@文章{信息:doi/10.2196/36989，作者=“市长，Nikhil和Meza-Torres, Bernardo和Okusi, Cecilia和Delanerolle, Gayathri和Chapman, Martin和Wang, Wenjuan和Anand, Sneha和Feher, Michael和Macartney, Jack和Byford, Rachel和Joy, Mark和Gatenby, Piers和Curcin, Vasa和Greenhalgh, Trisha和Delaney, Brendan和de Lusignan, Simon”，标题=“在国家初级保健哨兵队列中为急性后COVID-19开发长COVID表型:，期刊=“JMIR公共卫生监测”，年=“2022”，月=“8”，日=“11”，卷=“8”，数=“8”，页=“e36989”，关键词=“病案系统;计算机化;医学的系统化命名法;急性后COVID-19综合征;表型;COVID-19;长COVID;种族的;社会阶层; general practitioners; data accuracy; data extracts; biomedical ontologies; SARS-CoV-2; hospitalization; epidemiology; surveillance; public health; BioPortal; electronic health record; disease management; digital tool", abstract="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 Prot{\'e}g{\'e} 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. ", issn="2369-2960", doi="10.2196/36989", url="https://publichealth.www.mybigtv.com/2022/8/e36989", url="https://doi.org/10.2196/36989", url="http://www.ncbi.nlm.nih.gov/pubmed/35861678" }