Abstract
Abstract As an uncommon cancer, clinical and translational studies of glioma rely on multi-center collaborations, confirmatory studies, and meta-analyses. Unfortunately, interpretation of results across studies is hampered by the absence of uniformly coded clinical data. Common Data Elements (CDE) represent a set of clinical features for which the language has been standardized for consistent data capture across studies, institutions and registries. We constructed CDE for the longitudinal study of adult malignant glioma. To identify the minimum set of CDE needed to describe the clinical course of glioma, we surveyed clinical standards, ongoing trials, published studies, and data repositories for frequently used data elements. We harmonized the identified clinical variables, filled in gaps, and structured them in a modular schema, defining CDE for patient demographics, medical history, diagnosis, surgery, chemotherapy, radiotherapy, other treatments, and outcomes. Multidisciplinary experts from the Glioma Longitudinal AnalySiS (GLASS) consortium, representing clinical, molecular, and data research perspectives, were consulted regarding CDE. The validity and capture feasibility of the CDE were assessed through harmonization across published studies, then validated with single institution retrospective chart abstraction. The refined CDE library is implemented in the Research Electronic Data Capture (REDCap) System, a secure web application for building and managing online surveys and databases. The work was motivated by the GLASS consortium, which supports the aggregation and analysis of complex genetic datasets used to define molecular trajectories for glioma. The goal is that modular REDCap implementation of CDE allows broad adoption in glioma research. To accommodate novel aspects, the CDE sets can be expanded through additional modules. In contrast, for efficient initiation of focused studies, subsets of CDE can be selected. Broad adoption of CDE will improve the ability to compare results and share data between studies, thereby maximizing the value of existing data sources and small patient populations.
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