Associations Between Safety of Certolizumab Pegol, Disease Activity, and Patient Characteristics, Including Corticosteroid Use and Body Mass Index
Bibliographic record
Abstract
Objective To investigate the impact of baseline and time‐varying factors on the risk of serious adverse events (SAEs) in patients during long‐term certolizumab pegol (CZP) treatment. Methods Safety data were pooled across 34 CZP clinical trials in rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), psoriatic arthritis (PsA), and plaque psoriasis (PSO). Cox proportional hazards modeling was used to investigate the association of baseline patient characteristics with risk of serious infectious events (SIEs), malignancies, and major adverse cardiac events (MACEs). Cox modeling for recurrent events assessed the impact of time‐varying body mass index (BMI), systemic corticosteroid (CS) use, and disease activity on SIE risk in RA and SAE risk in PSO. Results Data were pooled from 8747 CZP‐treated patients across indications. Cox models reported a 44% increase in SIE risk associated with a baseline BMI of 35 kg/m 2 or more versus a baseline BMI of 18.5 kg/m 2 to less than 25 kg/m 2 . Baseline systemic CS use, age of 65 years or more, and disease duration of 10 years or longer also increased SIE risk. Older age was the only identified risk factor for malignancies. The risk of MACEs increased 107% for BMI of 35 kg/m 2 or more versus BMI of 18.5 kg/m 2 to less than 25 kg/m 2 and increased 51% for men versus women. Higher disease activity, older age, systemic CS use, BMI of 35 kg/m 2 or more, and baseline comorbidities were SIE risk factors in RA. Age and systemic CS use were risk factors for SAEs in PSO. Conclusion Age, BMI, systemic CS use, and disease activity were identified as SIE risk factors in CZP‐treated patients. Risk of malignancies was greater in older patients, whereas obesity and male sex were MACE risk factors.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".