Inferring Rheumatoid Arthritis Disease Activity Status From the Electronic Health Records Across Health Systems
Year: 2026
Type: article
Abstract: OBJECTIVE: Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. The inconsistent availability of data on disease activity in real-world electronic health records (EHRs) data has limited the ability to generate real-world evidence (RWE). This study aimed to develop and validate scalable machine learning (ML) models to infer RA disease activity from EHR data. METHODS: We used EHR data from Mass General Brigham (MGB) and the Department of Veterans Affairs (VA) linked with RA registries that prospectively collected the Disease Activity Score with 28-joint counts (DAS28). Features for the algorithm were extracted from the EHRs including structured data (eg, diagnosis codes and narrative data using natural language processing [NLP]). ML models were trained on the registry-collected DAS28. The performance of models trained within the same institution and across institutions was evaluated. To assess face validity, we estimated the association between inferred disease activity and major adverse cardiovascular events (MACEs) with stratified Cox models. RESULTS: We studied 1,105 MGB and 2,631 VA patients with RA. Models with structured data achieved an area under the receiver operating curve (AUC) of 0.68 to 0.70; models incorporating structured and NLP achieved higher performance (MGB, AUC = 0.843; VA, AUC = 0.833). Cross-institution validation demonstrated limited transportability of algorithms across sites (MGB→VA, AUC = 0.679; VA→MGB, AUC = 0.718). Within the same institution, inferred disease activity was significantly associated with increased risk for incident MACEs (MGB, hazard ratio [HR] = 1.12; VA, HR = 1.14). CONCLUSION: RA disease activity can be inferred at scale from within-institution EHR data, though cross-institution performance is limited. The inferred disease activity replicated known associations with MACEs, and the results support its use in future studies to generate RWE.
Source: Arthritis & Rheumatology
DOI: 10.1002/art.70181
Authors: David Cheng, Xuan Wang, Gregory C. McDermott, Jennifer S. Hanberg, Zoe Love, Katherine Zhong, Mary Jeffway, Jue Hou, Vidul Panickan, Rahul Sangar, Ying Qi, Connor Melley, Lauren Costa, Dakota Feil, Rachael Matty, Dana Weisenfeld, Abisayo Animashaun, Aimee Schreiner, Sara Morini, Lauren Rusnak, Andrew Cagan, Misti Paudel, J. Michael Gaziano, Brian Sauer, Michael Weinblatt, Joshua Baker, Bryant England, Yuk‐Lam Ho, Kelly Cho, Paul Monach, Grant W. Cannon, Nancy Shadick, Ted R. Mikuls, Tianxi Cai, Katherine P. Liao
Institutions: Massachusetts General Hospital, University of Utah, Brigham and Women's Hospital, VA Boston Healthcare System, University of Minnesota, Harvard University, Hebrew SeniorLife, Omaha VA Medical Center, VA Nebraska Western Iowa Health Care System, University of Nebraska Medical Center, VA Salt Lake City Healthcare System, Philadelphia VA Medical Center, University of Pennsylvania
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,190
Awards: G6605806092, Bridging clinical trial and real-world data via machine learning to advance rheumatoid arthritis treatment strategies, Mentoring Patient-Oriented Research Leveraging Bioinformatics to Study CV Risk in Rheumatic Disease, VERITY: Value and Evidence in Rheumatology using bioInformaTics, and advanced analYtics
OpenAlex ID: W7154172220