Electronic medical records for discovery research in rheumatoid arthritis

TLDR

Electronic medical records are a rich but underutilized source for discovery research because extracting highly accurate clinical data is difficult. The study evaluated whether incorporating narrative EMR data into a classification algorithm improves rheumatoid arthritis identification compared to using codified data alone. Researchers extracted narrative and codified RA information from 29,432 subjects, applied natural language processing and logistic regression on a training set of 96 RA and 404 non‑RA cases, and then applied the resulting algorithms to the full cohort. The combined narrative‑codified algorithm achieved a 94 % positive predictive value, significantly higher than the 88 % PPV of the codified‑only algorithm, and identified a cohort with characteristics similar to existing RA populations.

Abstract

Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone.Subjects with > or =1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti-cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an "RA Mart" (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms.A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions).We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone.