Background/Purpose: The presence of erosive disease influences diagnosis, management, & prognosis in inflammatory arthritis (IA). Inflammatory arthritis research in large datasets is limited by a lack of methods for identifying erosions.

The aim of our project was to develop methods for identifying articular erosions in radiology reports from veterans with IA.

Methods: Included veterans had ≥2 ICD codes for ankylosing spondylitis (AS), psoriatic arthritis (PsA), or rheumatoid arthritis (RA) between 2005- 2019, in Veterans Affairs Corporate Data Warehouse. Chart review & annotation of radiology notes produced the reference standard, & identified erosion terms that informed classification rule development. A rule-based natural language processing (NLP) model was created & revised in training snippets. The NLP method was validated in an independent reference sample of IA patients at the snippet & patient levels.

Results: In 168,667 veterans with inflammatory arthritis, the mean age was 63.1 & 90.3% were male. Method development involved radiology note & erosion term selection, rule development, NLP model building, & method validation. The NLP model accuracy was more than 95% at the snippet level & 90.0% at the patient level, for all IA patients.

Conclusion: The methods accurately identify erosions from radiology reports of veterans with inflammatory arthritis. They may facilitate a broad range of research involving cohort identification & disease severity stratification.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/identifying-erosive-disease-from-radiology-reports-of-veterans-with-inflammatory-arthritis-with-natural-language-processing/