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ISEL - Engenharia Química e Biológica

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Browsing ISEL - Engenharia Química e Biológica by contributor "Domingues, Nuno A. S."

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  • Predicting critically ill patients outcome in the ICU using UHPLC-HRMS data
    Publication . Henrique Fonseca, Tiago Alexandre; Von Rekowski, Cristiana; Araújo, Rúben Alexandre Dinis; Oliveira, Maria Conceição; Bento, Luís; Justino, Gonçalo; Calado, Cecília; Domingues, Nuno A. S.; Gomes, Vítor; Topcuoglu, Bulent
    The 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 (UHPLCHRMS) 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. The 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 (UHPLCHRMS) 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.
    2024-03Conference paper Open access Show more