Clinical Disease Activity Index Trajectories in Patients with Rheumatoid Arthritis Treated with Abatacept: A Real-World Study
Bibliographic record
Abstract
Objectives To describe the 12-month Clinical Disease Activity Index (CDAI) trajectory patterns and their determinants in patients treated for rheumatoid arthritis (RA) with subcutaneous abatacept. The association between 3- and 12-month CDAI trajectory classifications was also assessed. Methods Post-hoc analysis of RA patients initiated on abatacept in the Abatacept Best Care study ( NCT03274141 ).[1] A growth mixture model with Bayesian imputations was used to determine CDAI trajectory groups over 12 months. Time to CDAI low disease activity (LDA) and remission was assessed with Kaplan Meier Survivor estimates. Differences in baseline characteristics between trajectory groups were assessed using bivariate statistical methods. Determinants of trajectory patterns were identified with simple and multivariate logistic regression models with stepwise variable selection. The association between 3- and 12-month CDAI trajectory groupings was assessed with Cohen’s kappa. Results Among 256 patients, mean (SD) age was 60.1 (11.5) years, 75% were female, and baseline CDAI was 30.1 (10.6). Three CDAI trajectory groups were identified: Rapid Responders (RR; n=125; 49%); Late Responders (LR; n=96; 37%); and Non-responders (NR; n=35; 14%) (Figure 1). Mean (95% CI) time to CDAI LDA was 5.6 (5.1-6.1), 9.0 (8.2-9.7), and 11.3 (10.4-12.1) months for RR, LR, and NR, respectively, and time to remission was 9.6 (9.0-10.2), 12.0 (11.8-12.2) months, and not reached for NR (P<0.001). Mean (SD) baseline parameters for RR, LR and NR respectively were: CDAI: 27.0 (10.2), 30.5 (8.9), 40.0 (10.4); DAS28-CRP: 4.1 (1.0), 4.5 (0.8), 5.2 (0.7); Routine Assessment of Patient Index Data 3: 14.1 (5.4), 16.9 (4.5), 17.8 (5.4); tender joint count: 7.9 (5.6), 9.8 (5.5), 15.2 (5.9), Patient Global Assessment (PtGA) 57.4 (22.9), 67.2 (18.5), 73.9 (18.6); and fatigue (VAS 0-100): 58.5 (23.5), 63.3 (22.5), 76.5 (21.6; P<0.01). In multivariate analysis, baseline predictors of RR or LR vs NR were lower PtGA (odds ratio [95% CI]:0.98 [0.95-1.00]), swollen joint count (0.92 [0.84-1.00]), Rheumatic Disease Comorbidity Index (0.75 [0.59-0.95]), and no prior biologic use (0.36 [0.16-0.79]). 3-month CDAI trajectory classifications were not associated with 12-month classifications (kappa=0.46). Figure 1: 12-month response trajectory groups for rheumatoid arthritis patients treated with abatacept identified by GMM based on CDAI response overtime. Lines represent mean CDAI score through 12 months; shaded areas represent standard deviation. CDAI: Clinical Disease Activity Index; GMM: growth mixture model; LDA: low disease activity. Conclusion In this real-world-study, ~85% of abatacept-treated RA patients showed rapid/late CDAI reduction over 12 months. Lower baseline disease severity, no prior biologic treatment, and fewer comorbidities were predictive of 12-month change in CDAI, whereas disease severity changes during the first 3 months of treatment were not, suggesting that patients may benefit from a longer treatment course prior to switching treatment. Trajectory-based analyses are informative and may have implications for clinical practice and research. [1.] Bessette L. Arthritis Res Ther 2023;25(1):183.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".