効率的な血清代謝プロファイリング分析による感染性心内膜炎の診断と分類

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.