Dr. Kohane has received consultant fees, speaking fees, and/or honoraria (less than $10,000) from Merck. Dr. Plenge has received consultant fees, speaking fees, and/or honoraria (less than $10,000) from Biogen-Idec.
Electronic medical records for discovery research in rheumatoid arthritis†
Version of Record online: 16 MAR 2010
Copyright © 2010 by the American College of Rheumatology
Arthritis Care & Research
Volume 62, Issue 8, pages 1120–1127, August 2010
How to Cite
Liao, K. P., Cai, T., Gainer, V., Goryachev, S., Zeng-treitler, Q., Raychaudhuri, S., Szolovits, P., Churchill, S., Murphy, S., Kohane, I., Karlson, E. W. and Plenge, R. M. (2010), Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res, 62: 1120–1127. doi: 10.1002/acr.20184
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library of Medicine or the NIH.
- Issue online: 3 AUG 2010
- Version of Record online: 16 MAR 2010
- Manuscript Accepted: 5 MAR 2010
- Manuscript Received: 7 NOV 2009
- National Library of Medicine. Grant Number: U54LM008748
- NIH. Grant Numbers: T32-AR055885, R01-LM007222, R01-DK075837, R01-LM009966, R21-NR0101710-01, R21-NS067463, K08-AR-055688-01A1, UL1-RR02578-01, R01-HL091495-01A1, R01-AR049880, P60-AR047782, K24-AR0524-01, R01-AR057108, R01-AR056768, U54-LM00878)
- i2b2. Grant Number: (NIH U54-LM008748)
- Career Award for Medical Scientists from the Burroughs Wellcome Fund
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 ≥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.