External validation and extension of a diagnostic model for obstructive coronary artery disease: A cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination in Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort

__Objective__ To externally validate and extend a recently proposed prediction model to diagnose obstructive coronary artery disease (CAD), with the ultimate aim to better select patients for coronary angiography. __Design__ Analysis of individual baseline data of a prospective cardiology cohort. __Setting__ Single-centre secondary and tertiary cardiology clinic. __Participants__ 4888 patients with suspected CAD, without known previous CAD or other heart diseases, who underwent an elective coronary angiography between 2004 and 2008 as part of the prospective Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. Relevant data were recorded as in routine clinical practice. __Main outcome measures__ The probability of obstructive CAD, defined as a stenosis of minimally 50% diameter in at least one of the main coronary arteries, estimated with the predictors age, sex, type of chest pain, diabetes status, hypertension, dyslipidaemia, smoking status and laboratory data. Missing predictor data were multiply imputed. Performance of the suggested models was evaluated according to discrimination (area under the receiver operating characteristic curve, depicted by the c statistic) and calibration. Logistic regression modelling was applied for model updating. __Results__ Among the 4888 participants (38% women and 62% men), 2127 (44%) had an obstructive CAD. The previously proposed model had a c statistic of 0.69 (95% CI 0.67 to 0.70), which was lower than the expected c statistic while correcting for case mix (c=0.80). Regarding calibration, there was overprediction of risk for high-risk patients. All logistic regression coefficients were smaller than expected, especially for the predictor â € chest pain'. Ext

An ordinal prediction model of the diagnosis of non-obstructive coronary artery and multi-vessel disease in the CARDIIGAN cohort

© 2018 Elsevier B.V. Background: The extent of coronary artery disease (CAD) is relevant for the evaluation and the choice of treatment of patients and consists of the severity of stenoses and their distribution within the coronary tree. Diagnosis is not easy and severe CAD should not be missed. For low-risk patients one wants to avoid the invasive angiography. We aim to propose a diagnostic prediction model of CAD respecting the degree of disease severity. Methods: We included 4888 patients from the Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. An ordinal regression model was applied to estimate the probabilities of five incrementally disease categories: no CAD, non-obstructive stenosis, and one-, two- and three-vessel disease. We included 11 predictors in the model: age, sex, chest pain, diabetes, hypertension, dyslipidaemia, smoking, HDL and LDL cholesterol, fibrinogen, and C-reactive protein. Bootstrapping was used to validate model performance (discrimination and calibration). Results: Age, sex, and three laboratory measures had a large predictive effect. The model poorly separated most adjacent disease categories, but performed well for categories far apart, with little optimism. The overall discrimination added up to a c statistic of 0.71 (95% CI 0.69 to 0.73). The model enables the estimation of individual patient probabilities of disease severity categories. Conclusions: The proposed ordinal diagnostic risk model, employing routinely obtainable variables, allows distinguishing the extent of CAD and can especially discriminate between non-obstructive stenosis and multi-vessel disease in our CARDIIGAN patients. This can help to decide on treatment strategy and thereby reduce the number of unnecessary angiographies.status: publishe

HMG-CoA reductase inhibitors regulate inflammatory transcription factors in human endothelial and vascular smooth muscle cells

Objective-Pleiotropic atheroprotective effects of HMG-CoA reductase inhibitors may be mediated on the level of vascular gene transcription. The aim of this study was to characterize the effects of statins on the activation of transcription factors known to regulate inflammation and cell proliferation/differentiation. Methods and Results-Simvastatin, atorvastatin, and lovastatin (0.1 to 10 mumol/L) inhibited the binding of nuclear proteins to both the nuclear factor-kappa B (NF-kappaB) and activator protein-1 (AP-1) DNA consensus oligonucleotides in human endothelial and vascular smooth muscle cells as assessed by electrophoretic mobility shift assay (EMSA). The inhibitory effects of statins on NF-kappaB or AP-1-dependent transcriptional activity were examined by transient transfection studies. HMG-CoA reductase inhibitors upregulated IkappaB-alpha protein levels in endothelial cells and decreased c-Jun mRNA expression in smooth muscle cells as analyzed by Western and Northern blotting, respectively. Furthermore, statins inhibited DNA binding of hypoxia-inducible factor-1alpha. Downstream effects of statins included inhibition of plasminogen activator inhibitor-1 and vascular endothelial growth factor-A mRNA levels in endothelial cells. Conclusions-HMG-CoA reductase inhibitors downregulate the activation of transcription factors NF-kappaB, AP-1, and hypoxia-inducible factor-1alpha. These, findings support the concept that statins have antiinflammatory and antiproliferative effects that are relevant in the treatment of atherosclerotic diseases