons of CT and MRI. Covariate analyses explained only part of the observed heterogeneity. CONCLUSION: For ruling out CAD, CT is more accurate than MRI. Scanners with more than 16 rows improve sensitivity, as do slowed heart rates. PRIMARY FUNDING SOURCE: None.