A proteomic survival predictor for COVID-19 patients in intensive care
Authors
Tober-Lau, Pinkus
Lemke, Oliver
Freiwald, Anja
Ludwig, Daniela
Stubbemann, Paula
Olk, Nadine
Messner, Christoph B
Joannidis, Michael
Sonnweber, Thomas
Klein, Sebastian J
Sahanic, Sabina
Bosquillon de Jarcy, Laure
Pfeiffer, Moritz
Jürgens, Linda
Zickler, Daniel
Spies, Claudia
Enghard, Philipp
Weiss, Günter
Marioni, Riccardo E
Löffler-Ragg, Judith
Timms, John F
Hippenstiel, Stefan
Müller-Redetzky, Holger
Suttorp, Norbert
Lilley, Kathryn
Ralser, Markus
Publication Date
2022-01-18Journal Title
PLOS Digital Health
ISSN
2767-3170
Publisher
Public Library of Science (PLoS)
Volume
1
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Demichev, V., Tober-Lau, P., Nazarenko, T., Lemke, O., Kaur Aulakh, S., Whitwell, H. J., Röhl, A., et al. (2022). A proteomic survival predictor for COVID-19 patients in intensive care. PLOS Digital Health, 1 (1) https://doi.org/10.1371/journal.pdig.0000007
Abstract
<jats:p>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.</jats:p>
Keywords
Research Article, Medicine and health sciences, Biology and life sciences, Computer and information sciences
Sponsorship
Biotechnology and Biological Sciences Research Council (BB/N015282/1)
Identifiers
pdig-d-21-00015
External DOI: https://doi.org/10.1371/journal.pdig.0000007
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333155
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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