A combined risk modeling strategy for clinical prediction of beta-lactam allergies in children

Background: Drug provocation test (DPT) without skin tests is increasingly recommended in the evaluation of children with low-risk beta-lactam (BL) allergies. However, risk definitions are unclear. Objective: The aim of this study was to compose a clinical predictive model that could identify the children at low risk who could safely undergo direct DPT. Methods: The clinical data of 204 children who underwent a full diagnostic algorithm for suspected BL allergy were analyzed. Clinical data were used to construct mathematical predictive model for confirmed BL allergies. A prospective new sample was used for external validation of the final model. Results: The presentations during the index reaction were anaphylaxis in 5.9% and cutaneous reactions in the majority. BL allergy was confirmed in 15.7% of suspected cases. A backward multiple logistic regression model showed that a family history of drug allergy (adjusted odds ratio [aOR], 5.52), anaphylaxis (aOR, 5.14), any atopic disease other than asthma (aOR, 4.38), and a reaction interval of 0-6 hours during the index reaction (aOR, 5.32) were significantly associated with a confirmed BL allergy. A mathematical combined model based on these factors showed a sensitivity of 77.8% and a negative predictive value (NPV) of 94.3%. The validation study replicated sensitivity and NPV values of the main cohort. Conclusion: The risk definition in BL allergies should depend on population-specific predictive models, including a combination of significant risk factors rather than empiric risk approaches. This may help to accurately determinate children at low risk who may safely proceed to direct DPT.