Measuring and comparing the (in)efficiency of German and Swiss hospitals

A nonparametric Data Envelopment Analysis (DEA) is performed on hospitals in the federal state of Saxony (Germany) and in Switzerland. This study is of interest from three points of view. First, contrary to most existing work, patient days are not treated as an output but as an input. Second, the usual DEA assumption of a homogeneous sample is tested and rejected for a large part of the observations. The proposed solution is to restrict DEA to comparable observations in the two countries. Finally, hospital beds are treated as a discretionary input in one DEA and as a fixed input in the other, and the effect on efficiency is related to differences in hospital planning in Germany and Switzerland. Based on the comparable observations, hospitals of Saxony have higher efficiency scores than their Swiss counterparts. --International efficiency comparison,Hospitals,Data Envelopment Analysis

Effiziente Krankenhäuser? : ein Vergleich sächsischer und schweizerischer Krankenhäuser

Internationale Vergleiche von Krankenhausleistungen sind bislang selten. Die vorliegende Analyse untersucht die Effizienz der Krankenhäuser in Sachsen und der Schweiz mittels der Data Envelopment Analysis (DEA), einem Verfahren zur Ermittlung der effizienten Grenze. Dabei wird im Sinne eines Benchmarkings die Ineffizienz von Krankenhäusern in Relation zu der aus effizienten Krankenhäusern gebildeten Grenze gemessen. Die Untersuchung berücksichtigt den Einfluss institutioneller Gegebenheiten in beiden Ländern, wie z. B. die Vergütung von Krankenhausleistungen und die Krankenhausplanung. Die schweizerischen Krankenhäuser werden überraschenderweise als weniger effizient in der Erstellung eines gegebenen Leistungsbündels identifiziert.Krankhaus; Benchmarking; Wirtschaftliche Effizienz; Vergleich; Sachsen; Schweiz

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

The TREAT-NMD advisory committee for therapeutics (TACT): an innovative de-risking model to foster orphan drug development

Despite multiple publications on potential therapies for neuromuscular diseases (NMD) in cell and animal models only a handful reach clinical trials. The ability to prioritise drug development according to objective criteria is particularly critical in rare diseases with large unmet needs and a limited numbers of patients who can be enrolled into clinical trials. TREAT-NMD Advisory Committee for Therapeutics (TACT) was established to provide independent and objective guidance on the preclinical and development pathway of potential therapies (whether novel or repurposed) for NMD. We present our experience in the establishment and operation of the TACT. TACT provides a unique resource of recognized experts from multiple disciplines. The goal of each TACT review is to help the sponsor to position the candidate compound along a realistic and well-informed plan to clinical trials, and eventual registration. The reviews and subsequent recommendations are focused on generating meaningful and rigorous data that can enable clear go/no-go decisions and facilitate longer term funding or partnering opportunities. The review process thereby acts to comment on viability, de-risking the process of proceeding on a development programme. To date TACT has held 10 review meeting and reviewed 29 program applications in several rare neuromuscular diseases: Of the 29 programs reviewed, 19 were from industry and 10 were from academia; 15 were for novel compounds and 14 were for repurposed drugs; 16 were small molecules and 13 were biologics; 14 were preclinical stage applications and 15 were clinical stage applications. 3 had received Orphan drug designation from European Medicines Agency and 3 from Food and Drug Administration. A number of recurrent themes emerged over the course of the reviews and we found that applicants frequently require advice and education on issues concerned with preclinical standard operating procedures, interactions with regulatory agencies, formulation, repurposing, clinical trial design, manufacturing and ethics. Over the 5 years since its establishment TACT has amassed a body of experience that can be extrapolated to other groups of rare diseases to improve the community's chances of successfully bringing new rare disease drugs to registration and ultimately to marke

An integer linear programming approach for finding deregulated subgraphs in regulatory networks

Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players

Developmental roadmap for antimicrobial susceptibility testing systems

Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability, optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions

Measuring and comparing the (in)efficiency of German and Swiss hospitals

A nonparametric data envelopment analysis (DEA) is performed on hospitals in the federal state of Saxony (Germany) and in Switzerland. This study is of interest from three points of view. First, contrary to most existing work, patient days are not treated as an output but as an input. Second, the usual DEA assumption of a homogeneous sample is tested and rejected for a large part of the observations. The proposed solution is to restrict DEA to comparable observations in the two countries. The finding continues to be that hospitals of Saxony have higher efficiency scores than their Swiss counterparts. The finding proves robust with regard to modifications of DEA that are motivated by differences in hospital planning in Germany and Switzerland