TY - JOUR
T1 - Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort
T2 - Observational Retrospective Database Analysis
AU - Mayor, Nikhil
AU - Meza-Torres, Bernardo
AU - Okusi, Cecilia
AU - Delanerolle, Gayathri
AU - Chapman, Martin
AU - Wang, Wenjuan
AU - Anand, Sneha
AU - Feher, Michael
AU - Macartney, Jack
AU - Byford, Rachel
AU - Joy, Mark
AU - Gatenby, Piers
AU - Curcin, Vasa
AU - Greenhalgh, Trisha
AU - Delaney, Brendan
AU - de Lusignan, Simon
N1 - Funding Information:
This study shares funding sources through Predicting Risk of Hospital Admission in Patients with Suspected COVID-19 in a Community Setting (RECAP). The sources of funding for RECAP include the Community Jameel and the Imperial College President’s Excellence Fund, the Economic and Social Research Council (ES/V010069/1), the UK Research and Innovation, Health Data Research UK (HDRUK2020.139), the National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre, the NIHR Oxford Biomedical Research Centre (BRC-1215-20008 and 132807), and the NIHR Imperial Patient Safety Translational Research Centre.
Funding Information:
The study was funded by Health Data Research UK, Urgent Public Health Funding.
Funding Information:
We thank patients registered with practice members of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) who allowed their pseudonymized data to be shared for this research. We also thank EMIS, TPP, InPractice Systems, and Wellbeing for cooperation to facilitate data extraction. The RSC is principally funded by the UK Health Security Agency.
Funding Information:
NM was funded by a Health Education England Academic Foundation Programme. BM-T was supported by the Marie Skłodowska–Curie Innovative Training Network (HealthPros – Healthcare Performance Intelligence Professionals) funded by the European Union Research Framework Programme Horizon 2020 (Grant agreement 765141).
Publisher Copyright:
© Nikhil Mayor, Bernardo Meza-Torres, Cecilia Okusi, Gayathri Delanerolle, Martin Chapman, Wenjuan Wang, Sneha Anand, Michael Feher, Jack Macartney, Rachel Byford, Mark Joy, Piers Gatenby, Vasa Curcin, Trisha Greenhalgh, Brendan Delaney, Simon de Lusignan.
PY - 2022/8/11
Y1 - 2022/8/11
N2 - Background: Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. Objective: This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. Methods: We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics-defined LC symptoms between groups. We used descriptive statistics and logistic regression. Results: The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P < .001) with uncomplicated COVID-19. Conclusions: Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.
AB - Background: Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition. Objective: This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not. Methods: We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics-defined LC symptoms between groups. We used descriptive statistics and logistic regression. Results: The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P < .001) with uncomplicated COVID-19. Conclusions: Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.
KW - biomedical ontologies
KW - BioPortal
KW - computerized
KW - COVID-19
KW - data accuracy
KW - data extracts
KW - digital tool
KW - disease management
KW - electronic health record
KW - epidemiology
KW - ethnicity
KW - general practitioners
KW - hospitalization
KW - long COVID
KW - medical record systems
KW - phenotype
KW - postacute COVID-19 syndrome
KW - public health
KW - SARS-CoV-2
KW - social class
KW - surveillance
KW - Systematized Nomenclature of Medicine
UR - http://www.scopus.com/inward/record.url?scp=85135906317&partnerID=8YFLogxK
U2 - 10.2196/36989
DO - 10.2196/36989
M3 - Article
AN - SCOPUS:85135906317
SN - 2369-2960
VL - 8
JO - JMIR Public Health and Surveillance
JF - JMIR Public Health and Surveillance
IS - 8
M1 - e36989
ER -
