Evaluating a Penicillin Allergy Clinical Decision-Making Tool to Enhance Penicillin Allergy De-labeling

Su, Chang; Kuster, John K.; Belmont, Ami; Liao, Jane; Trubiano, Jason A; Kwah, Jason

doi:10.1016/j.jaci.2022.12.192

Cited by 3 publications

(1 citation statement)

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“…Another attempt to design a decision tool is represented by the user-friendly PEN-FAST questionnaire, a penicillin allergy clinical decision rule that used five clinical history questions to predict patient's risk to have a true BL-allergy [37]. This questionnaire has allowed a correct delabelling of low-risk patients in other populations [38 ▪▪ ,39 ▪▪ ,40,41]; however, it did not help to identify low-risk penicillin-allergy in a Canadian paediatric prospective multicentred cohort [42 ▪▪ ]. Future efforts to apply this strategy should increase the number of questions or items included to improve its performance as a classification algorithm.…”

Section: Predictive Models In Drug Allergymentioning

confidence: 99%

Predictive models and applicability of artificial intelligence-based approaches in drug allergy

Núñez,

Doña,

Cornejo-García

2024

Current Opinion in Allergy &Amp; Clinical Immunology

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Purpose of review Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, the limitations of in vitro tests, and the associated risk to in vivo tests. Predictive models, including those using recent advances in artificial intelligence, may circumvent these drawbacks, leading to an appropriate classification of patients and improving their management in clinical settings. Recent findings Scores and predictive models to assess drug allergy development, including patient risk stratification, are scarce and usually apply logistic regression analysis. Over recent years, different methods encompassed under the general umbrella of artificial intelligence, including machine and deep learning, and artificial neural networks, are emerging as powerful tools to provide reliable and optimal models for clinical diagnosis, prediction, and precision medicine in different types of drug allergy. Summary This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.

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Section: Predictive Models In Drug Allergymentioning

confidence: 99%