JAMIA open · 2026
Assessing data quality of inflammatory bowel disease patients in the All of Us research program.
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Spotnitz Matthew, Faye Adam S, Giannini John, Litwin Tamara R, Ostchega Yechiam, Berman Lew
Аннотация
PURPOSE: Inflammatory bowel disease (IBD) consists of Crohn's disease (CD) and ulcerative colitis (UC) and is a spectrum autoimmune disease of the gastrointestinal tract. Large scale real-world evidence studies could provide valuable evidence about IBD for personalized healthcare recommendations. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standardizes electronic health record (EHR) data, allowing for research that incorporates multiple data sources. We are interested in whether OMOP CDM data on IBD are fit-for-use.
METHODS: We selected IBD diagnosis codes to define the phenotype. We used a data quality checklist to evaluate 5 domains: conformance, completeness, concordance, plausibility, and temporality. We also did sensitivity analyses for CD and UC that consisted of at least 2 diagnosis codes that were at least 30 days apart.
RESULTS: All of the phenotype-defining ICD source codes mapped to SNOMED. Many concept prevalences were low. A total of 78 (30.1%) out of 253 concept correlations were above our strength threshold (⍴ > 0.5). The age distribution of concepts and relative frequency of IBD medications were plausible. The median time between diagnosis and biopsy for the cohort was 4.43 [-0.05, 104.29] weeks. For the subgroup of participants who had sufficient data for the timeline analysis, IBD diagnosis concepts tended to occur first. In our sensitivity analyses, the completeness percentages of many variables in the UC and CD subgroups were similar to IBD, except for disease specific workup and treatment concepts.
CONCLUSION: We have shown a novel implementation of our data quality framework on IBD cohorts.
METHODS: We selected IBD diagnosis codes to define the phenotype. We used a data quality checklist to evaluate 5 domains: conformance, completeness, concordance, plausibility, and temporality. We also did sensitivity analyses for CD and UC that consisted of at least 2 diagnosis codes that were at least 30 days apart.
RESULTS: All of the phenotype-defining ICD source codes mapped to SNOMED. Many concept prevalences were low. A total of 78 (30.1%) out of 253 concept correlations were above our strength threshold (⍴ > 0.5). The age distribution of concepts and relative frequency of IBD medications were plausible. The median time between diagnosis and biopsy for the cohort was 4.43 [-0.05, 104.29] weeks. For the subgroup of participants who had sufficient data for the timeline analysis, IBD diagnosis concepts tended to occur first. In our sensitivity analyses, the completeness percentages of many variables in the UC and CD subgroups were similar to IBD, except for disease specific workup and treatment concepts.
CONCLUSION: We have shown a novel implementation of our data quality framework on IBD cohorts.