Utilizing biologic disease-modifying anti-rheumatic treatment sequences to subphenotype rheumatoid arthritis

• Published in: Arthritis research & therapy Vol. 25; no. 1; pp. 93 - 7
• Main Authors: Das, Priyam, Weisenfeld, Dana, Dahal, Kumar, De, Debsurya, Feathers, Vivi, Coblyn, Jonathan S., Weinblatt, Michael E., Shadick, Nancy A., Cai, Tianxi, Liao, Katherine P.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 02.06.2023; BioMed Central; BMC
• Subjects: Abatacept - therapeutic use; Antirheumatic agents; Antirheumatic Agents - therapeutic use; Arthritis; Arthritis, Rheumatoid - drug therapy; Biologic disease-modifying anti-rheumatic drugs; Biological Products - therapeutic use; Complications and side effects; Computerized physician order entry; Disease; Drug therapy; Electronic health record; Electronic health records; Humans; Immunotherapy; Markov analysis; Markov chain; Medication prescriptions; Middle Aged; Mixture model; Monoclonal antibodies; Patient outcomes; Patients; Prescriptions; Probability; Rheumatoid arthritis; Rituximab - therapeutic use; United States; United States > US; Markov chain; Mixture model; Biologic disease-modifying anti-rheumatic drugs; Electronic health record; Medication prescriptions; Rheumatoid arthritis
• DOI: 10.1186/s13075-023-03072-0
• ISSN: 1478-6362; 1478-6354

Many patients with rheumatoid arthritis (RA) require a trial of multiple biologic disease-modifying anti-rheumatic drugs (bDMARDs) to control their disease. With the availability of several bDMARD options, the history of bDMARDs may provide an alternative approach to understanding subphenotypes of RA. The objective of this study was to determine whether there exist distinct clusters of RA patients based on bDMARD prescription history to subphenotype RA. We studied patients from a validated electronic health record-based RA cohort with data from January 1, 2008, through July 31, 2019; all subjects prescribed ≥ 1 bDMARD or targeted synthetic (ts) DMARD were included. To determine whether subjects had similar b/tsDMARD sequences, the sequences were considered as a Markov chain over the state-space of 5 classes of b/tsDMARDs. The maximum likelihood estimator (MLE)-based approach was used to estimate the Markov chain parameters to determine the clusters. The EHR data of study subjects were further linked with a registry containing prospectively collected data for RA disease activity, i.e., clinical disease activity index (CDAI). As a proof of concept, we tested whether the clusters derived from b/tsDMARD sequences correlated with clinical measures, specifically differing trajectories of CDAI. We studied 2172 RA subjects, mean age 52 years, RA duration 3.4 years, and 62% seropositive. We observed 550 unique b/tsDMARD sequences and identified 4 main clusters: (1) TNFi persisters (65.7%), (2) TNFi and abatacept therapy (8.0%), (3) on rituximab or multiple b/tsDMARDs (12.7%), (4) prescribed multiple therapies with tocilizumab predominant (13.6%). Compared to the other groups, TNFi persisters had the most favorable trajectory of CDAI over time. We observed that RA subjects can be clustered based on the sequence of b/tsDMARD prescriptions over time and that the clusters were correlated with differing trajectories of disease activity over time. This study highlights an alternative approach to consider subphenotyping of patients with RA for studies aimed at understanding treatment response.