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Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about 2.8 million people worldwide. Disease course after the most common diagnoses of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) is highly variable and cannot be reliably predicted. This impairs early personalized treatment decisions. Objectives: The main objective of this study was to algorithmically support clinical decision-making regarding the options of early platform medication or no immediate treatment of patients with early RRMS and CIS. Design: Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. Methods: Multiple data sources of routine clinical, imaging and laboratory data derived from a large and deeply characterized cohort of patients with MS were integrated to conduct a retrospective study to create and internally validate a treatment decision score [Multiple Sclerosis Treatment Decision Score (MS-TDS)] through model-based random forests (RFs). The MS-TDS predicts the probability of no new or enlarging lesions in cerebral magnetic resonance images (cMRIs) between 6 and 24 months after the first cMRI. Results: Data from 65 predictors collected for 475 patients between 2008 and 2017 were included. No medication and platform medication were administered to 277 (58.3%) and 198 (41.7%) patients. The MS-TDS predicted individual outcomes with a cross-validated area under the receiver operating characteristics curve (AUROC) of 0.624. The respective RF prediction model provides patient-specific MS-TDS and probabilities of treatment success. The latter may increase by 5-20% for half of the patients if the treatment considered superior by the MS-TDS is used. Conclusion: Routine clinical data from multiple sources can be successfully integrated to build prediction models to support treatment decision-making. In this study, the resulting MS-TDS estimates individualized treatment success probabilities that can identify patients who benefit from early platform medication. External validation of the MS-TDS is required, and a prospective study is currently being conducted. In addition, the clinical relevance of the MS-TDS needs to be established.
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Background and Aims: Esophageal adenocarcinoma (EAC) incidence has risen dramatically in the Western countries over the past decades. The underlying reasons are incompletely understood, and shifts in the esophageal microbiome have been postulated to increase predisposition to disease development. Multiple factors including medications, lifestyle, and diet could influence microbiome composition and disease progression. The aim of this study was (1) to identify a feasible method to characterize the tissue-associated microbiome, and (2) to investigate differences in the microbiome of saliva, esophageal tissue, and fecal samples by disease state and validate with 2 external cohorts. Methods: Forty-eight patients (15 Barrett's esophagus [BE], 4 dysplasia, 15 EAC, and 14 healthy) were enrolled in this cross-sectional study (Munich cohort). Demographics, epidemiologic and clinical data, medications, smoking, and alcohol consumption were assessed. 16S rRNA Gene sequencing was performed on saliva, tissue biopsy and fecal samples. PAXgene fixation was used as a novel methodology. Microbial community alpha- and beta-diversity, as well as microbial composition at phylum and genus level, were characterized for this cohort and compared with 2 external cohorts: New York cohort and Cooperative Health Research in the Augsburg Region cohort. Results: We first established PAXgene fixation is a feasible method for microbiome analysis and utilized it to identify a distinct microbial shift in tissue biopsies from patients with EAC, whereas overall microbial diversity in salivary and fecal samples did not differ significantly between disease states. Our findings were similar in a reanalysis to those from a US cohort that used a standardized fresh frozen biopsy collection protocol (New York cohort, N = 75 biopsies). Nevertheless, we could not distinguish German Munich cohort patients from a German population-based cohort (Cooperative Health Research in the Augsburg Region cohort, N = 2140 individuals) when fecal bacterial profiles were compared between both cohorts. In addition, we used data integration of diagnosis and risk factors of patients and found associations with microbiome alterations. Conclusion: Sample collection and microbiome analysis are indeed feasible and can be implemented into clinical routine by an easy-to-use biopsy protocol. The presence of BE and EAC together with epidemiologic factors can be associated with alterations of the salivary, tissue, and fecal microbial community in an easy-to-use data integration concept. Given a possible role of the microbiome in BE and EAC, it will be important in future studies to take tissue-specific microbial communities and individual taxa into account in larger prospective studies.
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BACKGROUND: Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. OBJECTIVE: Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. METHODS: We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. RESULTS: The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. CONCLUSIONS: Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.