A proteomic survival predictor for COVID-19 patients in intensive care
Creators: name: Demichev, Vadim
name: Tober-Lau, Pinkus
name: Nazarenko, Tatiana
name: Lemke, Oliver
name: Aulakh, Simran Kaur
name: Whitwell, Harry J
name: Röhl, Annika
name: Freiwald, Anja
name: Mittermaier, Mirja
name: Szyrwiel, Lukasz
name: Ludwig, Daniela
name: Correia-Melo, Clara
name: Lippert, Lena J
name: Helbig, Elisa T
name: Stubbemann, Paula
name: Olk, Nadine
name: Thibeault, Charlotte
name: Grüning, Nana-Maria
name: Blyuss, Oleg
name: Vernardis, Spyros
name: White, Matthew
name: Messner, Christoph B
name: Joannidis, Michael
name: Sonnweber, Thomas
name: Klein, Sebastian J
name: Pizzini, Alex
name: Wohlfarter, Yvonne
name: Sahanic, Sabina
name: Hilbe, Richard
name: Schaefer, Benedikt
name: Wagner, Sonja
name: Machleidt, Felix
name: Garcia, Carmen
name: Ruwwe-Glösenkamp, Christoph
name: Lingscheid, Tilman
name: de Jarcy, Laure Bosquillon
name: Stegemann, Miriam S
name: Pfeiffer, Moritz
name: Jürgens, Linda
name: Denker, Sophy
name: Zickler, Daniel
name: Spies, Claudia
name: Edel, Andreas
name: Müller, Nils B
name: Enghard, Philipp
name: Zelezniak, Aleksej
name: Bellmann-Weiler, Rosa
name: Weiss, Günter
name: Campbell, Archie
name: Hayward, Caroline
name: Porteous, David J
name: Marioni, Riccardo E
name: Uhrig, Alexander
name: Zoller, Heinz
name: Löffler-Ragg, Judith
name: Keller, Markus A
name: Tancevski, Ivan
name: Timms, John F
name: Zaikin, Alexey
name: Hippenstiel, Stefan
name: Ramharter, Michael
name: Müller-Redetzky, Holger
name: Witzenrath, Martin
name: Suttorp, Norbert
name: Lilley, Kathryn
name: Mülleder, Michael
name: Sander, Leif Erik
name: group, PA-COVID-19 Study
name: Kurth, Florian
name: Ralser, Markus
Publisher: name: The Francis Crick Institute
Year: 2022
Subjects: Synthetic Biology
Chemical Biology &amp; High Throughput
Ecology,Evolution &amp; Ethology
Metabolism
Computational &amp; Systems Biology

description: 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.