Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry.

ABSTRACT:

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

SUBMITTER: Curtis JR 

PROVIDER: S-EPMC8048846 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature