421 The role of itch resolution and skin clearance in patient-reported atopic dermatitis severity and quality of life: real-world insights from TARGET-DERM AD
Bibliographic record
Abstract
Abstract Severity of atopic dermatitis (AD) itch and lesions is associated with poor quality of life. However, there is limited evidence describing the combined impact of itch and skin severity on patient outcomes in AD. This study aims to assess the independent and combined effects of itch and skin severity on patient-reported symptoms and quality-of-life outcomes. The study included adult participants (age ≥18 years) with AD enrolled in TARGET-DERM AD, an observational, longitudinal study of more than 3158 participants across 43 academic/community centers in the USA and Canada. Itch severity was assessed by the Patient-Reported Outcome Measurement Information System Itch-Severity, specifically the item evaluating ‘itch at its worst’, a 0–10 numeric rating scale. A score of 0 or 1 was interpreted as no/minimal itch. Skin severity was assessed by the validated Investigators Global Assessment of AD (vIGA-AD), with a score of 0 or 1 representing clear/almost clear skin (vIGA-AD 0/1). The association of AD symptoms representing patient-reported clear/almost clear disease [Patient-Oriented Eczema Measure (POEM) 0–2] and no impact of AD on quality of life [Dermatology Life Quality Index (DLQI) 0/1] with itch and skin severity was assessed using descriptive statistics and logistic regression models that included main and interaction effects for itch and skin severity. Among adult participants (n = 1795; 59% female; 60% non-Hispanic White; mean age 44.5 years), vIGA-AD, POEM, Worst Itch and DLQI data at enrollment were available for 95% (1702), 44% (792), 43% (783) and 43% (783) of participants, respectively. The proportion reporting POEM 0–2 and DLQI 0/1 was highest among those with no/minimal worst itch (73% POEM 0–2; 72% DLQI 0/1) and clear/almost clear skin (46% POEM 0–2; 45% DLQI 0/1), with decreasing proportions observed at greater itch and skin severity levels. Among those with both no/minimal itch and clear/almost clear skin, 87.5% (42/48) and 95.8% (46/48) reported POEM 0–2 and DLQI 0/1, respectively. Logistic regression results suggest that no/minimal itch and clear/almost clear skin are significantly associated with POEM 0–2 and DLQI 0/1, though the interaction effect was not statistically significant in despite all models assessed. Relative to those who did not have either no/minimal itch or clear/almost clear skin, the odds ratio of reporting POEM 0–2 and DLQI 0/1 was highest among those with both no/minimal itch and clear/almost clear skin (82.9 for POEM 0–2; 35.4 for DLQI 0/1), followed by no/minimal itch only (15.9 for POEM 0–2; 10.5 for DLQI 0/1) and clear/almost clear skin only (5.9 for POEM 0–2; 3.8 for DLQI 0/1). Complete or almost complete relief of itch and skin lesions is associated with greater odds of achieving ideal states in PROs (POEM and DLQI) with itch relief exhibiting a relatively higher impact than skin clearance. These results underscore the importance of assessing and documenting both itch severity and skin clearance to support shared decision-making. Moreover, clearance of both itch and skin lesions should be considered when setting treatment goals in patients with AD. Future research should include analyzing discrepant data (e.g. patients with no/minimal itch, but some skin lesions) to more precisely identify which aspect drives the patient-reported outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".