TY - JOUR
T1 - Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
AU - Curtis, Jeffrey R.
AU - Weinblatt, Michael
AU - Saag, Kenneth
AU - Bykerk, Vivian P.
AU - Furst, Daniel E.
AU - Fiore, Stefano
AU - St John, Gregory
AU - Kimura, Toshio
AU - Zheng, Shen
AU - Bingham, Clifton O.
AU - Wright, Grace
AU - Bergman, Martin
AU - Nola, Kamala
AU - Charles-Schoeman, Christina
AU - Shadick, Nancy
N1 - Publisher Copyright:
© 2020 The Authors. Arthritis Care & Research published by Wiley Periodicals LLC on behalf of American College of Rheumatology.
PY - 2021/4
Y1 - 2021/4
N2 - Objective: 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. Methods: 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. Results: 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. Conclusion: 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.
AB - Objective: 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. Methods: 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. Results: 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. Conclusion: 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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U2 - 10.1002/acr.24471
DO - 10.1002/acr.24471
M3 - Article
C2 - 33002337
AN - SCOPUS:85102437172
SN - 2151-464X
VL - 73
SP - 471
EP - 480
JO - Arthritis Care and Research
JF - Arthritis Care and Research
IS - 4
ER -