英国长新冠的症状群：基于前瞻性社区队列的无监督机器学习研究
Symptom clusters in Long COVID in the UK: prospective community-based cohort study using unsupervised machine learning
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中文总结 AI 辅助
本研究基于英国ONS的CIS数据，用无监督机器学习分析长新冠症状，明确其症状群、核心症状及随病程的演变特征，发现早期长新冠可能存在三种表型。
中文摘要 AI 辅助
长新冠是一种通常定义为感染SARS-CoV-2病毒后，急性感染期后仍持续存在症状的疾病。该疾病对医疗系统、经济以及患者个体都有重大影响。由于长新冠的症状多样且广泛，症状共现追踪可用于捕捉患者体验，提升对疾病的理解、诊断与管理。本研究利用英国国家统计局（ONS）的COVID-19感染调查（CIS），该调查于2020年4月至2023年3月开展。2021年2月3日，ONS在CIS中新增一项问题，评估自报新冠感染后23项长新冠症状的自报持续情况。研究基于每项症状存在与否的二元调查应答，生成雅卡尔（Jaccard）症状-症状距离矩阵，随后采用三种方法可视化症状共现：热图、非度量多维标度、以及采用完全链接的凝聚层次聚类。研究将分析分为两部分：首先分析所有长新冠调查应答（n=207319），再按长新冠发病后时间分层分析（最长24个月，其中发病后0个月的应答为n=30224）。研究发现，病程延长的长新冠患者症状共现程度更高；还发现神经/全身症状、胃肠道症状、呼吸道症状的症状群，且随着长新冠发病后时间增加，各器官系统间症状共现的可分性逐渐降低；气短、虚弱/疲劳、肌肉疼痛是长新冠的核心症状。研究表明，长新冠的症状体验从早期到晚期会发生演变，表现为更高的症状负担与多系统表现；还发现早期长新冠可能存在三种表型。
英文摘要
Long COVID is a condition usually defined by persisting symptoms following infection by the SARS-CoV-2 virus beyond the acute phase of infection. The condition has a significant impact on healthcare systems, the economy, and the individuals living with it. Due to the diverse and extensive symptomatology of long COVID, symptom co-occurrence tracking can be used to capture patient experiences and improve understanding, diagnosis, and management. Here, we leverage the UK Office for National Statistics (ONS) COVID-19 Infection Survey (CIS). The CIS was run between April 2020 and March 2023. On February 3, 2021, the ONS launched a new CIS question evaluating self-reported symptom persistence of 23 long COVID symptoms post self-reported COVID-19 infection. We use Jaccard symptom-by symptom distance matrices, derived from the binary survey responses of the presence of each symptom. Three methods are then used to visualise symptom co-occurrence: heatmaps, non-metric multidimensional scaling, and agglomerative hierarchical clustering with complete linkage. We split our analysis into two parts, first looking at all long COVID survey responses (n = 207,319) and then looking at responses stratified by time-since-onset of long COVID up to 24 months (n = 30,224 at zero months-since-onset). We find higher symptom co-occurrence in prolonged long COVID. We also find clusters of neurological/systemic symptoms, gastrointestinal symptoms, and respiratory symptoms, with separability in symptom co-occurrence by organ system becoming less pronounced as time-since-onset of long COVID increases. Shortness of breath, weakness/tiredness, and muscle ache present as core symptoms long COVID. We demonstrate that the symptom experience of long COVID evolves from early stages to late stages with higher symptom burden and multi-systemic presentation. We also find three possible phenotypes of early long COVID.
发表机构
- University of Manchester（曼彻斯特大学）
- The University of Sheffield（谢菲尔德大学）
机构由 AI 辅助整理，请以论文原文为准。
