Frequency and Impact of Off-Label Biologic Prescriptions in Pediatric Rheumatology
Bibliographic record
Abstract
Objectives Over the past 25 years, biologic disease-modifying antirheumatic drugs (bDMARDs) have dramatically altered pediatric rheumatology practice and treatment outcomes. However, many bDMARDs have no on-label indications for pediatric rheumatological conditions in North America. In clinical practice, this gap creates a significant barrier for care. The objectives of this study were to 1) assess the frequency of off-label bDMARD prescriptions across pediatric rheumatological conditions in patients followed at the Stollery Children’s Hospital and the Glenrose Rehabilitation Hospital in Edmonton, Alberta, and 2) assess the impact label indication has on funding approval. Methods A retrospective chart review was conducted on patients with bDMARDs prescriptions at our 2 outpatient pediatric rheumatology clinics. Charts were identified through a list created by nursing staff of all patients on bMARDs. Patients were included if the bDMARD was started at our clinic for prescriptions between January 2015 to June 2023; there was a pediatric rheumatologic indication, and the patient was ≤18 years old at time of prescription. Duplicates, unfilled prescriptions, and non-bDMARD prescriptions were excluded. We tabulated the Health Canada and the FDA online databases on-label indications of prescribed bDMARDs (as of March 2024). Prescriptions and indication status were reviewed individually by the first and senior author and then reviewed together. Descriptive analysis was done with categorical data summarized as frequencies and percentages and numerical data as means and standard deviations (SD). Results 215 charts and 378 prescriptions were included (Table 1). The median age at diagnosis was 8.75 with SD 4.59. Juvenile Idiopathic Arthritis (JIA) was the most common diagnosis (n=158, 73.5%). The most prescribed bDMARDs were adalimumab (n=188, 44.3%), etanercept (n=75, 19.8%) and Tocilizumab (n=35, 9%). Across all included bDMARD prescriptions, 50.5% (n=191) were for use in an off-label indication. 151 patients (70.2%) had no insurance denials for coverage while 64 (29.8%) had ≥ 1 denials. Table 1. Patient diagnoses, rheumatologic medications prescribed, bDMARDs prescribed, and insurance coverage denials for outpatient pediatric rheumatology patients prescribed a bDMARD. Conclusion At our pediatric rheumatology center in Alberta, 50.5% of bDMARD prescriptions were for an off-label indication. 29.8% of our patients had at least 1 denial of medication coverage. This indicates that this widening gap between clinical and labeling indications in daily clinical practice may contribute to challenges in patient care. Further analysis is underway to examine the impact of off-label prescriptions on the approval of different funding streams.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".