Executive Summary: State-of-the-Art Review: Antibiotic Allergy—A Multidisciplinary Approach to Delabeling
Bibliographic record
Abstract
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).
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".