The dynamics of biomarkers as a predictive factor in the morbidity and mortality of critically ill patients.

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Predicting Critically Ill Patients Outcome in the ICU usinh UHPLC-HRMS data

Publication . Calado, Cecília; Fonseca, T.A.H.; Rekowski, C.P. Von; Araújo, R.; Oliveira, M. Conceição; Bento, L.; Justino, G.C.

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 (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.

2024Conference object

Open access