ArticleThe Lancet. Digital health2022
Predictive performance and clinical application of COV50, a urinary proteomic biomarker in early COVID-19 infection: a prospective multicentre cohort study.
Article in The Lancet. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT04368013 (Host-pathogen Interactions, Immune Response, and Clinical Prognosis at COVID-19 - the CoVUm Trial), which is not on this map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Host-pathogen Interactions, Immune Response, and Clinical Prognosis at COVID-19 - the CoVUm Trial
Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
- Urinary Collagen Peptides Predict Mortality.Proteomics · 2026Article
- In silico prediction of optimal multifactorial intervention in chronic kidney disease.Journal of translational medicine · 2025Article
- Article
- Clinical Proteomics, Quo Vadis?Proteomics · 2025Article
- Mortality Risk and Urinary Proteome Changes in Acute COVID-19 Survivors in the Multinational CRIT-COV-U Study.Biomedicines · 2024Article
- Integration of Urinary Peptidome and Fecal Microbiome to Explore Patient Clustering in Chronic Kidney Disease.Proteomes · 2024Article
- Recent progress in mass spectrometry-based urinary proteomics.Clinical proteomics · 2024Review
- Multi-omics Investigations in Endocrine Systems and Their Clinical Implications.Advances in experimental medicine and biology · 2024Article
- Kidney Disease and Proteomics: A Recent Overview of a Useful Tool for Improving Early Diagnosis.Advances in experimental medicine and biology · 2024Article
- Proteomic Profiling of Urine From Hospitalized Patients With Severe Pneumonia due to SARS-CoV-2 vs Other Causes: A Preliminary Report.Open forum infectious diseases · 2023Article
- Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios.Journal of translational medicine · 2023Article
- Intracellular peptides in SARS-CoV-2-infected patients.iScience · 2023Article
- Article
- Urinary N-Terminal Pro-Brain Natriuretic Peptide Predicts Acute Kidney Injury and Severe Disease in COVID-19.Kidney & blood pressure research · 2023Observational
Corrections and comments
- Erratum issued
Authors and funding
41 authors at 20 institutions in 9 countries.
Funding
Abstract
backgroundThe SARS-CoV-2 pandemic is a worldwide challenge. The CRIT-CoV-U pilot study generated a urinary proteomic biomarker consisting of 50 peptides (COV50), which predicted death and disease progression from SARS-CoV-2. After the interim analysis presented for the German Government, here, we aimed to analyse the full dataset to consolidate the findings and propose potential clinical applications of this biomarker.
methodsCRIT-CoV-U was a prospective multicentre cohort study. In eight European countries (Austria, France, Germany, Greece, North Macedonia, Poland, Spain, and Sweden), 1012 adults with PCR-confirmed COVID-19 were followed up for death and progression along the 8-point WHO scale. Capillary electrophoresis coupled with mass spectrometry was used for urinary proteomic profiling. Statistical methods included logistic regression and receiver operating characteristic curve analysis with a comparison of the area under curve (AUC) between nested models. Hospitalisation costs were derived from the care facility corresponding with the Markov chain probability of reaching WHO scores ranging from 3 to 8 and flat-rate hospitalisation costs adjusted for the gross per capita domestic product of each country.
findingsFrom June 30 to Nov 19, 2020, 228 participants were recruited, and from April 30, 2020, to April 14, 2021, 784 participants were recruited, resulting in a total of 1012 participants. The entry WHO scores were 1-3 in 445 (44%) participants, 4-5 in 529 (52%) participants, and 6 in 38 (4%) participants; and of all participants, 119 died and 271 had disease progression. The odds ratio (OR) associated with COV50 in all 1012 participants for death was 2·44 (95% CI 2·05-2·92) unadjusted and 1·67 (1·34-2·07) when adjusted for sex, age, BMI, comorbidities, and baseline WHO score; and for disease progression, the OR was 1·79 (1·60-2·01) when unadjusted and 1·63 (1·41-1·91) when adjusted (p<0·0001 for all). The predictive accuracy of the optimised COV50 thresholds was 74·4% (71·6-77·1%) for mortality (threshold 0·47) and 67·4% (64·4-70·3%) for disease progression (threshold 0·04). When adjusted for covariables and the baseline WHO score, these thresholds improved AUCs from 0·835 to 0·853 (p=0·033) for death and from 0·697 to 0·730 (p=0·0008) for progression. Of 196 participants who received ambulatory care, 194 (99%) did not reach the 0·04 threshold. The cost reductions associated with 1 day less hospitalisation per 1000 participants were million Euro (M€) 0·887 (5-95% percentile interval 0·730-1·039) in participants at a low risk (COV50 <0·04) and M€2·098 (1·839-2·365) in participants at a high risk (COV50 ≥0·04).
interpretationThe urinary proteomic COV50 marker might be predictive of adverse COVID-19 outcomes. Even in people with mild-to-moderate PCR-confirmed infections (WHO scores 1-4), the 0·04 COV50 threshold justifies earlier drug treatment, thereby potentially reducing the number of days in hospital and associated costs.
fundingGerman Federal Ministry of Health.
Indexed as
Identifiers
What OpenQuestion holds
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.