Designing Predictive Models for Beta-Lactam Allergy Using the Drug Allergy and Hypersensitivity Database

Abstract

Background: Beta-lactam antibiotics represent the main cause of allergic reactions to drugs, inducing both immediate and nonimmediate allergies. The diagnosis is well established, usually based on skin tests and drug provocation tests, but cumbersome.

Objectives: To design predictive models for the diagnosis of beta-lactam allergy, based on the clinical history of patients with suspicions of allergic reactions to beta-lactams.

Methods: The study included a retrospective phase, in which records of patients explored for a suspicion of beta-lactam allergy (in the Allergy Unit of the University Hospital of Montpellier between September 1996 and September 2012) were used to construct predictive models based on a logistic regression and decision tree method; a prospective phase, in which we performed an external validation of the chosen models in patients with suspicion of beta-lactam allergy recruited from 3 allergy centers (Montpellier, Nîmes, Narbonne) between March and November 2013. Data related to clinical history and allergy evaluation results were retrieved and analyzed.

Results: The retrospective and prospective phases included 1991 and 200 patients, respectively, with a different prevalence of confirmed beta-lactam allergy (23.6% vs 31%, P = .02). For the logistic regression method, performances of the models were similar in both samples: sensitivity was 51% (vs 60%), specificity 75% (vs 80%), positive predictive value 40% (vs 57%), and negative predictive value 83% (vs 82%). The decision tree method reached a sensitivity of 29.5% (vs 43.5%), specificity of 96.4% (vs 94.9%), positive predictive value of 71.6% (vs 79.4%), and negative predictive value of 81.6% (vs 81.3%).

Conclusions: Two different independent methods using clinical history predictors were unable to accurately predict beta-lactam allergy and replace a conventional allergy evaluation for suspected beta-lactam allergy.

Keywords: Allergy testing; Beta-lactams; Drug hypersensitivity; Predictive models.