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Record W4404455285 · doi:10.25251/skin.8.supp.463

Impact of Therapeutic Inertia on Patient-Reported Outcomes in Moderate-to-Severe Atopic Dermatitis: A 12-Month Longitudinal Study from the TARGET-DERM AD Registry

2024· article· en· W4404455285 on OpenAlexaboutno aff
Brenda Simpson, Ayman Grada, Keith Knapp, Breda Muñoz, Julie Crawford, Jonathan I. Silverberg

Bibliographic record

VenueSKIN The Journal of Cutaneous Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAtopic dermatitisMedicineLongitudinal studyDermatologyPatient registryPediatricsPathology

Abstract

fetched live from OpenAlex

Background: Therapeutic inertia, the delay or reluctance to modify treatment when goals are unmet, is a significant challenge in managing chronic diseases, including atopic dermatitis (AD). This inertia can lead to suboptimal disease control and affect patient outcomes. Objectives: This study evaluates the effect of therapeutic inertia on patient-reported outcomes (PROs) in moderate-to-severe AD patients undergoing systemic treatment over 3 to 12 months. Methods: We analyzed longitudinal data from the TARGET-DERM AD registry, which includes 3,457 patients with moderate-to-severe AD from 39 centers across the U.S. and Canada. Eligible patients had documented patient-reported outcomes (PROs) at the initiation of systemic therapy and at subsequent 3-month intervals up to 12 months of follow-up. We assessed the proportion of patients not meeting treatment targets based on expert consensus. Patients had a validated Investigator Global Assessment (vIGA-AD) score of 3 or more at initiation of either an advanced systemic therapy (AST) such as biologics or JAK inhibitors or a conventional systemic therapy (CST) such as cyclosporine, methotrexate, or prednisone. PROs were evaluated at 3-month intervals up to 12 months, assessing achievement against the predefined treatment targets. PRO measures included Worst-Itch (PROMIS Itch-Severity), POEM, PO-SCORAD, NRS-sleep, and NRS-pain with specific moderate and optimal target levels established for each. Itch-Severity (range: 0–10; moderate target: ≥4-point reduction, optimal target: score ≤1), POEM (range: 0-28; moderate target: ≥4-point reduction, optimal target: score ≤2), PO-SCORAD (range: 0-103; moderate target: score ≤24; optimal target: score ≤10), NRS-sleep and NRS-pain (range: 0-10; moderate target: reduction ≥3-points; optimal target: score ≤1). Results: Out of 2107 patients with moderate-to-severe AD, 445 qualifying participants were included (63.8% adult, 62.0% female, 45.4% Non-Hispanic White, mean age of 31 years). Most patients (88.8%) initiated AST, with dupilumab being the most common (86.5%). At 6 months, significant proportions of AST-treated patients failed to reach moderate and optimal targets for itch (67% and 79%, respectively), POEM (46% and 69%), and NRS-sleep (59% and 47%). By 12 months, these figures were similar, with 66% and 88% failing to meet itch targets, 53% and 73% failing to meet POEM targets, and 62% and 45% failing to meet NRS-sleep targets, respectively. A similar pattern was observed for other PROs. CST-treated patients exhibited similar trends. Conclusions: The study highlights the profound impact of therapeutic inertia on the quality of life of patients with moderate-to-severe AD. Despite systemic therapy, a considerable proportion failed to meet treatment targets over a 12-month period, underscoring the need for more proactive and responsive treatment strategies in AD management.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.314
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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