Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets.
Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets.
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DOI：
10.1038/s41598-023-47488-9
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发表时间：
2023-12-07
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
从 COVID-19 大流行的最初几个月起，病例严重程度和症状范围的变化就很明显。从临床角度来看，症状变异可能表明感染导致疾病的多种途径/机制，不同的途径可能需要不同的治疗方法。为了公共卫生和控制传播，社区病例的症状是采取 PCR 检测和隔离等行动的提示。然而，解释症状面临着挑战，例如，在平衡个体症状的敏感性和特异性与最大限度地发现病例的需要之间，同时管理对有限资源（例如测试）的需求。出于临床和传播控制的原因，我们需要一种允许不同症状表型的可能性的方法，而不是假设沿单一维度的变异性。在这里，我们通过汇集来自英国常规测试、人口代表性家庭调查和参与式智能手机监控的四个大型且多样化的数据集来解决这个问题。通过使用来自统计学和机器学习的尖端无监督分类技术，我们描述了有症状的 SARS-CoV-2 PCR 阳性社区病例的症状表型。在使用允许我们比较多个数据集的方法之前，我们首先跨年龄段单独分析每个数据集。虽然我们根据病例所经历的症状总数观察到症状的分离，但我们也看到症状分为胃肠道、呼吸道和其他类型，以及在极端年龄时不同的症状共现模式。通过这种方式，我们能够展示 COVID-19 症状的深层结构，而不会因研究设计而出现通常的偏差。预计这将对社区 SARS-CoV-2 病例的识别和管理产生影响，并可进一步应用于其他疾病和综合症的基于症状的管理。
Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.
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DOI：
10.1016/s1473-3099(21)00460-6
发表时间：
2022-01
期刊：
The Lancet. Infectious diseases
影响因子：
--
作者：
Antonelli M;Penfold RS;Merino J;Sudre CH;Molteni E;Berry S;Canas LS;Graham MS;Klaser K;Modat M;Murray B;Kerfoot E;Chen L;Deng J;Österdahl MF;Cheetham NJ;Drew DA;Nguyen LH;Pujol JC;Hu C;Selvachandran S;Polidori L;May A;Wolf J;Chan AT;Hammers A;Duncan EL;Spector TD;Ourselin S;Steves CJ
通讯作者：
Steves CJ
影响因子：
9
作者：
Geifman, Nophar;Kennedy, Richard E.;Brinton, Roberta Diaz
通讯作者：
Brinton, Roberta Diaz
影响因子：
13.6
作者：
Sudre CH;Lee KA;Lochlainn MN;Varsavsky T;Murray B;Graham MS;Menni C;Modat M;Bowyer RCE;Nguyen LH;Drew DA;Joshi AD;Ma W;Guo CG;Lo CH;Ganesh S;Buwe A;Pujol JC;du Cadet JL;Visconti A;Freidin MB;El-Sayed Moustafa JS;Falchi M;Davies R;Gomez MF;Fall T;Cardoso MJ;Wolf J;Franks PW;Chan AT;Spector TD;Steves CJ;Ourselin S
通讯作者：
Ourselin S
影响因子：
1.6
作者：
Landgraf, Andrew J.;Lee, Yoonkyung
通讯作者：
Lee, Yoonkyung
基于移动应用程序的双相情感障碍评估和监测自述问卷： 系统性综述。
影响因子：
2.2
作者：
Chan EC;Sun Y;Aitchison KJ;Sivapalan S
通讯作者：
Sivapalan S