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Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry

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, socioe...

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Autores principales: Curtis, Jeffrey R., Weinblatt, Michael, Saag, Kenneth, Bykerk, Vivian P., Furst, Daniel E., Fiore, Stefano, St John, Gregory, Kimura, Toshio, Zheng, Shen, Bingham, Clifton O., Wright, Grace, Bergman, Martin, Nola, Kamala, Charles‐Schoeman, Christina, Shadick, Nancy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048846/
https://www.ncbi.nlm.nih.gov/pubmed/33002337
http://dx.doi.org/10.1002/acr.24471
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author Curtis, Jeffrey R.
Weinblatt, Michael
Saag, Kenneth
Bykerk, Vivian P.
Furst, Daniel E.
Fiore, Stefano
St John, Gregory
Kimura, Toshio
Zheng, Shen
Bingham, Clifton O.
Wright, Grace
Bergman, Martin
Nola, Kamala
Charles‐Schoeman, Christina
Shadick, Nancy
author_facet Curtis, Jeffrey R.
Weinblatt, Michael
Saag, Kenneth
Bykerk, Vivian P.
Furst, Daniel E.
Fiore, Stefano
St John, Gregory
Kimura, Toshio
Zheng, Shen
Bingham, Clifton O.
Wright, Grace
Bergman, Martin
Nola, Kamala
Charles‐Schoeman, Christina
Shadick, Nancy
author_sort Curtis, Jeffrey R.
collection PubMed
description 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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spelling pubmed-80488462021-04-20 Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry Curtis, Jeffrey R. Weinblatt, Michael Saag, Kenneth Bykerk, Vivian P. Furst, Daniel E. Fiore, Stefano St John, Gregory Kimura, Toshio Zheng, Shen Bingham, Clifton O. Wright, Grace Bergman, Martin Nola, Kamala Charles‐Schoeman, Christina Shadick, Nancy Arthritis Care Res (Hoboken) Rheumatoid Arthritis 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. John Wiley and Sons Inc. 2021-03-13 2021-04 /pmc/articles/PMC8048846/ /pubmed/33002337 http://dx.doi.org/10.1002/acr.24471 Text en © 2020 The Authors. Arthritis Care & Research published by Wiley Periodicals LLC on behalf of American College of Rheumatology. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Rheumatoid Arthritis
Curtis, Jeffrey R.
Weinblatt, Michael
Saag, Kenneth
Bykerk, Vivian P.
Furst, Daniel E.
Fiore, Stefano
St John, Gregory
Kimura, Toshio
Zheng, Shen
Bingham, Clifton O.
Wright, Grace
Bergman, Martin
Nola, Kamala
Charles‐Schoeman, Christina
Shadick, Nancy
Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
title Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
title_full Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
title_fullStr Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
title_full_unstemmed Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
title_short Data‐Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women’s Rheumatoid Arthritis Sequential Study Registry
title_sort data‐driven patient clustering and differential clinical outcomes in the brigham and women’s rheumatoid arthritis sequential study registry
topic Rheumatoid Arthritis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048846/
https://www.ncbi.nlm.nih.gov/pubmed/33002337
http://dx.doi.org/10.1002/acr.24471
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