Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis.
Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis.
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DOI：
10.2196/36989
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发表时间：
2022-08-11
影响因子：
8.5
通讯作者：
de Lusignan, Simon
中科院分区：
文献类型：
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作者：
Mayor, Nikhil;Meza-Torres, Bernardo;Okusi, Cecilia;Delanerolle, Gayathri;Chapman, Martin;Wang, Wenjuan;Anand, Sneha;Feher, Michael;Macartney, Jack;Byford, Rachel;Joy, Mark;Gatenby, Piers;Curcin, Vasa;Greenhalgh, Trisha;Delaney, Brendan;de Lusignan, Simon
COVID-19之后，高达40%的人有持续的健康问题，称为急性COVID-19或长期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，并寻求更好地定义其临床特征和管理。表型为病例定义和常规数据识别提供了一种标准方法，并且通常是机器可处理的。LC表型可以支持对这种情况的研究。本研究旨在开发LC的表型，为流行病学和未来的研究提供信息。我们比较了从2020年3月1日至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人与住院和未住院的LC患者。我们使用的数据来自牛津皇家全科医师学院（RCGP）研究和监测中心（RSC）数据库的初级保健哨兵队列（PCSC）。这个网络被招募成为英国人口的全国代表。我们使用我们建立的3步本体论方法开发了LC表型：（1）本体论步骤（定义支持表型的推理过程），（2）编码步骤（探索可用的临床术语，以及（3）逻辑提取模型（测试性能）。我们在BioPortal的本体网络语言中使用Protégé并使用PhenoFlow创建了该表型的一个版本。接下来，我们使用表型来比较LC患者（1）在获得COVID-19之前一年的症状和（2）急性COVID-19患者。我们还比较了LC住院患者与未住院患者。我们比较了两组之间的社会人口统计学细节、合并症和国家统计局定义的LC症状。我们使用描述性统计和逻辑回归。长期COVID表型将LC住院患者与未发现索引感染的患者区分开来。PCSC（N= 740万）包括428，479名经实验室检测确诊的急性COVID-19患者和10，772名临床诊断的COVID-19患者。共7471（1.74%，95%CI 1.70-1.78）人被编码为LC，1009（13.5%，95% CI 12.7-14.3）因急性COVID-19住院，6462（86.5%，95%CI 85.7-87.3）未住院，其中2728例（42.2%）未记录COVID-19指数日期。此外，1009（13.5%，95%CI 12.73-14.28）例LC患者住院，而17，993（4.5%，95%CI 4.48-4.61; P<.001）例无并发症的COVID-19患者住院。我们的LC表型能够在常规数据集中识别患有该疾病的个体，通过回顾性研究促进他们与未受影响的人进行比较。本表型及其研究方案的表面效度有助于更好地理解LC。
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. 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. 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. 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. 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.
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影响因子：
17.3
作者：
Aiyegbusi OL;Hughes SE;Turner G;Rivera SC;McMullan C;Chandan JS;Haroon S;Price G;Davies EH;Nirantharakumar K;Sapey E;Calvert MJ;TLC Study Group
通讯作者：
TLC Study Group
影响因子：
8.5
作者：
de Lusignan S;Liyanage H;McGagh D;Jani BD;Bauwens J;Byford R;Evans D;Fahey T;Greenhalgh T;Jones N;Mair FS;Okusi C;Parimalanathan V;Pell JP;Sherlock J;Tamburis O;Tripathy M;Ferreira F;Williams J;Hobbs FDR
通讯作者：
Hobbs FDR
影响因子：
15.2
作者：
Brat, Gabriel A.;Weber, Griffin M.;Kohane, Isaac S.
通讯作者：
Kohane, Isaac S.
影响因子：
11.1
作者：
Deer RR;Rock MA;Vasilevsky N;Carmody L;Rando H;Anzalone AJ;Basson MD;Bennett TD;Bergquist T;Boudreau EA;Bramante CT;Byrd JB;Callahan TJ;Chan LE;Chu H;Chute CG;Coleman BD;Davis HE;Gagnier J;Greene CS;Hillegass WB;Kavuluru R;Kimble WD;Koraishy FM;Köhler S;Liang C;Liu F;Liu H;Madhira V;Madlock-Brown CR;Matentzoglu N;Mazzotti DR;McMurry JA;McNair DS;Moffitt RA;Monteith TS;Parker AM;Perry MA;Pfaff E;Reese JT;Saltz J;Schuff RA;Solomonides AE;Solway J;Spratt H;Stein GS;Sule AA;Topaloglu U;Vavougios GD;Wang L;Haendel MA;Robinson PN
通讯作者：
Robinson PN
DOI：
10.3399/bjgp.2021.0265
发表时间：
2021-11
期刊：
The British journal of general practice : the journal of the Royal College of General Practitioners
影响因子：
--
作者：
Nurek M;Rayner C;Freyer A;Taylor S;Järte L;MacDermott N;Delaney BC;Delphi panellists
通讯作者：
Delphi panellists