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dc.contributor.authorPA-COVID-19 Study group
dc.date.accessioned2022-01-20T13:15:03Z
dc.date.available2022-01-20T13:15:03Z
dc.date.issued2022-01-18
dc.identifier.citationPA-COVID-19 Study group 2022 , ' A proteomic survival predictor for COVID-19 patients in intensive care ' , PLOS Digital Health , vol. 1 , no. 1 , e0000007 . https://doi.org/10.1371/journal.pdig.0000007
dc.identifier.issn2767-3170
dc.identifier.otherPURE: 26693687
dc.identifier.otherPURE UUID: db89b947-8c9a-455c-8687-cd02cd86a654
dc.identifier.otherJisc: 26b92565a0964d4a934d4d7843d941e0
dc.identifier.otherpublisher-id: pdig-d-21-00015
dc.identifier.otherORCID: /0000-0002-0194-6389/work/106791733
dc.identifier.urihttp://hdl.handle.net/2299/25312
dc.description© 2022 Demichev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. https://creativecommons.org/licenses/by/4.0/
dc.description.abstractGlobal healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.en
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofPLOS Digital Health
dc.rightsOpen
dc.subjectResearch Article
dc.subjectMedicine and health sciences
dc.subjectBiology and life sciences
dc.subjectComputer and information sciences
dc.titleA proteomic survival predictor for COVID-19 patients in intensive careen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Physics, Astronomy and Mathematics
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Physics, Engineering & Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2022-01-18
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1371/journal.pdig.0000007
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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