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.

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.

Association of endogenous testosterone with subclinical atherosclerosis in men: the multi-ethnic study of atherosclerosis.

OBJECTIVES: Whether endogenous sex hormones play a role in cardiovascular disease (CVD) risk in men is unclear. Few studies have examined associations of sex hormones with atherosclerosis measured by coronary artery calcium score (CACS) and carotid intima-media thickness (cIMT). We evaluated the association of testosterone (T) and other sex hormones with CACS and cIMT. METHODS: Using the large multi-ethnic cohort of 3164 men without known CVD in the Multi-Ethnic Study of Atherosclerosis (MESA), cross-sectional associations of tertiles of endogenous sex hormones with CACS and cIMT were analysed. RESULTS: In regard to CAC, there was a significant negative trend (P-trend = 0·02) for CACS>0 over tertiles of free T (FT) with RRs (95% CI) for the lowest to highest tertiles. There was also a marginally significant positive trend (P-trend = 0·06) for CACS>0 over tertiles of sex hormone-binding globulin (SHBG) with RRs for the lowest to highest tertiles. There were no significant associations with CACS >0 for tertiles of TT (Total T), bioavailable T (BT), oestradiol (E2) and dehydroepiandrosterone (DHEA). There was significantly higher log CACS after adjustment for CVD risk factors for lower TT levels, compared to higher levels, using 9·54 and 10·4 nmol/l as cut-off points. In regard to cIMT, there was a significant positive trend (P = 0·003) in mean cIMT over the tertiles of BT, but not for TT, FT, E2, DHEA and SHBG. There was significantly lower cIMT after adjustment for CVD risk factors for lower TT levels compared to higher levels. CONCLUSION: In a population of male subjects with no known CVD, lower FT is associated with higher RR of CACS>0 and lower TT is associated with higher log CACS. Lower BT and TT are associated with lower cIMT. While these findings support the positive correlation between low T and coronary atherosclerosis, the opposite findings on cIMT warrant further evaluation.

Current indications for transplantation: stratification of severe heart failure and shared decision-making.

Heart failure (HF) is a complex clinical syndrome that results from structural or functional cardiovascular disorders causing a mismatch between demand and supply of oxygenated blood and consecutive failure of the body's organs. For those patients with stage D HF, advanced therapies, such as mechanical circulatory support (MCS) or heart transplantation (HTx), are potentially life-saving options. The role of risk stratification of patients with stage D HF in a value-based healthcare framework is to predict which subset might benefit from advanced HF (AdHF) therapies, to improve outcomes related to the individual patient including mortality, morbidity and patient experience as well as to optimize health care delivery system outcomes such as cost-effectiveness. Risk stratification and subsequent outcome prediction as well as therapeutic recommendation-making need to be based on the comparative survival benefit rationale. A robust model needs to (I) have the power to discriminate (i.e., to correctly risk stratify patients); (II) calibrate (i.e., to show agreement between the predicted and observed risk); (III) to be applicable to the general population; and (IV) provide good external validation. The Seattle Heart Failure Model (SHFM) and the Heart Failure Survival Score (HFSS) are two of the most widely utilized scores. However, outcomes for patients with HF are highly variable which make clinical predictions challenging. Despite our clinical expertise and current prediction tools, the best short- and long-term survival for the individual patient, particularly the sickest patient, is not easy to identify because among the most severely ill, elderly and frail patients, most preoperative prediction tools have the tendency to be imprecise in estimating risk. They should be used as a guide in a clinical encounter grounded in a culture of shared decision-making, with the expert healthcare professional team as consultants and the patient as an empowered decision-maker in a trustful safe therapeutic relationship.

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.

Comparison of atherosclerotic plaque burden and composition between diabetic and non diabetic patients by non invasive CT angiography.

Type 2 diabetes mellitus (DM) is associated with a higher risk of cardiovascular disease and atherosclerotic burden. However little data exists in regards to plaque distribution and plaque composition in these patients. To assess for differences in the coronary plaques burden and composition among symptomatic patients with and without type 2 DM using multidetector computed tomography angiography (MDCTA). The 416 symptomatic patients (64% males, mean age: 61 +/- 13 years) with 61 (15%) reporting type 2 DM, who underwent contrast-enhanced MDCTA were studied. Enrolled patients had an intermediate to high pre-test probability of obstructive coronary artery disease. Multivariate analysis was used to correct for differences in age and gender. Patients with type 2 DM were more likely to have significant stenosis >or=70% in at least one coronary segments (33% in type 2 DM vs. 18% in non diabetic, P = 0.013), whereas 11% of both type 2 DM and non diabetics had stenosis of 50-70% (P = NS). Also type 2 DM patients had a higher number of coronary segments with mixed plaques compared to nondiabetic patients (1.67 +/- 2.01 vs. 1.23 +/- 1.61, P = 0.05), whereas no such differences were observed for non-calcified or calcified plaques. Nearly half (43%) of type 2 DM had coronary artery calcium scores (CACS) >or=400 vs. 29% in non diabetic patients (P = 0.03). Patients with type 2 DM tend to have atherosclerotic plaques which are more likely to be mixed in nature. Future studies need to elucidate the prognostic value of differences in plaque characteristics observed according to type 2 diabetic status.