TY -非盟的市长,Nikhil AU - Meza-Torres Bernardo AU - Okusi,塞西莉亚盟——Delanerolle Gayathri AU -查普曼,马丁•王盟——Wenjuan AU - Anand, Sneha盟——视野中时,迈克尔•AU -麦杰克AU - Byford,瑞秋盟——快乐,马克AU - Gatenby,皮尔斯盟——Curcin瓦萨号非盟-格林哈尔希特丽莎AU -德莱尼,布伦丹盟- de Lusignan西蒙PY - 2022 DA - 2022/8/11 TI -开发一个长COVID表型Postacute COVID-19国家初级保健前哨队列:观察回顾数据库分析乔- JMIR公共卫生Surveill SP - e36989六世- 8 - 8 KW -医疗记录系统KW -电脑KW -系统化的医学术语KW - postacute COVID-19综合症KW -表型KW - COVID-19 KW -长COVID KW -种族KW -社会阶层KW -全科医生KW -数据准确性KW -数据提取KW -生物医学本体KW - SARS-CoV-2 KW -住院KW -流行病学KW -监视KW -公共卫生千瓦生物门户KW -电子健康记录KW -疾病管理KW -数字工具AB -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。我们还比较了记录为急性感染的患者、住院的LC患者和未住院的LC患者。 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. SN - 2369-2960 UR - https://publichealth.www.mybigtv.com/2022/8/e36989 UR - https://doi.org/10.2196/36989 UR - http://www.ncbi.nlm.nih.gov/pubmed/35861678 DO - 10.2196/36989 ID - info:doi/10.2196/36989 ER -