ArticlePLOS digital health2022
A proteomic survival predictor for COVID-19 patients in intensive care.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.Annals of medicine · 2026Article
- Pre-ECMO characteristics, organ dysfunction and exploratory mortality prediction in COVID-19 versus non-COVID-19 ARDS treated with veno-venous ECMO: a retrospective cohort study.BMC anesthesiology · 2026Article
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- Combining label-free Raman spectroscopy and machine learning to identify early biomarkers of COVID-19 disease severity and mortality.Journal of biomedical optics · 2026Article
- Innate Immune Evasion of Lyme Disease Pathogen Drives Alzheimer-Like Pathology.Research square · 2026Article
- Proteomic predictors of mortality among people with Coronary Artery Disease.American journal of preventive cardiology · 2025Article
- AHDSN: an attention-enabled hybrid deep sequential network for cancer survivability prediction from multi-omics data.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Proteomics and Metabolomics Profiles of Unvaccinated Nonagenarian Patients with Severe SARS-CoV-2 Infection.Journal of proteome research · 2025Article
- Plasma Proteomics Reveals Distinct Signatures in Occult and Microfilaremic Loa loa Infections.The Journal of infectious diseases · 2025Article
- Multiplex Targeted Proteomic Analysis of Cytokine Ratios for ICU Mortality in Severe COVID-19.Proteomes · 2025Article
- The Plasma Proteome and Risk of Future Venous Thromboembolism-Results from the HUNT Study in Thrombosis and Haemostasis.Thrombosis and haemostasis · 2025Article
- Differences in biomarker levels and proteomic survival prediction across two COVID-19 cohorts with distinct treatments.iScience · 2025Article
- Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.Current gene therapy · 2025Article
- The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends.Journal of proteome research · 2024Article
- Delineating sex-specific circulating host response signatures associated with COVID-19 severity and mortality.iScience · 2024Article
- Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes.Science advances · 2024Article
- A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning.Clinical proteomics · 2024Article
- Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics.Nature biomedical engineering · 2024Article
- Complex changes in serum protein levels in COVID-19 convalescents.Scientific reports · 2024Article
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Authors and funding
70 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.