An international team of scientists has developed an online tool for predicting recovery from COVID-19, based on an array of complex biological signatures. Their approach could pave the way for a better understanding of the molecular mechanisms underpinning long COVID.

Initial research on SARS-CoV-2 focussed on the immune response to the virus in order to address how it is linked to the severity of the acute illness. Now that hospitalization rates have dropped, the focus of research has shifted towards questions around incomplete recovery and long-term effects of the virus.

A new, first of its kind patient-centric study, trying to understand recovery from COVID-19 from an organismal, systemic sense, has now published in the journal Nature Immunology. Through analysis of existing and new data drawn from a large patient cohort, researchers set out to exploit a tailored statistical framework to disentangle the heterogeneity of patient’s response to infection and characterise the long-term recovery profiles at the patient level.

Lead author of the study, Dr Hélène Ruffieux, Senior Research Associate at the MRC Biostatistics Unit said:

The cellular, inflammatory and metabolic dynamics driving incomplete recovery from SARS-CoV-2 are complex and subject to strong inter-patient variability. We performed longitudinal latent modelling analyses to characterise the individual patient disease trajectories, taking full advantage of detailed clinical and biological data collected over a year post disease onset.”

Immunophenotypes, molecular measurements and patient questionnaires addressing long-term symptoms were obtained in 215 patients with different clinical severities of infection.

Patients had consented to join the COVID-19 BioResource cohort within the NIHR BioResource, a research resource designed to provide participant data, samples and recall based on genotype and or phenotype.

The study identified composite signatures predictive of incomplete recovery using a joint model on cellular and molecular parameters measured soon after disease onset. These signatures can be inspected and predictions for new patients can be obtained using the online tool: Integrative prediction of systemic recovery from COVID-19. The next step for the researchers is to validate the model in an independent cohort.

Senior author of the study Prof Christoph Hess (Cambridge Institute for Therapeutic Immunology & Infectious Disease):

Our work provides a basis for monitoring changes in patient recovery profiles and understand the drivers of these changes. In addition, our statistical framework can be re-deployed on other cohorts which permits systematic comparisons towards actionable strategies for personalized intervention.”

The work was supported by the Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute for Health and Care Research (CITIID-NIHR) COVID-19 BioResource Collaboration, NIHR BioResource, Cambridge University Hospitals NHS Foundation, MRC Biostatistics Unit and Department of Medicine, University of Cambridge, Addenbrooke’s Hospital.

Read full paper: A patient-centric modeling framework captures recovery from SARS-CoV-2 infection | Nature Immunology