Ocular Adverse Events in Patients with Atopic Dermatitis Treated with Upadacitinib: A Real-Life Experience
Bibliographic record
Abstract
Background: Dupilumab, an interleukin (IL)-4 receptor-α inhibitor that blocks IL-4 and IL-13 signaling pathways, is an effective and well-tolerated therapy for moderate-to-severe atopic dermatitis (AD). However, an increased incidence of dupilumab-associated conjunctivitis has been reported in patients treated with dupilumab. In contrast, upadacitinib, a selective Janus kinase 1 inhibitor, is reported to have lower incidence of conjunctivitis than dupilumab. Objective: The aim of this retrospective study was to investigate ocular adverse events in adult patients with moderate-to-severe AD treated with upadacitinib after discontinuing treatment with dupilumab. Methods: In total, 33 patients were examined at the start of treatment with upadacitinib after discontinuation of dupilumab, then again after 4 weeks and every 12 weeks up to a maximum of 72 weeks. Results: Among the patients in the study, 14 had developed dupilumab-associated conjunctivitis during dupilumab treatment and had complete resolution of ocular symptoms after the switch to upadacitinib within the 1-month follow-up visit. In addition, only 1 patient treated with upadacitinib developed an episode of conjunctivitis. This condition was of mild severity and it spontaneously resolved quickly. Interestingly, this patient had no history of dupilumab-associated conjunctivitis. Conclusions: All patients who developed dupilumab-associated conjunctivitis experienced complete remission on upadacitinib and only 3% of the patients in our sample developed conjunctivitis after the start of treatment with upadacitinib. In light of this, upadacitinib appears to be a prudent and safe treatment option for AD patients with uncontrolled ocular symptoms associated with dupilumab therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".