Project description
Currently, post acute sequelae of SARS-CoV-2 infection (PASC) is poorly characterized and the etiology is likely heterogenous. Different risk factors were already discussed (e.g. age, gender, hospitalization, pre-existing chronic illness) to be associated with the PASC, but it is still challenging to characterize and detect populations at risk of developing PASC for symptom monitoring. Such high heterogeneity demands that patients be considered and treated as individuals, rather than grouped and managed based on signs and symptoms.
The UriCoV project aims at the development of urinary peptides pattern (UPP) that will phenotype the individual patients previously infected by SARS-CoV-2 and identify patients at risk of PASC, also referred to as "long COVID”, within 10 days of COVID onset. This strategy using individual omics and clinical data will allow patient stratification to initiate personalized treatment for prevention of PASC.
The basic for this project is the multicentre “Prospective Validation of a Proteomic Urine Test for Early and Accurate Prognosis of Critical Course Complications in Patients with SARS-CoV-2 Infection Study (Crit-CoV-U)” funded by the Federal Ministry of Health in Germany (BMG, #V2020.6/1503_68403/2020-2021). Within the Crit-CoV-U project a COVID-19 biobank was established based on which a urinary peptide-based biomarker classifier based on 50 selected sequenced peptides was developed.
This classifier, CoV50, pre-emptively indicates the risk of life-threatening COVID-19 course with 87% certainty in the derivation cohort of 228 patients [PMID: 33969282]. The classifier was validated in the full prospective cohort of >1000 patients [PMID: 36057526]. Now, the previous Crit-CoV-U study together with follow-up data to be collected within this project will be used for the development of a tool for personalised prognosis and subsequent primary prevention of PASC.
Project objectives:
- follow-up COVID-patients from the Crit-CoV-U study and identify participants suffering from PASC by applying a standardised, established PASC questionnaire;
- evaluate baseline UPP for predictive biomarkers with regard to PASC;
- integrate omics, demographic and clinical data using machine learning and develop tailor-made diagnostic tools for the prediction of PASC;
- investigate the molecular mechanism of PASC based on the identified PASC-associated changes;
- assess the societal cost of PASC by means of occupancy insurance input, its impact on HRQoL, and the economic gains that would be associated with implementing the algorithm in current practice.
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