Electronic medical records for discovery research in rheumatoid arthritis

Abstract

Objective: 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.

Methods: 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.

Results: 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).

Conclusion: 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.

Figure 1

Overview of approach for classifying RA subjects in the electronic medical record (EMR). Subjects were selected from the EMR with ≥1 ICD9 RA code or had anti-CCP checked to create the RA Mart. 500 subjects were randomly selected from the RA Mart to undergo medical record review to establish a training set. This training set of RA and non-RA cases was used to develop and train the classification algorithm. The classification algorithm was applied to the entire RA Mart to determine predicted RA cases. 400 subjects were randomly selected from the predicted RA cases to undergo medical record review to determine the PPV of the algorithm (validation set). 279×89mm (96 × 96 DPI)