Real‐world reported adverse events related to systemic immunomodulating therapy in patients with atopic dermatitis: Results from the <scp>TREAT NL</scp> (<scp>TREatment</scp> of <scp>ATopic</scp> eczema, the Netherlands) registry
Bibliographic record
Abstract
BACKGROUND: Evidence on the (long-term) safety of systemic immunomodulating therapies in atopic dermatitis (AD) generated by real-world data is sparse. OBJECTIVES: To describe real-world reported adverse drug reactions (AEs) related to systemic immunomodulating therapy in patients with AD and to compare the incidence rates of AEs with the Summaries of Product Characteristics (SmPCs). METHODS: We conducted an observational prospective multi-centre cohort study, using the TREAT NL registry. All severe AEs, AEs of special interest and serious AEs in adult and paediatric patients on systemic immunomodulating treatment (ciclosporin, methotrexate, azathioprine, mycophenolic acid, dupilumab, tralokinumab, baricitinib and upadacitinib) were assessed. Incidences rates of all (potentially) drug-related AEs were standardized in patient years and compared to the cumulative incidences in the associated SmPCs. RESULTS: We collected 422 patient years of safety data from 266 patients, of whom 129 (48.5%) reported a total of 224 (potentially) drug-related AEs. Compared to dupilumab's SmPC, higher incidence rates were found for four AEs (reported ≥5 times): eosinophilia, blepharitis, dry eyes and head and neck erythema (i.e. dupilumab facial redness). A higher incidence rate of fatigue was found in patients on oral methotrexate in our cohort compared to the SmPC. Two new drug-related AEs (reported ≥5 times) were found in patients on dupilumab, including non-infectious conjunctivitis and meibomian gland dysfunction. CONCLUSIONS: Real-world reported AEs captured in AD patient registries can add information on the estimated incidence of AEs and benefit clinical decision aids. Future studies using data derived from the TREAT NL registry combined with data from other registries within the TREAT Registry Taskforce will provide more information on (rare) AEs associated with immunomodulating therapy in AD patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".