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Enregistrement W4411431053 · doi:10.1016/j.ard.2025.06.574

POS1224 CONSENSUS-BASED GUIDANCE FOR GLUCOCORTICOID TAPERING IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: RESULTS FROM A MODIFIED DELPHI CONSENSUS PROJECT

2025· article· en· W4411431053 sur OpenAlexaff
Edward M Vital, George Βertsias, Andrea Doria, Sindhu R. Johnson, Sarah Mackie, Sandra Navarra, Bart Nijmeijer, A. Olojo, Bernardo A. Pons‐Estel, Y K Onno Teng, Jinoos Yazdany

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Lupus Erythematosus Research
Établissements canadiensToronto Western HospitalUniversity of TorontoMount Sinai Hospital
Organismes subventionnairesnon disponible
Mots-clésMedicineTaperingGlucocorticoidDelphiConsensus conferenceDelphi methodImmunologyInternal medicineArtificial intelligenceComputer science

Résumé

récupéré en direct d'OpenAlex

Background: Glucocorticoids (GCs) are important for the management of systemic lupus erythematosus (SLE); however, chronic use is associated with significant adverse effects and organ damage. Despite guidelines emphasising the importance of tapering GCs in order to minimise GC-attributable organ damage accrual, detailed recommendations on how to taper are limited. Objectives: To develop consensus-based guidance for tapering GCs in patients with SLE, including specific tapering regimens, through the use of a modified Delphi technique. Methods: Delphi Rounds Following an initial literature and guideline review, candidate statements were developed and grouped into three topics: 1)GC-tapering regimens in lupus: general considerations 2)Managing flares and patients with high risk of flare during GC tapering 3)Managing GC-associated toxicity and GC withdrawal symptoms To gain further insights into current practice, open-ended and multiple-choice questions were also included, and example GC-tapering regimens for different disease severities were proposed for respondents to comment on. Results were obtained over two rounds of online surveys, with Round 1 results supporting the development of Round 2. In Round 2, respondents were asked to vote for which regimens they deemed acceptable for use in clinical practice and could vote for multiple regimens per patient scenario. Respondents An interdisciplinary group of experts were invited to participate in two online Delphi rounds. Snowball sampling was used to identify global lupus experts including rheumatologists, nephrologists, dermatologists and internists. Defining consensus In Round 1, respondents were asked to rate their agreement with the candidate statements using a nine-point Likert scale, with a rating of 1–3 indicating disagreement, 4–6 indicating neutrality, and 7–9 indicating agreement. Consensus was defined as ≥70% of respondents voting either ≤3 (disagreement) or ≥7 (agreement). In Round 2, respondents were presented with the mean ratings from Round 1, their own Round 1 ratings and the opportunity to revise their rating. Results: Of the 124 respondents invited, 101 (81%) completed Round 1 and, of these, 94 (93%) completed Round 2. Respondents who completed both rounds were from 30 countries and comprised 79 rheumatologists, 11 nephrologists, 3 dermatologists and 1 internist. Of the eight example GC-tapering regimens presented, three achieved ≥70% agreement that they would be acceptable to use in clinical practice (regimens for mild and moderate SLE and lupus nephritis). The regimens that received the highest agreement in Round 2 for each patient scenario are presented in Table 1. For patients with mild SLE, 87.2% of respondents agreed that a 4-week regimen resulting in withdrawal of GCs, if disease was controlled, was acceptable. Additionally, respondents agreed with the regimen for moderate SLE, with 93.6% voting it acceptable; however, only 59.6% of respondents considered the use of intravenous methylprednisolone appropriate for these patients. For patients with active lupus nephritis, agreement was achieved (74.5% of respondents) in using intravenous methylprednisolone at a dose of 0.25–5 g/day for up to 3 days, followed by GC tapering to <2.5 mg/day by Week 25. 88.3% of respondents agreed that this regimen could also be used for patients with severe non-renal SLE. Lastly, for patients on long-term low-dose GC treatment, 94.7% of respondents considered either the 12-week regimen, the 6-month regimen or both regimens acceptable for use (Table 1); 62.8% of respondents considered the 12-week regimen acceptable and 67.0% considered the 6-month regimen acceptable. Overall, consensus for either agreement or disagreement was achieved in 31/33 statements in Round 2 (Table 2). Conclusion: This consensus-based guidance will support physicians in choosing a GC-tapering approach for patients with SLE across different clinical scenarios and help them to use GCs more efficiently. The next step for this guidance is to obtain the patient perspective on these results to ensure that patient preferences are considered when initiating GC tapering, followed by dissemination into routine practice. Funding: This study was sponsored by AstraZeneca. Table 1. Summary of recommended GC-tapering regimens IV, intravenous; KDIGO, Kidney Disease: Improving Global Outcomes Table 2. Consensus received on final GC-tapering voting statements dsDNA, double-stranded DNA; IV, intravenous REFERENCES: NIL . Acknowledgements: Medical writing support was provided by Helios Medical Communications and was funded by AstraZeneca in accordance with Good Publication Practice (GPP) guidelines (http://www.ismpp.org/gpp-2022). The authors retained full control of the content and made the final decisions for all aspects of this abstract. Additionally, the authors acknowledge the time and insights provided by the respondents. Disclosure of Interests: Edward M. Vital has received honoraria from Abbvie, AstraZeneca, Aurinia, BMS, Idorsia, ILTOO Pharma, Lilly, Merck, Novartis, Otsuka, Pfizer, Roche and UCB, and has received grants paid to his employer from AstraZeneca, Roche and Sandoz, George K. Bertsias has received speaker fees from AstraZeneca, GSK, Otsuka, Pfizer, Novartis and Abbvie, has received consulting fees from AstraZeneca, GSK and Novartis, and has received grants from AstraZeneca, GSK and MSD, Andrea Doria has received speaker fees from AstraZeneca, GSK and Otsuka, and has received consulting fees from AstraZeneca, Biogen, GSK, MSD and Otsuka, Sindhu R. Johnson: None declared, Sarah Mackie has received speaker fees for AbbVie, CSL Vifor, Fresenius Kabi, Novartis, Roche/Chugai Pharmaceutical and UCB, and has received consulting fees from AbbVie, AstraZeneca, Pfizer, Roche/Chugai Pharmaceutical and Sanofi, Sandra Navarra has received speaker fees from AstraZeneca and Boehringer Ingelheim, and has received consulting fees from Biogen, Bas Nijmeijer is an employee at AstraZeneca, Ayobami Olojo is a shareholder at AstraZeneca, and is an employee at AstraZeneca, Bernardo Pons-Estel has received speaker fees from AstraZeneca, GSK, Janssen and Novartis, and has received grants from AstraZeneca, GSK, Janssen and Roche, Y.K. Onno Teng has received grants from CSL Vifor and GSK, Jinoos Yazdany has received consulting fees from AstraZeneca, Pfizer and UCB, and has received grants from Aurinia, BMS Foundation and Gilead. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,190
score de la tête « metaresearch » (Gemma)0,203
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,190
Score d'incertitude au seuil0,999

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1900,203
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,002
Communication savante0,0030,002
Science ouverte0,0020,010
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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.

Tête enseignante Opus0,042
Tête enseignante GPT0,324
Écart entre enseignants0,282 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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