Division of General Internal Medicine, Department of Medicine, Weill Cornell Medicine, 420 E 70th street LH 348, New York, NY, USA.
Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA.
BMC Public Health. 2024 Jul 25;24(1):1994. doi: 10.1186/s12889-024-19379-9.
Recent studies have demonstrated that individuals hospitalized due to COVID-19 can be affected by "long-COVID" symptoms for as long as one year after discharge.
Our study objective is to identify data-driven clusters of patients using a novel, unsupervised machine learning technique.
The study uses data from 437 patients hospitalized in New York City between March 3rd and May 15th of 2020. The data used was abstracted from medical records and collected from a follow-up survey for up to one-year post-hospitalization. Hospitalization data included demographics, comorbidities, and in-hospital complications. The survey collected long-COVID symptoms, and information on general health, social isolation, and loneliness. To perform the analysis, we created a graph by projecting the data onto eight principal components (PCs) and running the K-nearest neighbors algorithm. We then used Louvain's algorithm to partition this graph into non-overlapping clusters.
The cluster analysis produced four clusters with distinct health and social connectivity patterns. The first cluster (n = 141) consisted of patients with both long-COVID neurological symptoms (74%) and social isolation/loneliness. The second cluster (n = 137) consisted of healthy patients who were also more socially connected and not lonely. The third cluster (n = 96) contained patients with neurological symptoms who were socially connected but lonely, and the fourth cluster (n = 63) consisted entirely of patients who had traumatic COVID hospitalization, were intubated, suffered symptoms, but were socially connected and experienced recovery.
The cluster analysis identified social isolation and loneliness as important features associated with long-COVID symptoms and recovery after hospitalization. It also confirms that social isolation and loneliness, though connected, are not necessarily the same. Physicians need to be aware of how social characteristics relate to long-COVID and patient's ability to cope with the resulting symptoms.
最近的研究表明，因 COVID-19 住院的患者在出院后长达一年的时间里可能会受到“长新冠”症状的影响。
我们的研究目的是使用一种新颖的无监督机器学习技术确定具有相似特征的患者数据集群。
本研究使用了 2020 年 3 月 3 日至 5 月 15 日期间在纽约市住院的 437 名患者的数据。这些数据是从病历中提取出来的，并从住院后长达一年的随访调查中收集而来。住院数据包括人口统计学信息、合并症和院内并发症。调查收集了长新冠症状以及一般健康状况、社会隔离和孤独感的信息。为了进行分析，我们通过将数据投影到八个主成分（PC）上并运行 K-最近邻算法来创建一个图。然后，我们使用 Louvain 算法将该图划分为非重叠的聚类。
聚类分析产生了四个具有不同健康和社交连接模式的聚类。第一个聚类（n=141）包含既有长新冠神经系统症状（74%）又有社会隔离/孤独感的患者。第二个聚类（n=137）包含健康的患者，他们的社交联系更加紧密且不孤独。第三个聚类（n=96）包含有神经系统症状的患者，他们的社交联系紧密但孤独，第四个聚类（n=63）完全由经历过创伤性 COVID 住院、插管、有症状但社交联系紧密并康复的患者组成。
聚类分析确定了社会隔离和孤独感是与长新冠症状和住院后康复相关的重要特征。它还证实了社会隔离和孤独感虽然相关，但并不一定相同。医生需要意识到社会特征与长新冠以及患者应对相关症状的能力之间的关系。
