Therapies for Long COVID in non-hospitalised individuals: From symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study)
One in 20 people who catch COVID-19 still have symptoms and reduced quality of life more than three months later, even if they were never sick enough to be hospitalised. This matters because Long COVID is poorly understood. It likely consists of several distinct syndromes with different underlying causes, but researchers have not yet mapped these out in the non-hospitalised population—the vast majority of cases. Without knowing which biological pathways are disrupted, doctors cannot match patients to effective treatments. The TLC Study will recruit a representative group of people from GP records, use machine learning to identify distinct Long COVID clusters, and then take blood samples from 50 people per cluster to measure inflammatory markers, autoantibodies, and T-cell function. Wearable devices will track heart rate, oxygen levels, activity, and sleep. The team will then review existing evidence on post-viral syndromes, prioritise potential pharmacological and supportive therapies for each cluster, and co-produce a remotely-delivered intervention with patients and clinicians. A digital trial platform will test whether that intervention works and is cost-effective. If successful, this could replace guesswork with targeted treatment pathways for specific Long COVID syndromes, and provide an infrastructure for future trials.
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Background Approximately 1 in 20 individuals with COVID-19 experience symptoms and impaired quality of life beyond 12 weeks ( Long COVID ). Long COVID may comprise several distinct syndromes yet to be fully characterised. Aim: To evaluate symptoms and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals, to co-produce a remotely-delivered intervention, and to establish a virtual trial platform. Objectives and Methods WP1 Establish a representative population-based cohort of individuals with Long COVID. A representative population-based cohort of non-hospitalised individuals with Long COVID will be established using Clinical Practice Research Datalink (CPRD) primary care records. Individuals with a diagnosis of COVID-19 at least 12 weeks prior and matched controls will be identified. We will invite them to report symptoms/quality of life through the Aparito Atom5™ digital platform. WP2 Characterise and immunologically phenotype Long COVID syndromes. Machine learning clustering techniques will be used to identify distinct Long COVID syndromes. Individuals from each cluster (n=50) and controls will be invited to undertake biosampling to measure inflammatory markers, autoantibodies, and T cell function, and wearable devices to provide data on heart rate, oxygen saturation, physical activity and sleep quality to identify potential pharmacological and supportive therapies. WP3 Provide evidence-based recommendations on targeted pharmacological and supportive therapies for Long COVID syndromes. We will review existing evidence on post-viral inflammatory syndromes and existing evidence on Long COVID to prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulated evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers. WP4 Co-produce and evaluate a virtual targeted supportive intervention for Long COVID. A digital trial platform will be established to evaluate the effectiveness, cost-effectiveness, and acceptability of the coproduced virtual supportive intervention in a randomised clinical trial, and for future research.
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