A proteomic survival predictor for COVID-19 patients in intensive careMore about Open Access at the Crick
Authors listVadim Demichev Pinkus Tober-Lau Tatiana Nazarenko Oliver Lemke Simran Kaur Aulakh Harry J Whitwell Annika Röhl Anja Freiwald Mirja Mittermaier Lukasz Szyrwiel Daniela Ludwig Clara Correia-Melo Lena J Lippert Elisa T Helbig Paula Stubbemann Nadine Olk Charlotte Thibeault Nana-Maria Grüning Oleg Blyuss Spyros Vernardis Matthew White Christoph Messner Michael Joannidis Thomas Sonnweber Sebastian J Klein Alex Pizzini Yvonne Wohlfarter Sabina Sahanic Richard Hilbe Benedikt Schaefer Sonja Wagner Felix Machleidt Carmen Garcia Christoph Ruwwe-Glösenkamp Tilman Lingscheid Laure Bosquillon de Jarcy Miriam S Stegemann Moritz Pfeiffer Linda Jürgens Sophy Denker Daniel Zickler Claudia Spies Andreas Edel Nils B Müller Philipp Enghard Aleksej Zelezniak Rosa Bellmann-Weiler Günter Weiss Archie Campbell Caroline Hayward David J Porteous Riccardo E Marioni Alexander Uhrig Heinz Zoller Judith Löffler-Ragg Markus A Keller Ivan Tancevski John F Timms Alexey Zaikin Stefan Hippenstiel Michael Ramharter Holger Müller-Redetzky Martin Witzenrath Norbert Suttorp Kathryn Lilley Michael Mülleder Leif Erik Sander PA-COVID-19 Study group Florian Kurth Markus Ralser
Toggle all authors (70)
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.
Journal PLOS Digital Health
Issue number 1