Bone and mineral disease is a key problem in patients with kidney disease. The available clinical parameters are non-specific, unproven for the assessment of the bone metabolism and do not reflect the complexity and diversity of the underlying bone pathology.
In this study bone histology parameters including results of tetracycline labeling will be compared to non-invasive parameters of bone metabolism (e.g. sclerostin, calciprotein particles) and bone structure obtained by imaging (e.g. bone density).
The aim of the study is to establish a reliable decision model (diagnostic tool) with these non-invasive parameters that can be used to guide the individual therapy. The decision model will be based on single calculations of the area under the curve for each parameter, including determination of the optimal cutpoint, sensitivity and specificity. These results will be integrated in a multivariate logistic regression analysis. The resulting model will enable to calculate the individual probability of the underlying bone diagnosis as a single value (without a specific dimension).
