Rapid Diagnosis of Bacteremia in Febrile Pediatric Oncology Patients via Host-Response Infrared Spectroscopy of Blood and Machine Learning

Bacteremia is a life-threatening complication and a leading cause of sepsis and septic shock in patients. Conventional diagnostic methods, such as blood culture, remain the clinical gold standard but require 24-72 h, often necessitating the empirical use of broad-spectrum antibiotics, which significantly contribute to the development and spread of antimicrobial resistance (AMR). We hypothesized that the host immune system mounts a specific, detectable systemic metabolic response to bloodstream infection, biochemically distinct from that elicited by focal bacterial infection (FBI) or viral etiologies. This study presents a rapid (<1 h), objective, and culture-independent diagnostic method for bacteremia based on host-response profiling using Fourier-transform infrared (FTIR) spectroscopy of white blood cells (WBCs). Blood samples from 410 pediatric oncology patients were clinically categorized into 71 bacteremia cases, 75 FBI, 157 viral infections, and 107 afebrile controls. WBCs were analyzed using FTIR spectroscopy to capture immune-metabolic fingerprints. Spectral profiles were classified using Logistic Regression with Principal Component Analysis (PCA) feature vectors and Log-Likelihood Ratio decision logic to differentiate bacteremia. The FTIR + Machine Learning (ML) platform successfully resolved the subtle biochemical differences, achieving 94.5% accuracy, 96.5% sensitivity, and 87.8% specificity in diagnosing bacteremia from all other categories combined (FBI, viral, and control). Importantly, the platform maintained high diagnostic performance, achieving 94.6% accuracy in distinguishing bacteremia from the FBI. This approach provides early, targeted diagnostic information that can support clinical decision-making, offering a powerful analytical tool to guide antibiotic stewardship and combat the global threat of AMR in this vulnerable population.