Demichev, V;
Tober-Lau, P;
Nazarenko, T;
Lemke, O;
Kaur Aulakh, S;
Whitwell, HJ;
Röhl, A;
... Ralser, M; + view all
(2022)
A proteomic survival predictor for COVID-19 patients in intensive care.
PLOS Digital Health
, 1
(1)
, Article e0000007. 10.1371/journal.pdig.0000007.
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Abstract
Global 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.
| Type: | Article |
|---|---|
| Title: | A proteomic survival predictor for COVID-19 patients in intensive care |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pdig.0000007 |
| Publisher version: | https://doi.org/10.1371/journal.pdig.0000007 |
| Language: | English |
| Additional information: | © 2022 Demichev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142558 |
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