Usefulness of an Artificial Neural Network in the Prediction of β-Lactam Allergy

Abstract

Background: An accurate diagnosis of β-lactam (BL) allergy improves the use of antibiotics, increases patients' safety, and reduces costs to health systems. Nevertheless, it requires skin and drug provocation tests, which are time-consuming and put the patient at risk. Furthermore, allergy testing is not available in circumstances such as the urgent need for antibiotic therapy.

Objective: To evaluate the usefulness of an artificial neural network (ANN) in the prediction of hypersensitivity to BLs, and compare it with logistic regression (LR) analysis.

Methods: In a single-center study, 656 patients evaluated for BL allergy between 1994 and 2000 were retrospectively analyzed, and the data were used to construct an ANN. The ANN predictive capabilities were compared with LR and then prospectively evaluated in 615 patients who underwent BL evaluation between 2011 and 2017.

Results: A total of 1271 patients were evaluated. All patients had a definite diagnosis as allergic or nonallergic to BL. The prospective sample showed a lower percentage of patients with allergy than the retrospective sample (20.7% vs 25.8%; P = .018). In the retrospective and prospective series, the ANN reached a sensitivity of 89.5% and 81.1%, a specificity of 86.1% and 97.9%, a positive predictive value of 82.1% and 91.1%, and a negative predictive value of 92.1% and 95.2%, respectively. The ANN's performance was far superior to that of the LR, whose best performance reached a sensitivity of 31.9% and a specificity of 98.8%.

Conclusions: This ANN demonstrated a superior performance than the LR in predicting BL hypersensitivity without misdiagnosing severe allergic reactions. The ANN could be a helpful tool to classify the reaction risk, particularly in the identification of low-risk patients, in which an open challenge could be done to delabel patients.

Keywords: Allergy workup; Artificial neural networks; Drug hypersensitivity; Drug provocation tests; Penicillin allergy; Predictive models; Skin tests; β-Lactams.