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

Estimating aquatic invertebrate diversity in the southern Alps using data from Biodiversity Days

High biodiversity is a prerequisite for the integrity, stability, and functioning of global aquatic ecosystems, but it is currently subject to anthropogenic threats. Small freshwater bodies with high habitat diversity are essential to sustain regional biodiversity, but species inventory and biodiversity are largely overlooked, especially in mountainous regions. In the Italian Alps, obligate assessments of freshwater biota (e.g., for the European water framework directive, WFD) are usually done in larger rivers or lakes only, which is why many taxa from small freshwater habitats might have been overlooked so far. Here we summarize and discuss the efforts to record aquatic invertebrates within the framework of so-called "Biodiversity Days", organized since 2001 at 13 different sites located across the North Italian province of South Tyrol. These events with voluntary participation of scientists and naturalists from universities and environmental agencies led to the detection of 334 benthic invertebrate taxa in streams and lakes (mostly species or genus level), whereby higher taxa richness was found in streams. The overall hierarchy of species numbers within invertebrate orders or families corresponded to that of other Alpine regions (groups richest in taxa were Chironomidae and Trichoptera) and these Biodiversity Days contributed to biodiversity research of that region in detecting 167 additional taxa. Besides analyzing yearly gains in the regional taxa inventory, we predict that future surveys will lead to new discoveries of aquatic taxa for that province (i.e., current modeling estimates a regional inventory of more than 600 taxa). However, specific surveys in hitherto unconsidered habitats, such as morphologically modified or urban waters, might reveal even more taxa than currently estimated. Besides characterizing the invertebrate fauna of this region and providing a first reference list for future monitoring projects in the same region, this work demonstrates that such Biodiversity Days can contribute to biodiversity research

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