Development of Betalactam-Predictor: A Clinical Decision Tool for Delabeling Low-Risk Betalactam Allergy Patients....

A label of betalactam (BL) allergy is estimated in around 10% of the population in their medical records. Second-line choices carry significant negative consequences, including reduced efficacy, effectiveness, and safety. This study aimed to develop a new highly specific score constructed by selecting variables assisted by artificial intelligence to identify low-risk BL-allergic patients.

In this study, derivation and validation of the BL-predictor score were performed on a retrospective cohort of 2207 patients who underwent penicillin allergy testing at Málaga University Hospital (Spain). The development of the BL-predictor encompassed expert drafting and a two-step variable selection process consisting of univariate analysis and variable filtering, followed by stepwise logistic regression with resampling. To assess the efficiency, a multicentric retrospective external validation was performed in 4261 patients from six populations: Salamanca and Madrid, Spain; Nashville, United States of America; Verona, Italy; Paris, France; and Copenhagen, Denmark.

The definitive questionnaire consisted of eight items and risk points were computed from the logistic regression model as follows: +1 for reactions after first dose or in less than 1 h (ITEM-1), +2 for anaphylaxis (ITEM-2); +1 for previous reaction with the culprit (ITEM-3); -1 for resolution in > 24 h (ITEM-4); +2 for spontaneous resolution (ITEM-5); -2 for unknown symptoms (ITEM-6); -2 for reaction occurred > 5 years (ITEM-7), and -1 for another reported drug allergy (ITEM-8). After establishing a threshold of ≤ 0 points to classify individuals with low risk, internal validation showed a specificity of 86% and a negative predictive value (NPV) of 83%. Overall multicenter external validation showed a specificity of 93%, which implies a 25% increase in specificity compared to the previously published BL decision tool.

This score would simplify diagnostic procedures in low-risk patients, enabling rapid delabeling, potentially in non-specialty settings, and reducing diagnostic costs and the negative consequences associated with incorrect antibiotic allergy labels.