Predictive Factors for the Long-Term Clinical Course in Patients with Rheumatoid Arthritis Receiving Second-Line Anti-Rheumatic Drugs in Real-World Practice: An Analysis Using Disease Activity Trajectory-Based Clustering Approach
Bibliographic record
Abstract
Abstract Background: The purpose of this study was to stratify patients with rheumatoid arthritis (RA) according to the trend of disease activity by trajectory-based clustering and to identify the predictive factors for treatment response and the switching patterns of biologics according to trajectory groups. Methods: We analysed the data from a nationwide RA cohort from the Korean College of Rheumatology Biologics and Targeted Therapy (KOBIO) registry. Patients treated with second-line disease-modifying anti-rheumatic drugs (DMARDs) were included. Trajectory modeling for clustering was used to group the disease activity trend. The predictive factors and switching patterns of biologics for each trajectory were investigated.Results: The trends in the disease activity of 688 RA patients were clustered into 4 groups: rapid decrease and stable disease activity (group 1, N = 319), rapid decrease followed by an increase (group 2, N = 36), slow and continued decrease (group 3, N = 290), and no decrease in disease activity (group 4, N = 43). In the multivariable analysis for predictive factors, current smoking (OR, 7.845; 95% CI 2.158–28.220), low hemoglobin (OR 0.694; 95% CI, 0.532–0.901), and high initial disease activity score according to the 28-joint assessment (DAS28) (OR, 2.397; 95% CI, 1.638–3.586) were significantly associated with group 4 compared with group 1. Group 1 had a higher proportion of patients who had never had switching (86.5%) and who were initially treated with non-TNF inhibitors (44.2%) compared with groups 2 (52.8% and 25%), 3 (50.3% and 23.4%), and 4 (25.6% and 18.6%).Conclusions: The trajectory-based approach was useful for clustering the disease activity in longitudinal data in patients with RA. Among the four trajectories, the group with sustained high disease activity was associated with current smoking, low hemoglobin, high initial DAS28, and frequent switching of biologics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".