Abstract
OBJECTIVES: Sickle cell disease (SCD) is a rare group of inherited red blood cell disorders that affect hemoglobin, resulting in serious multi-system complications. The limited number of patients available to participate in research studies can inhibit investigating sophisticated relationships. Secondary analysis is a research method that involves using existing data to answer new research questions. Data harmonization enables secondary analysis by combining data across studies, especially helpful for rare disease research where individual studies may be small. The National Heart, Lung, and Blood Institute Cure Sickle Cell Initiative (CureSCi) Metadata Catalog is a web-based tool to identify SCD study datasets for conducting data harmonization and secondary analysis. We present a proof-of-concept secondary analysis to explore factors associated with discontinuation of hydroxyurea, a safe and effective first line SCD therapy, to illustrate the utility of the CureSCi Metadata Catalog to expedite and enable more robust SCD research. METHODS: We performed secondary analysis of SCD studies using a multi-step workflow: develop research questions, identify study datasets, identify variables of interest, harmonize variables, and establish an analysis method. A harmonized dataset consisting of eight predictor variables across five studies was created. Secondary analysis employed a generalized linear model to identify factors that significantly impact hydroxyurea discontinuation. RESULTS: The CureSCi Metadata Catalog provided a platform to efficiently find relevant studies and design a harmonization strategy to prepare data for secondary analysis. Multivariate analysis of the harmonized data identified that patients who were female, had a history of blood transfusion therapy, experiencing pain, and had the SC sickle cell genotype are more likely to stop hydroxyurea treatment. CONCLUSION: This secondary analysis provides a template for how the CureSCi Metadata Catalog expedites dataset discovery of sickle cell studies for identifying relationships between variables or validating existing findings.