Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia.
Clinical Snapshot
PICO Framework
| P — Population | Acute myeloid leukemia patients undergoing chemotherapy with neutropenic fever |
| I — Intervention | Gut microbiome composition and fecal metabolite analysis |
| C — Comparator | Machine learning models comparing infectious vs non-infectious neutropenic fever |
| O — Outcomes | Diagnostic accuracy for distinguishing infectious from non-infectious neutropenic fever using microbiome biomarkers |
Bottom Line
This diagnostic accuracy study suggests gut microbiome and metabolite profiles may help distinguish infectious from non-infectious neutropenic fever in AML patients, with moderate diagnostic performance (AUROC ~0.75). Key bacterial taxa like Akkermansia and Enterobacter were associated with infectious cases, while short-chain fatty acid producers favoured non-infectious fever. However, the study lacks crucial diagnostic accuracy metrics including sensitivity, specificity, and confidence intervals needed for clinical decision-making. The reference standard for defining infectious vs non-infectious cases is unclear, and methodological details are insufficient for replication. While the concept addresses an important clinical need to reduce unnecessary antibiotic use in neutropenic patients, significant validation in larger cohorts with robust reference standards and cost-effectiveness analysis would be required before clinical implementation. The moderate AUROC values suggest this approach might serve as an adjunct rather than standalone diagnostic tool.
Key Findings
P Value: Not reported
Effect Size: AUROC 0.769 (baseline model), AUROC 0.752 (fever onset model)
Primary Outcome: Diagnostic accuracy for distinguishing infectious from non-infectious neutropenic fever
Nnt Or Sensitivity: Sensitivity and specificity not reported
Confidence Interval: Not reported
Clinical Application
Would require specialised microbiome analysis capabilities and rapid turnaround times for clinical utility Could potentially reduce inappropriate antibiotic prescribing aligned with Australian antimicrobial stewardship guidelines, though implementation would require significant laboratory infrastructure investment AML patients undergoing chemotherapy with neutropenic fever episodes
Abstract
UNLABELLED: Neutropenic fever (NF) is often the first sign of infection in patients with hematologic malignancies, but its cause is frequently unknown, leading to broad-spectrum antibiotic use without confirmed infections. Although research links gut microbiome disruptions to treatment-related infections, it typically examines NF as the outcome, leaving a gap in understanding how the microbiome and metabolic factors distinguish infectious from non-infectious cases. Stool samples from acute myeloid leukemia patients were analyzed to characterize gut microbiome composition and fecal metabolites at baseline and at fever onset. Machine learning models, network analyses, and functional profiling were used to differentiate infectious NF vs non-infectious NF at baseline and at fever onset. The baseline model (area under the receiver operating characteristic [AUROC] = 0.769) identified higher levels of Akkermansia, Enterobacter, Escherichia-Shigella, and Flavonifractor as predictors of infectious NF, while Collinsella, Lachnospiraceae, Coprococcus, and acetic acid were linked to non-infectious cases. At fever onset, Enterococcus was enriched in infectious NF, whereas Gemella, Butyrate, Lachnospiraceae, Ruminococcaceae, and Eisenbergiella abundances favored non-infectious NF outcomes (AUROC = 0.752). Network analyses also revealed greater functional diversity and microbiome-metabolome connectivity in non-infectious cases at fever onset. This study suggests that gut microbiota and metabolites may serve as biomarkers for distinguishing infectious from non-infectious neutropenic fever, warranting further validation in larger cohorts. IMPORTANCE: Our study tackles the challenge of managing neutropenic fever (NF) in immunocompromised patients whose numbers have increased due to various immunodeficiencies and treatments that suppress immune function. Fever is often the only sign of a serious infection in these patients, yet there are neither clear patterns linking risk factors to infection nor biomarkers reliable for ruling out non-infectious causes. As a result, febrile patients are typically empirically treated for major pathogens, even in the absence of confirmed infections, which propagates antimicrobial resistance and gut dysbiosis. Our research utilizes gut microbiome and targeted metabolomic profiling from two cohorts of patients with acute myeloid leukemia undergoing chemotherapy and employs a machine learning framework to distinguish between infectious and non-infectious NFs at baseline and upon fever onset.
Related Research
Human vaccines & immunotherapeutics
Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape
30 July 2026
Blood advances
Successful gene therapy for transfusion-dependent α-thalassemia: a case report
29 July 2026
Journal of chemical information and modeling
Functionally Guided Graph Learning for Robust Cross-Patient Cell-Type Annotation in Single-Cell RNA Sequencing
29 July 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service