3 Key Takeaways
Plain English Summary- 01Core Finding
Early identification of patients unlikely to remain febrile could provide additional information during clinical reassessment.
- 02Study Evidence
Tested in a ehr cohort study + clinical ai evaluation at the real-world cohort stage.
- 03Practical Meaning
These findings indicate that short-term fever persistence can be predicted with clinically meaningful accuracy using routine data.
What This Means for You
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.
The Discovery
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.
How It Works, Simply
This ai in medicine research focuses on AI model. Researchers evaluated it using an eHR Cohort study + Clinical AI evaluation at the real-world cohort stage of medical research.
3 Key Takeaways
Clinical Takeaways- 01Core Finding
Early identification of patients unlikely to remain febrile could provide additional information during clinical reassessment.
- 02Study Evidence
Tested in a ehr cohort study + clinical ai evaluation at the real-world cohort stage.
- 03Practical Meaning
These findings indicate that short-term fever persistence can be predicted with clinically meaningful accuracy using routine data.
Biological Mechanism & Study Design
Study Design & Cohort
EHR Cohort study + Clinical AI evaluation
EHR Cohort study + Clinical AI evaluation · Real-World Cohort
Biological Pathway & Modality
AI model
Intervention: Predicting persistent fever in patients with cancer receiving antibiotics: a machine
Experimental Findings & Endpoints
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.
Clinical Translation & Practice Context
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.
The Bottom Line · Clinical Pearl
“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.”
Published in npj Digital Medicine · Current Stage: Real-World Cohort
