RESUMO
BACKGROUND: A minimum dataset (MDS) can be determined ad hoc by an investigator or small team; by a metadata expert; or by using a consensus method to take advantage of the global knowledge and expertise of a large group of experts. The first method is the most commonly applied. OBJECTIVE: Here, we describe a use of the third approach using a modified Delphi method to determine the optimal MDS for a dataset of full body computed tomography scans. The scans are of decedents whose deaths were investigated at the New Mexico Office of the Medical Investigator and constitute the New Mexico Decedent Image Database (NMDID). METHODS: The authors initiated the consensus process by suggesting 50 original variables to elicit expert reactions. Experts were recruited from a variety of scientific disciplines and from around the world. Three rounds of variable selection showed high rates of consensus. RESULTS: In total, 59 variables were selected, only 52% of which the original resource authors selected. Using a snowball method, a second set of experts was recruited to validate the variables chosen in the design phase. During the validation phase, no variables were selected for deletion. CONCLUSION: NMDID is likely to remain more "future proof" than if a single metadata expert or only the original team of investigators designed the metadata.
Assuntos
Projetos de Pesquisa , Consenso , Bases de Dados Factuais , Técnica Delphi , New MexicoRESUMO
Patient registries remove barriers to performing research by assembling patient cohorts and data in a systematic, efficient, and proactive manner. Consequently, registries are a valuable strategy for facilitating research and scientific discovery. Registries for rare diseases are arguably even more valuable since there is difficulty in assembling cohorts of adequate size for study. Recently, the NIH Office of Rare Diseases Research created a rare disease registry Standard to facilitate research across multiple registries. We implemented the Standard for the Oculopharyngeal Muscular Dystrophy patient registry created at the University of New Mexico Health Sciences Center. We performed a data element analysis for each Common Data Element defined in the Standard. Problems included the use of previous HL7 versions, non-structured data types, and a recent update to the Standard. Overall, the Standard is an excellent first step toward standardizing patient registries to facilitate work on broader questions and promote novel interdisciplinary collaborations.