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 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.

Figure 1

Heatmap of principal component (PC) scores across the clusters, showing variation relative to overall Brigham and Women’s Rheumatoid Arthritis Sequential Study average. PCs are ranked by variance explained. Positive scores appear in red, negative scores in blue. csDMARD = conventional synthetic disease‐modifying antirheumatic drug; curr = current; GI = gastrointestinal; NSAID = nonsteroidal antiinflammatory drug; RA = rheumatoid arthritis; TNFi = tumor necrosis factor inhibitor.

Figure 2

Clinical outcomes over 2 years. Mean Clinical Disease Activity Index (CDAI) score (A), Multidimensional Health Assessment Questionnaire (MDHAQ) score (B), 5‐item Mental Health Index (MHI‐5) score (C), and tender joint count (D). Observed cases with no imputation for missing data. 95% CI = 95% confidence interval; BMI = body mass index; RA = rheumatoid arthritis. Error bars indicate the 95% CI.

Figure 3

Time to first infection (A), change of conventional synthetic or biologic disease‐modifying antirheumatic drug (DMARD) (B), and change of biologic DMARD (C). BMI = body mass index; RA = rheumatoid arthritis.