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Journal article
Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry
Arthritis care & research (2010), v 73(4), pp 471-480
Apr 2021
PMID: 33002337
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
To use unbiased, data-driven, principal component (PC) and cluster analysis to identify patient phenotypes of rheumatoid arthritis (RA) that might exhibit distinct trajectories of disease progression, response to treatment, and risk for adverse events.
Patient demographic, socioeconomic, health, and disease characteristics recorded at entry into a large, single-center, prospective observational registry cohort, the Brigham and Women's Rheumatoid Arthritis Sequential Study (BRASS), were harmonized using PC analysis to reduce dimensionality and collinearity. The number of PCs was established by eigenvalue >1, cumulative variance, and interpretability. The resulting PCs were used to cluster patients using a K-means approach. Longitudinal clinical outcomes were compared between the clusters over 2 years.
Analysis of 142 variables from 1,443 patients identified 41 PCs that accounted for 77% of the cumulative variance in the data set. Cluster analysis distinguished 5 patient clusters: 1) less RA disease activity/multimorbidity, shorter RA duration, lower incidence of comorbidities; 2) less RA disease activity/multimorbidity, longer RA duration, more infections, psychiatric comorbidities, health care utilization; 3) moderate RA disease activity/multimorbidity, more neurologic comorbidity; 4) more RA disease activity/multimorbidity, shorter RA duration, more metabolic comorbidity, higher body mass index; 5) more RA disease activity/multimorbidity, longer RA duration, more hepatic, orthopedic comorbidity and RA-related surgeries. The clusters exhibited differences in clinical outcomes over 2 years of follow-up.
Data-driven analysis of the BRASS registry identified 5 distinct phenotypes of RA. These results illustrate the potential of data-driven patient profiling as a tool to support personalized medicine in RA. Validation in an independent data set is ongoing.
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Details
- Title
- Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry
- Creators
- Jeffrey R Curtis - University of Alabama at BirminghamMichael Weinblatt - Brigham and Women's HospitalKenneth Saag - University of Alabama at BirminghamVivian P Bykerk - Hospital for Special SurgeryDaniel E Furst - University of California Los Angeles University of Washington, Seattle, and University of Florence Florence ItalyStefano Fiore - SanofiGregory St John - Regeneron Pharmaceuticals, Tarrytown, New YorkToshio Kimura - RegeneronShen Zheng - SanofiClifton O Bingham, 3rd - Johns Hopkins University, Baltimore, MarylandGrace Wright - Private Practice New York New YorkMartin Bergman - Drexel UniversityKamala Nola - Lipscomb UniversityChristina Charles-Schoeman - University of California, Los AngelesNancy Shadick - Brigham and Women's Hospital
- Publication Details
- Arthritis care & research (2010), v 73(4), pp 471-480
- Publisher
- Wiley
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Medicine (Graduate)
- Web of Science ID
- WOS:000628382000001
- Scopus ID
- 2-s2.0-85102437172
- Other Identifier
- 991019169113904721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Rheumatology