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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 pre
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