Observational studyArthritis care & research2021
Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry.
Observational study in Arthritis care & research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 37 citations in OpenAlex.
- Early prediction of joint space narrowing in rheumatoid arthritis using AI-quantified bilateral joint space asymmetry on hand radiography.Japanese journal of radiology · 2026Article
- Neither ultrasound synovitis nor clinical-ultrasound phenotypes of established rheumatoid arthritis predict response to targeted therapy.Rheumatology (Oxford, England) · 2025Observational
- Location and amount of joint involvement differentiates rheumatoid arthritis into different clinical subsets.NPJ digital medicine · 2025Article
- Article
- Vitamins improve the effect of heavy metal exposure in arthritis after hysterectomy.Frontiers in nutrition · 2025Article
- A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis.Journal of translational medicine · 2024Article
- Comorbidity clusters in patients with rheumatoid arthritis identify a patient phenotype with a favourable prognosis.Annals of the rheumatic diseases · 2024Article
- Patterns and determinants of response to novel therapies in juvenile and adult-onset polyarthritis.Rheumatology (Oxford, England) · 2024Review
- Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach.Arthritis research & therapy · 2023Article
- Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.PLoS computational biology · 2023Article
- Researchers in rheumatology should avoid categorization of continuous predictor variables.BMC medical research methodology · 2023Article
- Identification of Multimorbidity Patterns in Rheumatoid Arthritis Through Machine Learning.Arthritis care & research · 2023Article
- Using Unsupervised Machine Learning Methods to Cluster Comorbidities in a Population-Based Cohort of Patients With Rheumatoid Arthritis.Arthritis care & research · 2023Article
- [A cross-sectional study on the clinical phenotypes of rheumatoid arthritis].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2022Article
- Disease activity trajectories for early and established rheumatoid arthritis: Real-world data from a rheumatoid arthritis cohort.PloS one · 2022Article
- Classification of long-term condition patterns in rheumatoid arthritis and associations with adverse health events: a UK Biobank cohort study.Journal of multimorbidity and comorbidityArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 10 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
methodsPatient 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.
resultsAnalysis 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.
conclusionData-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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.