1. A proteomic survival predictor for COVID-19 patients in intensive care
- Author
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Alexander Uhrig, Richard Hilbe, Michael Muelleder, Michael Ramharter, Oleg Blyuss, Sophy Denker, Daniel Zickler, Miriam Stegemann, Christoph B. Messner, Caroline Hayward, Riccardo E. Marioni, Clara Correia-Melo, Rosa Bellmann-Weiler, Mirja Mittermaier, Nils B. Mueller, Elisa T Helbig, Carmen Garcia, Alexey Zaikin, Moritz Pfeiffer, Ivan Tancevski, David J. Porteous, Holger Mueller-Redetzky, Daniela Ludwig, Aleksej Zelezniak, Philipp Enghard, Matthew White, Vadim Demichev, Sonja Wagner, Heinz Zoller, Sebastian J. Klein, Spyros I. Vernardis, Markus A. Keller, Harry J. Whitwell, Leif E. Sander, Annika Roehl, Felix Machleidt, Christoph Ruwwe-Gloesenkamp, Michael Joannidis, Linda Juergens, Yvonne Wohlfarter, Nana-Maria Gruening, Stefan Hippenstiel, Judith Loeffler-Ragg, Kathryn S. Lilley, Simran Kaur Aulakh, Martin Witzenrath, Guenter Weiss, Florian Kurth, Sabina Sahanic, Tilman Lingscheid, Benedikt Schaefer, Thomas Sonnweber, Laure Bosquillon de Jarcy, Anja Freiwald, Norbert Suttorp, Lena J Lippert, Markus Ralser, Charlotte Thibeault, Pinkus Tober-Lau, John F. Timms, Nadine Olk, Lukasz Szyrwiel, Alex Pizzini, Paula Stubbemann, Tatiana Nazarenko, Archie Campbell, Andreas Edel, Claudia Spies, and Oliver Lemke
- Subjects
Mechanical ventilation ,medicine.medical_specialty ,APACHE II ,Coronavirus disease 2019 (COVID-19) ,business.industry ,medicine.medical_treatment ,Clinical trial ,Intensive care ,Charlson comorbidity index ,Emergency medicine ,medicine ,SOFA score ,Risk assessment ,business - 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. Comprehensively capturing the host 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 were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 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 predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.Trial registrationGerman Clinical Trials Register DRKS00021688
- Published
- 2021