Febrile Neutropenia
Conditions
Keywords
febrile neutropenia, hematologic malignancy, proteomics, sepsis, biomarkers, septic shock, mass spectrometry
Brief summary
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.
Detailed description
This multicenter, prospective, observational study will evaluate whether combined proteomic profiles of host and microbial origin can predict complications in patients with hematologic malignancies presenting with high-risk febrile neutropenia (FN). FN is defined as an oral temperature ≥38.3 °C once or ≥38.0 °C for ≥1 hour in patients with an absolute neutrophil count (ANC) \<500 cells/mm³ or expected to decrease below that threshold within 48 hours. Despite empirical broad-spectrum antibiotics, up to 50% of these patients develop sepsis, and 10% progress to septic shock. Current biomarkers such as C-reactive protein (CRP) or procalcitonin (PCT) have limited specificity in this immunocompromised population. This study proposes a novel integrative proteomic approach based on mass spectrometry to simultaneously quantify host and microbial proteins in plasma, identifying molecular patterns associated with poor outcomes. Plasma samples (10 mL, EDTA) will be obtained at two time points: the first febrile episode (prior to antibiotic administration) and 48 hours later. Proteins will be processed using PreOmics® ENRICHplus technology and analyzed via LC-MS/MS on an Evosep One-timsTOF Pro2 platform. Differentially expressed proteins will be identified using a data-independent acquisition (DIA-PASEF) workflow and validated in a subset of 200 patients through targeted mass spectrometry. Clinical, analytical, and microbiological data will be collected via the REDCap platform. Machine learning models (XGBoost, SHAP interpretability) will be used to generate a predictive risk model for complications, integrating proteomic and clinical data. This study is expected to establish a new decision-support tool for early identification of high-risk FN patients, facilitating personalized antimicrobial strategies and improved prognosis.
Interventions
BIOLOGICALPlasma and DNA sample collection for proteomic and genomic analysis
Collection 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.
Sponsors
Instituto de Investigación Biomédica de Salamanca
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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). * Fever defined as oral temperature ≥38.3 °C once or ≥38.0 °C for ≥1 hour. * Hospitalized or requiring immediate admission at the time of FN diagnosis. ´- Initial uncomplicated clinical presentation, with no previous infection or colonization by multidrug-resistant bacteria. * Eligible for initial monotherapy with broad-spectrum empirical antibiotic. * Availability for serial plasma sampling and clinical follow-up.
Exclusion criteria
* Age \<18 years. * Low-risk FN according to MASCC/CISNE criteria. * Initial sample collected after antibiotic administration. * Decline or inability to provide informed consent. * Any condition preventing safe participation or reliable sample collection. * Fever induced by noninfectious causes (considered as adjustment factor, not exclusion).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification of plasma host-microbial proteomic signatures (combitypes) associated with major complications in febrile neutropenia. | Within 7 days from fever onset. | Evaluation of proteomic profiles (human and microbial) associated with hemodynamic instability, sepsis, septic shock, or death. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Dynamic changes in plasma proteome over 48 hours | 0-48 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) | Up to 7 days. | Comparison of ROC-AUC for new proteomic models against current inflammatory markers. |
| Correlation between microbial proteomic profiles and microbiologically documented infections | During hospitalization (up to 30 days). | Association between detected microbial peptides and confirmed pathogens. |
| Development of a predictive model for complications | Study duration (36 months). | 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. |
| Validation of selected protein biomarkers by targeted mass spectrometry | By end of study (month 36). | Selected protein biomarkers previously identified through discovery-phase proteomic profiling will be validated using targeted LC-MS/MS mass spectrometry in plasma samples from 200 patients within the study cohort. This validation phase will assess the analytical performance (including reproducibility, accuracy, and sensitivity) of the selected biomarkers, as well as their clinical relevance in predicting complications such as hemodynamic instability, sepsis, septic shock, or death. This outcome cannot be divided into sepparate ones, as biomarker expression will be analyzed as a whole, given the fact that until the trial begins, the biomarkers associated with complications in oncohematologic patients with febrile neutropenia will be unkown. This outcome will determine the feasibility of implementing the identified biomarkers as prognostic tools in routine clinical practice for the early risk stratification of patients |
Countries
Spain
Contacts
CONTACTJesús Francisco Bermejo Martín, MD PhD
CONTACTNadia García Mateo, PhD
PRINCIPAL_INVESTIGATORJesús Francisco Bermejo Martín, MD PhD
Centro Asistencial Universitario de Salamanca (CAUSA)
Outcome results
None listed