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.

Prognostic implications of non-culprit plaques in acute coronary syndrome: non-invasive assessment with coronary CT angiography.

AIMS: Non-culprit plaques are responsible for a substantial number of future events in patients with acute coronary syndrome (ACS). In this study, we evaluated the prognostic implications of non-culprit plaques seen on coronary computed tomography angiography (CTA) in patients with ACS. METHODS AND RESULTS: Coronary CTA was performed in 169 patients (mean 59 ± 11 years, 129 males) admitted with ACS. Data sets were assessed for the presence of obstructive non-culprit plaques (>50% luminal narrowing), segment involvement score, and quantitative measures of plaque burden, after censoring initial culprit plaques. Follow-up was performed for the occurrence of major adverse cardiovascular events (MACEs) unrelated to the initial culprit plaque; cardiac death, second ACS, or coronary revascularization after 90 days. After a median follow-up of 4.8 (IQR 2.6-6.6) years, MACE occurred in 36 (24%) patients: 6 cardiac deaths, 16 second ACS, and 14 coronary revascularizations. Dyslipidaemia (hazard ratio [HR] 3.1 [95% confidence interval 1.5-6.6]) and diabetes mellitus (HR 4.8 [2.3-10.3]) were univariable clinical predictors of MACE. Patients with remaining obstructive non-culprit plaques (HR 3.66 [1.52-8.80]) and higher plaque burden index (HR 1.22 [1.01-1.48]) had a more risk of MACE. In multivariate analysis, with diabetes, dyslipidaemia, and plaque burden index, obstructive non-culprit plaques (HR 3.76 [1.28-11.09]) remained an independent predictor of MACE. CONCLUSION: Almost a quarter of the study population experienced a new event arising from a non-culprit plaque during a follow-up of almost 5 years. ACS patients with remaining obstructive non-culprit plaques or high plaque burden have an increased risk of future MACE.

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.