Contemporary diagnostic strategies for blood culture-negative infective endocarditis: from conventional microbiology to emerging molecular and multimodality approaches.

BACKGROUND: Blood culture-negative infective endocarditis (BCNE) remains one of the most challenging presentations of infective endocarditis (IE) because the absence of microbiological confirmation delays diagnosis and complicates therapeutic management. BCNE may result from prior antibiotic exposure, infection with fastidious or intracellular microorganisms, fungal pathogens, or non-infectious conditions mimicking IE. METHODS: This narrative review provides a clinically oriented overview of contemporary diagnostic strategies for BCNE based on current evidence and recent international guidelines. The diagnostic performance, limitations, and clinical applicability of biomarkers, microbiological methods, multimodality imaging, and pathological analysis were critically evaluated and integrated into practical diagnostic algorithms. RESULTS: Targeted serology and polymerase chain reaction (PCR), particularly on excised valve tissue, remain the most clinically validated microbiological tools for routine BCNE diagnosis. Emerging molecular approaches, including droplet digital PCR (ddPCR) and metagenomic next-generation sequencing (mNGS), show promising diagnostic potential but remain insufficiently standardized for routine implementation. Echocardiography remains the first-line imaging modality, while cardiac computed tomography, 18 F-FDG PET/CT, and white blood cell SPECT/CT (WBC SPECT/CT) provide complementary diagnostic information, particularly in prosthetic valve endocarditis (PVE) and other diagnostically challenging cases. Histopathological examination combined with molecular analysis of surgical specimens remains an important complementary diagnostic approach when valve tissue is available. CONCLUSIONS: Optimal diagnosis of BCNE requires an integrated multidisciplinary strategy combining clinical assessment, microbiology, multimodality imaging, and pathology. Future progress will depend on evidence-based diagnostic algorithms integrating emerging technologies according to their level of validation, local epidemiology, and available expertise.