Prosthetic valve infective endocarditis (PVIE) remains among the most clinically complex infectious syndromes because diagnosis is frequently time-sensitive, microbiology can be inconpletely informative, host responses are heterogeneous, and management decisions must balance antimicrobial efficacy, embolic and hemodynamic risk, and surgical timing. Contemporary “omics” technologies—genomics, transcriptomics, proteomics, metabolomics, and integrative multi-omics—offer a pathway to strengthen PVIE care by moving beyond single-marker diagnostics toward composite, mechanism-informed signatures of pathogen presence, virulence, host immune activation, tissue injury, and treatment response (Horgan & Kenny, 2011; D’Adamo et al., 2021; Chen et al., 2023). However, clinical translation is constrained by analytic complexity, interpretability, operational feasibility, and the risk of overpromising clinical utility without robust validation (Boyd et al., 2014; Subramanian et al., 2020; Wekesa & Kimwele, 2023). This study develops a publication-ready conceptual and methodological framework for integrating multi-omics diagnostics into PVIE clinical pathways, with the goal of improving diagnostic certainty, prognostic stratification, and treatment monitoring. Using a structured qualitative synthesis of the provided literature, we derive a PVIE-specific multi-omics logic model that maps clinical questions to omics layers, proposes integration strategies suited to heterogeneous clinical data, and anticipates implementation challenges in real-world endocarditis teams (Subramanian et al., 2020; Shahrajabian & Sun, 2023; Sibilio et al., 2025). Particular attention is given to PVIE due to Staphylococcus aureus, given its clinical severity and prognostic importance, and to emerging echocardiographic criteria and evolving clinical research directions in infective endocarditis (Diego-Yagüe et al., 2024; Poggio Pantte & Santa María, 2024; Editorial, 2023; Olmos et al., 2024). The resulting framework emphasizes clinical actionability, staged implementation (from targeted panels to integrative signatures), and governance of uncertainty to ensure that multi-omics strengthens rather than complicates patient care (Boyd et al., 2014; D’Adamo et al., 2021).

Prosthetic valve endocarditis, multi-omics integration, transcriptomics

Boyd, S. D., Galli, S. J., Schrijver, I., Zehnder, J. L., Ashley, E. A., & Merker, J. D. (2014). A balanced look at the implications of genomic (and other “omics”) testing for disease diagnosis and clinical care. Genes, 5(3), 748–766.

Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. P. (2023). Applications of multi-omics analysis in human diseases. MedComm, 4(4), e315.

D’Adamo, G. L., Widdop, J. T., & Giles, E. M. (2021). The future is now? Clinical and translational aspects of “omics” technologies. Immunology and Cell Biology, 99(2), 168–176.

Diego-Yagüe, I., Ramos-Martínez, A., Muñoz, P., et al. (2024). Clinical features and prognosis of prosthetic valve endocarditis due to Staphylococcus aureus. European Journal of Clinical Microbiology & Infectious Diseases, 43, 1989–2000. https://doi.org/10.1007/s10096-024-04848-1

Editorial. (2023). Special issue: Infective endocarditis—what is new in the clinical research? Journal of Clinical Medicine, 12(15), 5064. https://doi.org/10.3390/jcm12155064

Horgan, R. P., & Kenny, L. C. (2011). ‘Omic’ technologies: Genomics, transcriptomics, proteomics and metabolomics. The Obstetrician & Gynaecologist, 13(3).

Latterich, M., Abramovitz, M., & Leyland-Jones, B. (2008). Proteomics: New technologies and clinical applications. European Journal of Cancer, 44(18), 2737–2741.

López, E., Madero, L., López-Pascual, J., & Latterich, M. (2012). Clinical proteomics and OMICS clues useful in translational medicine research. Proteome Science, 10(1), 35.

Olmos, C., Lancellotti, P., et al. (2024). Novedades en la endocarditis infecciosa. Revista Española de Cardiología, 77(9), 779–787. https://doi.org/10.1016/j.recesp.2024.03.011

Poggio Pantte, C. L., & Santa María, M. (2024). Valvulitis aórtica protésica aislada: Un nuevo criterio de endocarditis infecciosa. Revista de Ecocardiografía Práctica, 8(2). https://doi.org/10.37615/retic.v8n2a11

Sager, M., Yeat, N. C., Pajaro-Van der Stadt, S., Lin, C., Ren, Q., & Lin, J. (2015). Transcriptomics in cancer diagnostics: Developments in technology, clinical research and commercialization. Expert Review of Molecular Diagnostics, 15(12), 1589–1603.

Shahrajabian, M. H., & Sun, W. (2023). Survey on multi-omics, and multi-omics data analysis, integration and application. Current Pharmaceutical Analysis, 19(4), 267–281.

Sibilio, P., De Smaele, E., Paci, P., & Conte, F. (2025). Integrating multi-omics data: Methods and applications in human complex diseases. Biotechnology Reports, e00938.

Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.

Tagoe, B., Quainoo, L., & Amponsah, S. K. (2026). Current technological approaches to the utility of omics (metabolomics, lipidomics, genomics, and transcriptomics) in renal diseases diagnosis, prognosis, and treatment. In Understanding Renal Biochemistry (pp. 145–166). Academic Press.

Wekesa, J. S., & Kimwele, M. (2023). A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Frontiers in Genetics, 14, 1199087.