Evaluation of an automated phenotyping algorithm for rheumatoid arthritis.

To better understand the challenges of generally implementing and adapting computational phenotyping approaches, the performance of a Phenotype KnowledgeBase (PheKB) algorithm for rheumatoid arthritis (RA) was evaluated on a University of California, Los Angeles (UCLA) patient population, focusing on examining its performance on ambiguous cases. The algorithm was evaluated on a cohort of 4,766 patients, along with a chart review of 300 patients by rheumatologists against accepted diagnostic guidelines. The performance revealed low sensitivity towards specific subtypes of positive RA cases, which suggests revisions in features used for phenotyping. A close examination of select cases also indicated a significant portion of patients with missing data, drawing attention to the need to consider data integrity as an integral part of phenotyping pipelines, as well as issues around the usability of various codes for distinguishing cases. We use patterns in the PheKB algorithm's errors to further demonstrate important considerations when designing a phenotyping algorithm.

Core Outcome Sets Specifically for Longterm Observational Studies: OMERACT Special Interest Group Update in Rheumatoid Arthritis.

OBJECTIVE: This is an update from the Outcome Measures in Rheumatology (OMERACT) Core Outcomes in Longterm Observational Studies Special Interest Group with a focus on rheumatoid arthritis. METHODS: Preliminary data and proposed next steps were outlined and discussed by participants. RESULTS: Domains identified after initial steps (systematic review and qualitative research) were pain, physical functioning, participation (i.e., work, social), longterm symptoms, fertility/family planning, emotional well-being, coping, financial status, and adverse events including death. CONCLUSION: The group agreed conceptually that short-term core outcomes could be different from longer term ones. Participants emphasized the importance of analyzing the need for core domains specifically for longterm longitudinal observational studies.