Persistence rates of abatacept and TNF inhibitors used as first or second biologic DMARDs in the treatment of rheumatoid arthritis: 9 years of experience from the Rhumadata® clinical database and registry
Bibliographic record
Abstract
BACKGROUND: Treatment persistence is an important consideration when selecting a therapy for chronic conditions such as rheumatoid arthritis (RA). We assessed the long-term persistence of abatacept or a tumor necrosis factor inhibitor (TNFi) following (1) inadequate response to a conventional synthetic disease-modifying antirheumatic drug (first-line biologic agent) and (2) inadequate response to a first biologic DMARD (second-line biologic agent). METHODS: Data were extracted from the Rhumadata® registry for patients with RA prescribed either abatacept or a TNFi (adalimumab, certolizumab, etanercept, golimumab, or infliximab) who met the study selection criteria. The primary outcome was persistence to abatacept and TNFi treatment, as first- or second-line biologics. Secondary outcomes included the proportion of patients discontinuing therapy, reasons for discontinuation, and predictors of discontinuation. Persistence was defined as the time from initiation to discontinuation of biologic therapy. Baseline characteristics were compared using descriptive statistics; cumulative persistence rates were estimated using Kaplan-Meier methods, compared using the log-rank test. Multivariate Cox proportional hazard models were used to compare the persistence between treatments, controlling for baseline covariates. RESULTS: Overall, 705 patients met the selection criteria for first-line biologic agent initiation (abatacept, n = 92; TNFi, n = 613) and 317 patients met the criteria for second-line biologic agent initiation (abatacept, n = 105; TNFi, n = 212). There were no clinically significant differences in baseline characteristics between the treatments with either first- or second-line biologics. Persistence was similar between the first-line biologic treatments (p = 0.7406) but significantly higher for abatacept compared with TNFi as a second-line biologic (p = 0.0001). Mean (SD) times on first-line biologic abatacept and TNFi use were 4.53 (0.41) and 5.35 (0.20) years, and 4.80 (0.45) and 2.82 (0.24) years, respectively, as second-line biologic agents. The proportion of patients discontinuing abatacept and TNFi in first-line was 51.1% vs. 59.5% (p = 0.1404), respectively. In second-line, it was 57.1% vs. 74.1% (p = 0.0031). The main reasons for stopping both treatments were inefficacy and adverse events. CONCLUSIONS: Abatacept and TNFi use demonstrated similar persistence rates at 9 years as a first-line biologic agent. As a second-line biologic agent, abatacept had better persistence rates over a TNFi.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".