Predictive Factors for Primary and Secondary Nonresponders to Upadacitinib in Patients with Moderate-to-Severe Atopic Dermatitis: A Real-World Study
Bibliographic record
Abstract
Abstract: Background: Some patients with atopic dermatitis (AD) do not sufficiently respond to upadacitinib, a Janus kinase 1 inhibitor. However, predictive factors for nonresponders remain unclear in real-world practice. Objective: To identify predictive factors for primary and secondary nonresponders to upadacitinib 15 mg; primary nonresponders are defined as patients with investigator’s global assessment (IGA) >2 at week 12, while secondary nonresponders are defined as patients with IGA ≤2 at week 12 and IGA >2 at week 24. Methods: A prospective study was conducted from August 2021 to March 2024, involving 204 Japanese AD patients treated with upadacitinib 15 mg. Baseline clinical and laboratory indexes were compared between nonresponders and responders. Results: Primary nonresponders showed higher baseline eczema area and severity index (EASI), immunoglobulin E (IgE), thymus and activation-regulated chemokine (TARC), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) compared with responders. Secondary nonresponders had a higher proportion of previous systemic therapies, dupilumab, and corticosteroids. Conclusions: Higher baseline EASI, IgE, TARC, LDH, NLR, CRP, SII, and SIRI may predict primary nonresponders to upadacitinib 15 mg, while previous systemic dupilumab or corticosteroids may predict secondary nonresponders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".