%0期刊文章%@ 2369-2960 %I JMIR出版物%V 8% 卡塔尔世界杯8强波胆分析N 8% P e36989% T在国家初级保健哨站队列中为急性后COVID-19开发长COVID表型:观察性回顾性数据库分析%A Mayor,Nikhil %A Meza-Torres,Bernardo %A Okusi,Cecilia %A Delanerolle,Gayathri %A Chapman,Martin %A Wang,Wenjuan %A Anand,Sneha %A Feher,Michael %A Macartney,Jack %A Byford,Rachel %A Joy,Mark %A Gatenby,Piers %A Curcin,Vasa %A Greenhalgh,Trisha %A Delaney,Brendan %A de Lusignan,Simon + Nuffield牛津大学初级保健健康科学系，Eagle House, 7 Walton Well Road, Oxford, OX2 6ED，英国，44 01865 616600，simon.delusignan@phc.ox.ac.uk %K病历系统%K计算机化%K系统化医学术语%K急性后COVID-19综合征%K表型%K COVID-19 %K长COVID %K种族%K社会阶级%K全科医生%K数据准确性%K数据提取%K生物医学本体%K SARS-CoV-2 %K住院%K流行病学%K监测%K公共卫生%K生物门户%K电子健康记录%K疾病管理%K数字工具%D 2022 %7 11.8.2022 %9原始论文%JJMIR公共卫生监测背景:COVID-19之后，多达40%的人有持续的健康问题，称为急性后COVID-19或长COVID (LC)。LC从单一的持续症状到复杂的多系统疾病。研究表明，这种情况在初级保健记录中记录不足，并试图更好地定义其临床特征和管理。表型为病例定义和从常规数据中识别提供了标准方法，通常可由机器处理。LC表型可以支持对这种情况的研究。目的:本研究旨在建立LC的表型，为该病的流行病学和未来研究提供依据。我们比较了从2020年3月1日到2021年4月1日记录的LC患者在指数感染前后的临床症状。 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. %M 35861678 %R 10.2196/36989 %U https://publichealth.www.mybigtv.com/2022/8/e36989 %U https://doi.org/10.2196/36989 %U http://www.ncbi.nlm.nih.gov/pubmed/35861678