Predicting persistent fever in patients with cancer receiving antibiotics: a machine learning approach

Abstract Persistent fever after initiating intravenous antibiotics is common in hospitalized patients with cancer and often leads to additional imaging and antimicrobial escalation. Early identification of patients unlikely to remain febrile could provide additional information during clinical reassessment. We developed and externally validated machine learning models to predict persistent fever 48–72 h after antibiotic initiation using electronic health records from two academic hospitals. Adult cancer patients with fever within 24–48 h of treatment were included. Models incorporated demographics, comorbidities, laboratory results, vital signs, and temperature patterns. The best-performing model achieved an AUROC of 0.82 (95% CI 0.79–0.84) internally and 0.78 (95% CI 0.77–0.79) externally. Temperature-related variables were the dominant predictors. Decision curve analysis demonstrated positive net benefit across much of the evaluated threshold range. Retrospective assessment suggested that approximately one quarter of antibiotic escalations and chest CT scans occurred in patients correctly predicted not to remain febrile, highlighting the potential contextual value of fever predictions. These findings indicate that short-term fever persistence can be predicted with clinically meaningful accuracy using routine data. However, clinical use should be limited to an adjunctive role rather than standalone rule-out decision-making, and prospective studies are required to evaluate safety, clinician uptake, and real-world impact.