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Enregistrement W4416100721 · doi:10.1093/cid/ciaf402

Executive Summary: State-of-the-Art Review: Antibiotic Allergy—A Multidisciplinary Approach to Delabeling

2025· article· en· W4416100721 sur OpenAlexaff
Elise Mitri, Gemma Reynolds, Ana Maria Copaescu, Fionnuala Cox, Jamie Waldron, Jonny Peter, Jason A. Trubiano

Notice bibliographique

RevueClinical Infectious Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueAllergic Rhinitis and Sensitization
Établissements canadiensMcGill University Health Centre
Organismes subventionnairesnon disponible
Mots-clésMultidisciplinary approachAntibioticsExecutive summaryMEDLINEAntibacterial agent

Résumé

récupéré en direct d'OpenAlex

Antibiotic allergy labels (AALs), especially to beta-lactams, remain highly prevalent across healthcare systems. In high-income countries, up to 11.5% of adults report a penicillin allergy, and in high-risk groups such as hematology, oncology, and transplant patients, prevalence can reach 35%. However, more than 90% of these labels are inaccurate, resulting in substantial clinical and public health consequences in hospitals and communities. Patients with AALs face increased risk of Clostridioides difficile and methicillin-resistant Staphylococcus aureus infections, surgical site infections, intensive care unit admission, prolonged hospital stays, and higher mortality. In addition, they are frequently prescribed broader-spectrum antibiotics from World Health Organization Watch or Reserve categories, thereby increasing antimicrobial resistance (AMR). The burden data primarily exist for patient-reported antibiotic allergy; therefore, this will be the focus of this review, including both more commonly reported severe immunoglobulin E and T-cell–mediated reactions. For infectious diseases (ID) physicians, antimicrobial stewardship (AMS) pharmacists, and internal medicine and community medicine providers, AALs complicate first-line prescribing and delay time to effective antimicrobial therapy. This review positions antibiotic allergy assessment as a critical component of ID and AMS practice. It introduces a multidisciplinary, evidence-based framework to guide risk stratification, safe delabeling, and informed prescribing, empowering physicians to address inaccurate labels, reduce AMR, and improve patient outcomes. Validated clinical decision rules now provide practical tools to guide risk stratification at the point of care. Tools such as PEN-FAST, CEPH-FAST, and SULF-FAST allow trained non-allergist clinicians, including ID physicians and pharmacists, to identify patients at low risk for true allergy and safely proceed with direct oral challenge (DOC) without prior skin testing. Randomized trials and cohort studies support DOC as a first-line delabeling strategy, with adverse event rates below 5% across thousands of challenges. For patients with high-risk phenotypes, particularly severe cutaneous adverse reactions such as drug reaction with eosinophilia and systemic symptoms (DRESS) and Steven-Johnson Syndome (SJS)/toxic epidermal necrolysis (TEN), emerging immunologic diagnostics that are primarily in the research phase show clinical promise. These include interferon-gamma ELISpot assays and pharmacogenomic testing (eg, HLA class I typing) to distinguish phenotype-specific risk profiles and guide future safe antibiotic use. Prescribing safety has also been enhanced by refined understanding of beta-lactam cross-reactivity, now recognized as primarily driven by R1 side-chain similarity. This insight allows safe prescribing of non–cross-reactive penicillins or cephalosporins, even in patients with prior allergy labels and severe reactions, restoring access to essential first-line agents. Innovative care models now integrate allergy assessment into routine inpatient and outpatient settings. These multidisciplinary pathways, led by ID physicians, pharmacists, anesthetists, and trained clinicians, enable systematic delabeling at scale. Two core strategies are defined: opportunistic delabeling (evaluation during routine hospital encounters) and targeted delabeling (focused assessment in high-risk populations such as immunocompromised patients). Programs that incorporate continuous or checkpoint surveillance have demonstrated efficacy in improving prescribing outcomes and sustaining delabeling efforts. A whole-of-hospital approach, one that spans risk stratification, testing, prescribing, and documentation, is illustrated in Figure 4 of the main text. Such models facilitate seamless integration of delabeling into AMS, with durable changes in prescribing behavior and patient records. Evidence from national and international studies confirms that non–allergist-led programs are safe, feasible, and sustainable. Incorporating these practices into AMS practice, both in the community and hospital setting, represents a transformative opportunity for physicians to lead in optimizing antimicrobial use, reducing resistance, and improving patient safety. Financial support. E. A. M. is supported by a PhD Scholarship from the Australian Government funded National Allergy Centre of Excellence (NACE), hosted by the Murdoch Children’s Research Institute (MCRI), and their work was supported by the Victorian Government’s Operational Infrastructure Support Program. G. K. R. receives an NHMRC PhD scholarship (2013970). J. A. T. is supported by a National Health and Medical Research Council Emerging Leadership Fellowship (1139902).

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)0,005
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,032
Score d'incertitude au seuil0,108

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,023
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0050,003
Bibliométrie0,0070,007
Études des sciences et des technologies0,0010,001
Communication savante0,0050,004
Science ouverte0,0030,002
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0320,009

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 Opus0,024
Tête enseignante GPT0,342
Écart entre enseignants0,318 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

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

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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