Development of a predictive model for eosinophilia in patients treated with ampicillin/sulbactam or piperacillin/tazobactam

Aim We aim to identify risk factors for antibiotic‐induced eosinophilia in hospitalized patients receiving penicillin/beta‐lactamase inhibitor therapy and to develop a machine learning–enhanced predictive risk‐scoring model. Methods A retrospective cohort study was conducted involving 490 hospitalized patients treated with intravenous ampicillin/sulbactam or piperacillin/tazobactam. After applying eligibility criteria, 378 patients were included in the final analysis. Multivariate logistic regression was used to identify independent risk factors for eosinophilia, which were incorporated into a clinical risk‐scoring system. To improve predictive accuracy, machine learning models including elastic net regression were developed and evaluated using five‐fold cross‐validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). Results Among the 378 patients, 27 (7.1%) developed eosinophilia. Nine independent risk factors were identified: aminoglycosides, arrhythmias, chlorpheniramine maleate, glycopeptides, levetiracetam, polystyrene sulfonate, rheumatoid arthritis, stroke and ursodeoxycholic acid. The risk score ranged from 0 to 8, corresponding to predicted eosinophilia probabilities of 3.3% to 93.8% (AUROC = 0.787). Among the machine learning approaches tested, elastic net regression achieved the highest predictive performance. Conclusion This study proposes a clinically applicable risk‐scoring model for predicting eosinophilia in patients treated with penicillin/beta‐lactamase inhibitors. Incorporating machine learning improved model accuracy, enabling more precise identification of high‐risk patients. The model supports proactive risk assessment and may inform tailored monitoring and intervention strategies in clinical settings.