20.500.12556/DiRROS-28127 A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM) a multi-cohort machine learning study Background: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. Methods: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. Findings: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-gamma), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89 center dot 4% and 93 center dot 6%. Interpretation: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. C-reactive protein antibiotic overuse virus-infection resistance true false true Angleški jezik Ni določen Neznano 2026-03-10 14:30:58 2026-03-10 14:30:58 2026-03-11 03:59:26 0000-00-00 00:00:00 2023 0 0 Nasl. z nasl. zaslona; Opis z dne 22. 7. 2025; str. e774-e785 issue 11 Vol. 5 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616-053.2 2589-7500 10.1016/S2589-7500(23)00149-8 243409667 RAZ_Jackson_Heather_R._2023.pdf RAZ_Jackson_Heather_R._2023.pdf 1 0334F6CA3B50C6C2830F83F66CE88449 13fe219ba6f3cc76e7535a32fc8c056ab6aea81140a631e78486d407cac95f3e 913caf64-1c85-11f1-acf8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=41578 https://www.thelancet.com/journals/landig/article/PIIS2589-7500(23)00149-8/fulltext 0 abe7be2b-1c85-11f1-acf8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=41579 Univerzitetni klinični center Ljubljana 0 0 0