Elderly age is not a negative predictive factor for virological response to therapy with pegylated interferon-α and ribavirin in chronic hepatitis C virus patients

BACKGROUND & AIMS: Age is frequently discussed as negative host factor to achieve a sustained virological response (SVR) to antiviral therapy of chronic hepatitis C. However, elderly patients often show advanced fibrosis/cirrhosis as known negative predictive factor. The aim of this study was to assess age as an independent predictive factor during antiviral therapy. METHODS: Overall, 516 hepatitis C patients were treated with pegylated interferon-α and ribavirin, thereof 66 patients ≥60 years. We analysed the impact of host factors (age, gender, fibrosis, haemoglobin, previous hepatitis C treatment) and viral factors (genotype, viral load) on SVR per therapy course by performing a generalized estimating equations (GEE) regression modelling, a matched pair analysis and a classification tree analysis. RESULTS: Overall, SVR per therapy course was 42.9 and 26.1%, respectively, in young and elderly patients with hepatitis C virus (HCV) genotypes 1/4/6. The corresponding figures for HCV genotypes 2/3 were 74.4 and 84%. In the GEE model, age had no significant influence on achieving SVR. In matched pair analysis, SVR was not different in young and elderly patients (54.2 and 55.9% respectively; P = 0.795 in binominal test). In classification tree analysis, age was not a relevant splitting variable. CONCLUSIONS: Age is not a significant predictive factor for achieving SVR, when relevant confounders are taken into account. As life expectancy in Western Europe at age 60 is more than 20 years, it is reasonable to treat chronic hepatitis C in selected elderly patients with relevant fibrosis or cirrhosis but without major concomitant diseases, as SVR improves survival and reduces carcinogenesis.

A new universal, standardized implant database for product identification: a unique tool for arthroplasty registries.

INTRODUCTION Every joint registry aims to improve patient care by identifying implants that have an inferior performance. For this reason, each registry records the implant name that has been used in the individual patient. In most registries, a paper-based approach has been utilized for this purpose. However, in addition to being time-consuming, this approach does not account for the fact that failure patterns are not necessarily implant specific but can be associated with design features that are used in a number of implants. Therefore, we aimed to develop and evaluate an implant product library that allows both time saving barcode scanning on site in the hospital for the registration of the implant components and a detailed description of implant specifications. MATERIALS AND METHODS A task force consisting of representatives of the German Arthroplasty Registry, industry, and computer specialists agreed on a solution that allows barcode scanning of implant components and that also uses a detailed standardized classification describing arthroplasty components. The manufacturers classified all their components that are sold in Germany according to this classification. The implant database was analyzed regarding the completeness of components by algorithms and real-time data. RESULTS The implant library could be set up successfully. At this point, the implant database includes more than 38,000 items, of which all were classified by the manufacturers according to the predefined scheme. Using patient data from the German Arthroplasty Registry, several errors in the database were detected, all of which were corrected by the respective implant manufacturers. CONCLUSIONS The implant library that was developed for the German Arthroplasty Registry allows not only on-site barcode scanning for the registration of the implant components but also its classification tree allows a sophisticated analysis regarding implant characteristics, regardless of brand or manufacturer. The database is maintained by the implant manufacturers, thereby allowing registries to focus their resources on other areas of research. The database might represent a possible global model, which might encourage harmonization between joint replacement registries enabling comparisons between joint replacement registries.

Time-lapse microscopy and classification of 2D human mesenchymal stem cells based on cell shape picks up myogenic from osteogenic and adipogenic differentiation

Current methods to characterize mesenchymal stem cells (MSCs) are limited to CD marker expression, plastic adherence and their ability to differentiate into adipogenic, osteogenic and chondrogenic precursors. It seems evident that stem cells undergoing differentiation should differ in many aspects, such as morphology and possibly also behaviour; however, such a correlation has not yet been exploited for fate prediction of MSCs. Primary human MSCs from bone marrow were expanded and pelleted to form high-density cultures and were then randomly divided into four groups to differentiate into adipogenic, osteogenic chondrogenic and myogenic progenitor cells. The cells were expanded as heterogeneous and tracked with time-lapse microscopy to record cell shape, using phase-contrast microscopy. The cells were segmented using a custom-made image-processing pipeline. Seven morphological features were extracted for each of the segmented cells. Statistical analysis was performed on the seven-dimensional feature vectors, using a tree-like classification method. Differentiation of cells was monitored with key marker genes and histology. Cells in differentiation media were expressing the key genes for each of the three pathways after 21 days, i.e. adipogenic, osteogenic and chondrogenic, which was also confirmed by histological staining. Time-lapse microscopy data were obtained and contained new evidence that two cell shape features, eccentricity and filopodia (= 'fingers') are highly informative to classify myogenic differentiation from all others. However, no robust classifiers could be identified for the other cell differentiation paths. The results suggest that non-invasive automated time-lapse microscopy could potentially be used to predict the stem cell fate of hMSCs for clinical application, based on morphology for earlier time-points. The classification is challenged by cell density, proliferation and possible unknown donor-specific factors, which affect the performance of morphology-based approaches. Copyright © 2012 John Wiley & Sons, Ltd.