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

Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption

Calibration is a vital aspect of the performance of risk prediction models, but research in the context of ordinal outcomes is scarce. This study compared calibration measures for risk models predicting a discrete ordinal outcome, and investigated the impact of the proportional odds assumption on calibration and overfitting. We studied the multinomial, cumulative, adjacent category, continuation ratio, and stereotype logit/logistic models. To assess calibration, we investigated calibration intercepts and slopes, calibration plots, and the estimated calibration index. Using large sample simulations, we studied the performance of models for risk estimation under various conditions, assuming that the true model has either a multinomial logistic form or a cumulative logit proportional odds form. Small sample simulations were used to compare the tendency for overfitting between models. As a case study, we developed models to diagnose the degree of coronary artery disease (five categories) in symptomatic patients. When the true model was multinomial logistic, proportional odds models often yielded poor risk estimates, with calibration slopes deviating considerably from unity even on large model development datasets. The stereotype logistic model improved the calibration slope, but still provided biased risk estimates for individual patients. When the true model had a cumulative logit proportional odds form, multinomial logistic regression provided biased risk estimates, although these biases were modest. Nonproportional odds models require more parameters to be estimated from the data, and hence suffered more from overfitting. Despite larger sample size requirements, we generally recommend multinomial logistic regression for risk prediction modeling of discrete ordinal outcomes

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

Relationship of sonographic wall components of the brachial artery to hypertension and coronary atherosclerosis

International audienceThe aim of this study was to determine whether sonographically assessed intimal (echodense, ED) or medial (echolucent, EL) thickening of the brachial artery is associated with coronary artery disease (CAD) and/or arterial hypertension (HT). In 201 patients the ED and EL wall components, as well as the total wall thickness of the brachial artery, were measured with high-resolution ultrasound (13 MHz). According to the presence or absence of CAD and HT, the patients were divided into four groups: no HT and no CAD (n = 26, group 1), CAD (≥30% diameter stenosis in ≥1 major branch) only (n = 63, group 2), HT only (n = 34, group 3), and HT and CAD (n = 78, group 4). EL ( < 0.001) and combined wall thickness ( < 0.001), but not the ED wall component, were significantly different between the groups, with the highest values occurring in group 4. On logistic regression analyses adjusting for age, coronary risk factors and body mass index, EL, but not ED, thickness correlated independently with the presence of CAD (p = 0.04) and HT ( < 0.001). High-resolution ultrasound examination of the brachial artery wall structure may contribute to the noninvasive assessment of early atherosclerosis