327 Optimizing the management of atopic dermatitis with a new minimal disease activity concept and criteria and consensus-based recommendations for systemic therapy
Notice bibliographique
Résumé
Abstract Inconsistent criteria are used to identify patients with atopic dermatitis (AD) who are candidates for systemic therapy and assess their response to systemic therapy. This may lead to undertreatment and treatment dissatisfaction. A treat-to-target (T2T) framework was previously proposed to guide systemic treatment decisions in patients with moderate-to-severe AD.1 While patient representatives were included in the T2T consensus voting process, no patient or caregiver stakeholders were included in the development of the T2T recommendations. Additionally, a recent analysis of the cross-sectional, 28-country MEASURE-AD study suggests that the treatment targets in the T2T criteria may be insufficient to ensure optimal treatment outcomes.2 To develop optimized and practical criteria for identifying patients who should receive systemic therapy, including definitions of treatment goals, treatment failure and disease severity. An executive steering committee (ESC) of seven international experts was formed in January 2021 to provide insights and perspectives on how to optimize the identification of patients who would most benefit from systemic therapy for AD. After discussing the gaps and needs in current AD management, the ESC agreed that there was a lack of evidence on patients’ treatment goals, needs, and expectations. The ESC, therefore, initiated a global, ethically and culturally diverse patient research study (N = 88) to collect these insights. Subsequently, nine regional sub-committees (SCs) were created to gain clinical perspectives from different regions worldwide (covering the USA and Puerto Rico, Latin America, Western Europe and Canada, Eastern Europe and Russia, the Middle East, Asia, and Australia and New Zealand). Overall, 87 experts from 44 countries contributed to the initiative, and 46 virtual ESC and SC meetings took place to discuss how to improve the lives of patients with AD. A virtual secure platform allowed discussions and contributions to continue outside these meetings. In April 2022, all experts rated their agreement with a series of recommendations regarding the identification and monitoring of patients eligible for systemic therapy, using a 10-point Likert scale in a modified eDelphi voting process. The consensus was pre-defined as ≥70% of all respondents rating agreement as 7 (‘mildly agree’), 8 (‘moderately agree’), 9 (‘agree’) or 10 (‘strongly agree’) with a recommendation. A strong consensus was defined as ≥90% agreement. Expert perspectives and patient insights led to the development of 34 patient-focused clinical recommendations on disease severity assessments, treatment goals and targets for clinician- and patient-reported outcomes, long-term disease control and a novel minimal disease activity (MDA) concept. A consensus of ≥80% was reached for all recommendations in one round of voting, with 88% of the recommendations reaching a ‘strong’ consensus. The MDA concept combines T2T principles with shared patient/clinician treatment decision-making principles. Patients are asked to select 1–3 feature(s) of AD which are most important to them (from the itch, skin appearance/condition, sleep disturbance, mental health, skin pain and impact on daily life). The clinician is asked to choose an objective measure of disease (from the Eczema Area and Severity Index [EASI], SCORing AD and/or the Investigator’s Global Assessment and body surface area). Treatment targets are then chosen from a list of ‘moderate’ and ‘optimal’ targets based on discussions between the clinician and patient. Optimal treatment targets include ≥90% improvement in EASI and a numeric rating scale of ≤ 1 for peak pruritus, sleep, and pain. Achievement of ‘optimal’ targets is defined as MDA. This international group of AD experts developed a novel MDA concept and criteria, which builds upon existing T2T work by providing a patient-centric approach to the optimal treatment of AD. The criteria and patient-focused clinical recommendations will help to identify and monitor patients with AD who could benefit from systemic therapy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».