Diagnosis and classification of infective endocarditis via efficient serum metabolic fingerprint analysis.

Infective endocarditis (IE) continues to pose significant clinical challenges as a life-threatening condition associated with 30 % mortality. The current diagnostic criteria, the 2023 Duke-International Society for Cardiovascular Infectious Diseases (ISCVID) criteria, present diagnostic challenges due to complex processes. Blood culture remains a cornerstone of IE diagnosis, enabling identification of the causative microorganism and guiding targeted antibiotic therapy. However, results typically take 2-5 days, significantly delaying critical treatment decisions. To overcome these limitations, we developed a nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI MS) platform capable of acquiring serum metabolic fingerprints (SMFs). When integrated with machine learning algorithms, this platform achieves accurate IE diagnosis (area under the curve (AUC) = 0.882) and rapid streptococcal classification within 10 min. Notably, our platform enables simultaneous IE diagnosis and classification via a single assay free of culture process. This integrated approach addresses the critical unmet need in IE management, offering transformative potential for timely therapeutic decision-making and improved patient outcomes.