Therapies for Long COVID in non-hospitalised individuals: from symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study)
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Abstract
Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4–12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies.
Methods and analysis
A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.
Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.
We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.
Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group.
Ethics and dissemination
Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers.
Trial registration number
1567490.
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SciScore for 10.1101/2021.12.20.21268098: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: The study consent form will be hosted on Atom5™, which will include additional eligibility checks to ensure potential participants meet the study criteria. Sex as a biological variable not detected. Randomization The review will also summarise the evidence from randomised controlled trials and observational studies on non-pharmacological treatments for post-viral syndromes including Long COVID. Blinding not detected. Power Analysis Reliable detection of clustered data via well-chosen combinations of these methods has been shown to require a minimum of 20-30 observations per subgroup provided good cluster separation exists (effect size Δ=4 or over).[30] We expect that with 500 patients in … SciScore for 10.1101/2021.12.20.21268098: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: The study consent form will be hosted on Atom5™, which will include additional eligibility checks to ensure potential participants meet the study criteria. Sex as a biological variable not detected. Randomization The review will also summarise the evidence from randomised controlled trials and observational studies on non-pharmacological treatments for post-viral syndromes including Long COVID. Blinding not detected. Power Analysis Reliable detection of clustered data via well-chosen combinations of these methods has been shown to require a minimum of 20-30 observations per subgroup provided good cluster separation exists (effect size Δ=4 or over).[30] We expect that with 500 patients in the Long Covid cohort we will have power above 80% to detect well-separated clusters comprising at least 5% of the cohort for K=4 to K=6 clusters. Cell Line Authentication Authentication: We will evaluate algorithm performance and the optimal number of clusters (e.g., by internal validation against a holdout dataset using metrics such as Gap statistic and silhouette index).[28,29] The demographic and clinical characteristics of individuals in each symptom cluster will be described using data from Atom5™ and linked GP records in CPRD Aurum. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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