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

2024Conference object Open access
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dc.contributor.authorCalado, Cecília
dc.contributor.authorFonseca, T.A.H.
dc.contributor.authorRekowski, C.P. Von
dc.contributor.authorAraújo, R.
dc.contributor.authorOliveira, M. Conceição
dc.contributor.authorBento, L.
dc.contributor.authorJustino, G.C.
dc.date.accessioned2024-03-21T17:53:34Z
dc.date.available2024-03-21T17:53:34Z
dc.date.issued2024
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doihttps://doi.org/10.17758/EIRAI20pt_PT
dc.identifier.isbn978-989-9121-36-2
dc.identifier.urihttp://hdl.handle.net/10400.21/17197
dc.publisherInternational Forum of Engineers & Practitionerspt_PT
dc.relationThe dynamics of biomarkers as a predictive factor in the morbidity and mortality of critically ill patients.
dc.relationDiagnosis and prognosis disease biomarkers on critically ill patients with COVID towards a precision medicine – a machine learning approach
dc.subjectBiomarkerspt_PT
dc.subjectIntensive care unitpt_PT
dc.subjectPredictive modelspt_PT
dc.subjectMetabolomicspt_PT
dc.subjectMass Spectrometrypt_PT
dc.titlePredicting Critically Ill Patients Outcome in the ICU usinh UHPLC-HRMS datapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleThe dynamics of biomarkers as a predictive factor in the morbidity and mortality of critically ill patients.
oaire.awardTitleDiagnosis and prognosis disease biomarkers on critically ill patients with COVID towards a precision medicine – a machine learning approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0117%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//2023.01951.BD/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/2021.05553.BD/PT
oaire.citation.conferencePlacePortugalpt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title3rd International Conference on Challenges in Engineering, Medical, Economics and Education: Research & Solutions (CEMEERS-24a)pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStreamOE
person.familyNameCalado
person.givenNameCecília
person.identifier130332
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id6603163260
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublication.latestForDiscoverye8577257-c64c-4481-9b2b-940fedb360cc
relation.isProjectOfPublication8f80f51c-b563-49e2-9864-a2c78479fc19
relation.isProjectOfPublication0e941509-e437-436e-9e84-2618ba108bfd
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