Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data.
Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data.
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DOI：
10.1136/bmjopen-2021-059385
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发表时间：
2022-07-06
期刊：
影响因子：
2.9
通讯作者：
Sheikh A
中科院分区：
文献类型：
--
作者：
Daines L;Mulholland RH;Vasileiou E;Hammersley V;Weatherill D;Katikireddi SV;Kerr S;Moore E;Pesenti E;Quint JK;Shah SA;Shi T;Simpson CR;Robertson C;Sheikh A
COVID-19通常是一种急性疾病，但有些人继续出现持续数周或数月的症状（通常称为“长期COVID”）。目前尚不清楚哪些患者发展为长期COVID的风险最高。在本方案中，我们描述了开发预测模型的计划，以识别有长期COVID风险的个体。我们将使用国家2019冠状病毒病早期大流行评估和加强监测（EAVE II）平台，这是一个与苏格兰540万人的常规电子医疗数据相关联的人口水平数据集。我们将通过识别与非工作时间全科医生就诊、事故和急诊就诊、住院、门诊就诊、药物处方/分发和死亡率相关的初级保健数据模式，确定长期COVID的潜在指标。我们将通过对SARS-CoV-2感染的逆转录酶PCR（RT-PCR）检测阳性的人和两个对照组进行匹配分析来调查长期COVID的潜在指标：（1）至少有一次阴性RT-PCR检测且从未检测出阳性的个人;（2）苏格兰的一般人群（所有未检测出阳性的人）。然后将使用聚类分析来确定长期COVID结果指标的最终定义。然后，我们将推导、内部和外部验证一个预测模型，以确定与长期COVID相关的流行病学风险因素。EAVE II研究已获得研究伦理委员会（参考编号：12/SS/0201）和健康和社会护理公共利益和隐私小组（参考编号：1920-0279）的批准。研究结果将发表在同行评审的期刊上，并在会议上发表。了解长期COVID的预测因素并确定持续症状风险最大的患者群体将为长期COVID的未来治疗和预防策略提供信息。
COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as ‘long-COVID’). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID. We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups: (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID. The EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID.
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DOI：
10.1016/s2213-2600(21)00383-0
发表时间：
2021-11
期刊：
The Lancet. Respiratory medicine
影响因子：
--
作者：
Evans RA;McAuley H;Harrison EM;Shikotra A;Singapuri A;Sereno M;Elneima O;Docherty AB;Lone NI;Leavy OC;Daines L;Baillie JK;Brown JS;Chalder T;De Soyza A;Diar Bakerly N;Easom N;Geddes JR;Greening NJ;Hart N;Heaney LG;Heller S;Howard L;Hurst JR;Jacob J;Jenkins RG;Jolley C;Kerr S;Kon OM;Lewis K;Lord JM;McCann GP;Neubauer S;Openshaw PJM;Parekh D;Pfeffer P;Rahman NM;Raman B;Richardson M;Rowland M;Semple MG;Shah AM;Singh SJ;Sheikh A;Thomas D;Toshner M;Chalmers JD;Ho LP;Horsley A;Marks M;Poinasamy K;Wain LV;Brightling CE;PHOSP-COVID Collaborative Group
通讯作者：
PHOSP-COVID Collaborative Group
影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
1.9
作者：
GOWER, JC
通讯作者：
GOWER, JC
DOI：
10.1136/bmj.m3731
发表时间：
2020-10-20
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Clift AK;Coupland CAC;Keogh RH;Diaz-Ordaz K;Williamson E;Harrison EM;Hayward A;Hemingway H;Horby P;Mehta N;Benger J;Khunti K;Spiegelhalter D;Sheikh A;Valabhji J;Lyons RA;Robson J;Semple MG;Kee F;Johnson P;Jebb S;Williams T;Hippisley-Cox J
通讯作者：
Hippisley-Cox J
影响因子：
7.7
作者：
Mulholland, Rachel H.;Vasileiou, Eleftheria;Sheikh, Aziz
通讯作者：
Sheikh, Aziz