Why Do Optimal Targets for Itch and Skin Clearance Matter in Atopic Dermatitis Treatment? Insights from TARGET-DERM AD Registry
Bibliographic record
Abstract
Introduction/Background: Atopic dermatitis (AD) patients undergoing treatment may only experience partial improvement in itch and skin lesions, often leading to suboptimal outcomes. The Aiming High in Eczema/Atopic Dermatitis (AHEAD) treat-to-target recommendations emphasize the importance of achieving optimal treatment targets, such as complete or near-complete itch relief and skin clearance. However, there is limited evidence on the impact of achieving these higher efficacy targets on patient-reported outcomes and quality of life in AD. Objectives: To evaluate the independent and combined effects of achieving optimal treatment targets for itch and skin clearance on patient-reported outcomes (PROs) in AD, based on the AHEAD treat-to-target recommendations. Method: A cross-sectional analysis was conducted on adult participants in TARGET-DERM AD, a longitudinal study with over 4,000 participants across 52 U.S. and Canadian clinical-practice sites (2019-2024). Itch severity was measured using the PROMIS Itch-Severity question (NRS-Itch, 0–10 scale), with scores of 0/1 indicating no or minimal itch. Skin severity was assessed using the validated Investigator Global Assessment (vIGA-AD), where 0/1 represents clear or almost clear skin. Associations between itch and skin severity with optimal patient outcomes including POEM 0–2 (clear/almost-clear disease), DLQI 0/1 (minimal/no impact on quality of life), NRS-Sleep 0/1, and NRS-Pain 0/1 were evaluated. Logistic regression models examined the main and interaction effects of itch and skin severity. Results: Among 1,920 patients (58.6% female; 54.5% Non-Hispanic White; 93.8% US; mean age 45 years), optimal DLQI, POEM, NRS-Sleep, and NRS-Pain occurred most frequently among those achieving the optimal treatment targets for itch (WI-NRS 0/1; 52.1%, 53.7%, 57.3%, and 83.1%, respectively) and skin clearance (vIGA-AD 0/1; 44.7%, 44.3%, 44.7%, and 74.3%, respectively). Compared to partial improvement, the adjusted odds ratios (aOR) of optimal PROs were greatest for participants with complete or near-complete resolution of both itch and skin lesions (DLQI 0/1: 20.0; POEM 0-2: 41.7; Sleep-NRS: 16.1; Pain-NRS: 6.0). Conclusions: Achieving optimal treatment targets for both itch and skin lesions markedly enhances patient-reported outcomes in AD. The results of this real-world study support treat-to-optimal targets to assess therapeutic effectiveness and optimize patient outcomes.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".