Daines, L. et al. (2022) Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data. BMJ Open, 12(7), e059385. (doi: 10.1136/bmjopen-2021-059385) (PMID:35793922) (PMCID:PMC9260199)
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Abstract
Introduction: COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as ‘long-COVID’). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID. Methods and analysis: We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups: (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID. Ethics and dissemination: The EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Katikireddi, Professor Vittal |
| Authors: | Daines, L., Mulholland, R. H., Vasileiou, E., Hammersley, V., Weatherill, D., Katikireddi, S. V., Kerr, S., Moore, E., Pesenti, E., Quint, J. K., Shah, S. A., Shi, T., Simpson, C. R., Robertson, C., and Sheikh, A. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > MRC/CSO SPHSU |
| Journal Name: | BMJ Open |
| Publisher: | BMJ Publishing Group |
| ISSN: | 2044-6055 |
| ISSN (Online): | 2044-6055 |
| Published Online: | 06 July 2022 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in BMJ Open 12(7): e059385 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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Funder and Project Information
Funder and Project Information