Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data
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 t...
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| Format: | Article |
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BMJ Publishing Group
2022-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/7/e059385.full |
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| author | Jennifer K Quint Aziz Sheikh Chris Robertson Srinivasa Vittal Katikireddi Emily Moore Colin R Simpson Luke Daines Eleftheria Vasileiou Syed Ahmar Shah Rachel H Mulholland Vicky Hammersley Steven Kerr Ting Shi David Weatherill Elisa Pesenti |
| author_facet | Jennifer K Quint Aziz Sheikh Chris Robertson Srinivasa Vittal Katikireddi Emily Moore Colin R Simpson Luke Daines Eleftheria Vasileiou Syed Ahmar Shah Rachel H Mulholland Vicky Hammersley Steven Kerr Ting Shi David Weatherill Elisa Pesenti |
| author_sort | Jennifer K Quint |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1e35aca783714c30ae7e363962488b94 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-1e35aca783714c30ae7e363962488b942025-01-30T15:15:09ZengBMJ Publishing GroupBMJ Open2044-60552022-07-0112710.1136/bmjopen-2021-059385Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish dataJennifer K Quint0Aziz Sheikh1Chris Robertson2Srinivasa Vittal Katikireddi3Emily Moore4Colin R Simpson5Luke Daines6Eleftheria Vasileiou7Syed Ahmar Shah8Rachel H Mulholland9Vicky Hammersley10Steven Kerr11Ting Shi12David Weatherill13Elisa Pesenti14Imperial College London, London, UKCentre for Medical Informatics, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UKPublic Health Scotland, Glasgow and Edinburgh, UK3 MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK2Public Health UKSchool of Health, Victoria University of Wellington, Wellington, New ZealandUsher Institute, The University of Edinburgh, Edinburgh, UKCentre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UKThe University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UKUsher Institute, The University of Edinburgh, Edinburgh, UKUsher Institute, The University of Edinburgh, Edinburgh, UKUsher Institute, The University of Edinburgh, Edinburgh, UKUsher Institute, The University of Edinburgh, Edinburgh, UKUsher Institute, The University of Edinburgh, Edinburgh, UKInstitute of Cell Biology, University of Edinburgh, Edinburgh, UKIntroduction 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.https://bmjopen.bmj.com/content/12/7/e059385.full |
| spellingShingle | Jennifer K Quint Aziz Sheikh Chris Robertson Srinivasa Vittal Katikireddi Emily Moore Colin R Simpson Luke Daines Eleftheria Vasileiou Syed Ahmar Shah Rachel H Mulholland Vicky Hammersley Steven Kerr Ting Shi David Weatherill Elisa Pesenti Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data BMJ Open |
| title | Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data |
| title_full | Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data |
| title_fullStr | Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data |
| title_full_unstemmed | Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data |
| title_short | Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data |
| title_sort | deriving and validating a risk prediction model for long covid 19 protocol for an observational cohort study using linked scottish data |
| url | https://bmjopen.bmj.com/content/12/7/e059385.full |
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