Calado, CecíliaFonseca, T.A.H.Rekowski, C.P. VonAraújo, R.Oliveira, M. ConceiçãoBento, L.Justino, G.C.2024-03-212024-03-212024978-989-9121-36-2http://hdl.handle.net/10400.21/17197The available scores to predict patients’ outcomes in specific settings generally present low sensitivities and specificities when applied to intensive care units’ (ICUs) populations. Advancements in analytical techniques, notably Ultra-High Performance Liquid Chromatography- Mass Spectrometry (UHPLC HRMS) transformed biomarker identification, enabling a comprehensive profiling of biofluids, including serum. In the current work, untargeted metabolomics, utilizing UHPLC-HRMS serum analysis, was performed on 16 ICU patients, categorized as either discharged (n=8), or deceased (n=8) in average seven days post sample collection. Linear discriminant analysis (LDA) or principal component analysis (PCA)-LDA models involving different metabolite sets were developed, enabling to predict patients’ outcomes in the ICU with 92% accuracy and 83% sensitivity on validation datasets. These results highlight the advantages of UHPLC-HRMS as a platform capable of providing a set of clinically significant biomarkers to predict patients’ outcome.BiomarkersIntensive care unitPredictive modelsMetabolomicsMass Spectrometryconference objecthttps://doi.org/10.17758/EIRAI20