Febrile neutropenia (FN) is a common oncologic emergency in patients with hematologic malignancies, associated with high morbidity and mortality. Early identification of patients at higher risk of complications such as sepsis or septic shock is critical to optimize antimicrobial management.
This study aims to characterize the human and microbial plasma proteome using high-resolution mass spectrometry to identify biomarker combinations ("combitypes") capable of predicting complications in oncohematologic patients with FN.
A cohort of 350 adult patients with high-risk FN and initially uncomplicated clinical presentation will be enrolled across three tertiary hospitals. Plasma samples will be collected at fever onset (before antibiotic initiation) and after 48 hours. Proteomic data will be integrated with clinical information using multivariate and machine learning models to develop a predictive model for complications.
Adults (≥18 years).
Written informed consent provided by patient or legal representative.
Diagnosis of hematologic malignancy under induction chemotherapy, post-allogeneic hematopoietic stem cell transplantation, or CAR-T therapy.
High-risk febrile neutropenia (ANC ≤ 100 cells/mm³, expected duration ≥ 7 days, or significant comorbidities).
Age <18 years.
Low-risk FN according to MASCC/CISNE criteria.
Initial sample collected after antibiotic administration.
Decline or inability to provide informed consent.
Oncohematologic Patients With Febrile Neutropenia
Cohort
Prospective
Plasma and DNA sample collection for proteomic and genomic analysis
Biological/VaccineCollection of 10 mL of peripheral blood in EDTA tubes at fever onset (before antibiotic initiation) and 48 hours later for proteomic and genomic analysis. Samples are processed to obtain plasma and DNA, which will be used for mass spectrometry-based proteomics and potential metagenomic studies.
Identification of plasma host-microbial proteomic signatures (combitypes) associated with major complications in febrile neutropenia.
Evaluation of proteomic profiles (human and microbial) associated with hemodynamic instability, sepsis, septic shock, or death.
Dynamic changes in plasma proteome over 48 hours
Assessment of longitudinal variations in host and microbial protein levels between baseline and 48 hours.
Predictive performance of identified combitypes versus conventional biomarkers (CRP, PCT)
Comparison of ROC-AUC for new proteomic models against current inflammatory markers.
Correlation between microbial proteomic profiles and microbiologically documented infections
Association between detected microbial peptides and confirmed pathogens.
Development of a predictive model for complications
A predictive risk model for complications will be developed using machine-learning algorithms (XGBoost) based on the integration of plasma proteomic data and relevant clinical parameters collected at fever onset and during follow-up. The model will be trained and internally validated within the full study cohort using cross-validation techniques to optimize predictive performance and minimize overfitting.
Click on the lead sponsor to view all of their trials.
