Decision support for prediction and management of Long Covid Syndrome (LCS)

Project Information

Long Covid

Grant agreement ID: 101057553

DOI

10.3030/101057553

Project closed

EC signature date 23 May 2022

Start date 1 June 2022

End date 31 May 2026

Funded under

• Health

Total cost

€ 6 549 674,79

EU contribution

€ 6 549 673,69

Coordinated by

HUS-YHTYMA
Finland

Periodic Reporting for period 2 - Long Covid (Decision support for prediction and management of Long Covid Syndrome (LCS))

Reporting period: 2023-12-01 to 2025-05-31

Summary of the context and overall objectives of the project

We will develop tools and knowledge to support physicians in accurately managing Long COVID syndrome (LCS) which has a significant impact on sufferers as well as their surroundings. Although much is now known regarding appropriate clinical management of acute COVID-19, very little is known about clinical manifestations, risk factors and underlying mechanisms for development of the highly heterogenous LCS. In this project, we aim to understand and mechanisms of LCS by combining front-line expertise from the fields of clinical medicine, virology, metabolism and immunology. We will study the pathogenesis of LCS by conducting geographically diverse cohort and registry studies, by conducting mechanistic studies, by using novel high-throughput methods for biomarker analysis, and by conducting interventional and follow-up studies on LCS patients. We will combine results from clinical and mechanistic studies to identify molecular and physiological parameters and/or pathways to decipher the mechanisms underlying LCS. We will exploit the high-throughput omics technologies to identify the predisposing factors and biomarkers that lead to the development of LCS. We will collect data from the cohort, mechanistic, biomarker and interventional studies and use these to validate the predictive artificial intelligence algorithms and to produce information and gain understanding on the combination of factors that lead to certain clustering of patients into different groups with specific symptoms. A machine learning and AI-informed Long Covid Prediction Support (LCPS) tool will be developed for the use of clinicians to predict the LCS and its possible clinical manifestations in patients. It will also help in the choice of personalized treatments for LCS patients. Additionally, an interactive graphic user interface infographic will also be available to clinicians and patients; this will communicate novel and understandable information about LCS and recommendations for patient management.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Since the project is in early stages, most of the results are still to come. We will study the pathogenesis of LCS by conducting geographically diverse cohort and registry studies, by conducting mechanistic studies, by using novel high-throughput methods for biomarker analysis, and by conducting interventional and follow-up studies on LCS patients. We will combine results from clinical and mechanistic studies to identify molecular and physiological parameters and/or pathways to decipher the mechanisms underlying LCS. We will exploit the high-throughput omics technologies to identify the predisposing factors and biomarkers that lead to the development of LCS.

At the beginning of the second reporting period, the Helsinki Long COVID cohort sent its final samples to the designated laboratories, while the Basel cohort continued sample collection until March 2025. Overall, analyses in the project are off to a strong start, with 11 scientific publications already completed, five published as pre-prints in archives and many more currently under review. In the biomechanistic analyses, some interesting findings have emerged, particularly in metabolomics. These findings will be further refined as researchers conduct deeper analyses using the full clinical and registry data available in HUS Acamedic.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Since the project is in early stages, most of the results and Impacts are still to come. We will collect data from the cohort, mechanistic, biomarker and interventional studies and use these to validate the predictive artificial intelligence algorithms and to produce information and gain understanding on the combination of factors that lead to certain clustering of patients into different groups with specific symptoms. A machine learning and AI-informed Long Covid Prediction Support (LCPS) tool will be developed for the use of clinicians to predict the LCS and its possible clinical manifestations in patients. It will also help in the choice of personalized treatments for LCS patients. Additionally, an interactive graphic user interface infographic will also be available to clinicians and patients; this will communicate novel and understandable information about LCS and recommendations for patient management.