目的:症状是管理和控制急性传染病爆发的重要表型,比如COVID-19。尽管症状群和时间序列模式被认为是患者预后的潜在预测因素,仍需要检测与COVID-19患者预后相关的详细亚型及其基于症状表型的进展模式.这项研究旨在调查患者亚型及其进展模式,并具有不同的预后和预后特征。
方法:这项研究包括从湖北省四家医院获得的14,139份纵向电子病历(EMR)。中国,涉及2,683名处于COVID-19大流行早期的个体。开发了一种深度表征学习模型来帮助获取患者的症状概况。使用K-means聚类算法将它们划分为不同的亚型。随后,通过考虑入院和出院时与患者相关的亚型来确定症状进展模式.此外,我们使用Fisher检验来确定每种亚型的重要临床实体.
结果:已经确定了三种表现出特定症状和预后的不同患者亚型。特别是,0亚型占整体的44.2%,以食欲不振为特征,疲劳和睡眠障碍;亚型1包括25.6%的病例,以混乱为特征,咳嗽有痰,包囊和尿失禁;亚型2包括30.2%的病例,以干咳和鼻漏为特征。这三种亚型在预后方面表现出显著差异,死亡率为4.72%,8.59%,分别为0.25%。此外,症状群进展模式显示,亚型0的患者表现为深黄色尿液,胸痛,等。在入院阶段表现出转化为结果较差的更严重亚型的风险升高,而那些出现恶心和呕吐的人倾向于进入温和的亚型。
结论:这项研究提出了一种有临床意义的方法,利用深度表征学习和包含症状表型的真实世界EMR数据来识别COVID-19亚型及其进展模式。该结果可能有助于改善急性传染病的精确分层和管理。
OBJECTIVE: Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction factors for the prognosis of patients, the elaborated subtypes and their progression patterns based on symptom phenotypes related to the prognosis of COVID-19 patients still need be detected. This study aims to investigate patient subtypes and their progression patterns with distinct features of outcome and prognosis.
METHODS: This study included a total of 14,139 longitudinal electronic medical records (EMRs) obtained from four hospitals in Hubei Province, China, involving 2,683 individuals in the early stage of COVID-19 pandemic. A deep representation learning model was developed to help acquire the symptom profiles of patients. K-means clustering algorithm is used to divide them into distinct subtypes. Subsequently, symptom progression patterns were identified by considering the subtypes associated with patients upon admission and discharge. Furthermore, we used Fisher\'s test to identify significant clinical entities for each
subtype.
RESULTS: Three distinct patient subtypes exhibiting specific symptoms and prognosis have been identified. Particularly,
Subtype 0 includes 44.2% of the whole and is characterized by poor appetite, fatigue and sleep disorders;
Subtype 1 includes 25.6% cases and is characterized by confusion, cough with bloody sputum, encopresis and urinary incontinence;
Subtype 2 includes 30.2% cases and is characterized by dry cough and rhinorrhea. These three subtypes demonstrate significant disparities in prognosis, with the mortality rates of 4.72%, 8.59%, and 0.25% respectively. Furthermore, symptom cluster progression patterns showed that patients with
Subtype 0 who manifest dark yellow urine, chest pain, etc. in the admission stage exhibit an elevated risk of transforming into the more severe subtypes with poor outcome, whereas those presenting with nausea and vomiting tend to incline towards entering the milder subtype.
CONCLUSIONS: This study has proposed a clinical meaningful approach by utilizing the deep representation learning and real-world EMR data containing symptom phenotypes to identify the COVID-19 subtypes and their progression patterns. The results would be potentially useful to help improve the precise stratification and management of acute infectious diseases.