Table 1_Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19.docx

BackgroundDisease presentation and progression can vary greatly in heterogeneous diseases, such as COVID-19, with variability in patient outcomes, even within the hospital setting. This variability underscores the need for tailored treatment approaches based on distinct clinical subgroups.
ObjectivesThis study aimed to identify COVID-19 patient subgroups with unique clinical characteristics using real-world data (RWD) from electronic health records (EHRs) to inform individualized treatment plans.
Materials and methodsA Factor Analysis of Mixed Data (FAMD)-based agglomerative hierarchical clustering approach was employed to analyze the real-world data, enabling the identification of distinct patient subgroups. Statistical tests evaluated cluster differences, and machine learning models classified the identified subgroups.
ResultsThree clusters of COVID-19 in patients with unique clinical characteristics were identified. The analysis revealed significant differences in hospital stay durations and survival rates among the clusters, with more severe clinical features correlating with worse prognoses and machine learning classifiers achieving high accuracy in subgroup identification.
ConclusionBy leveraging RWD and advanced clustering techniques, the study provides insights into the heterogeneity of COVID-19 presentations. The findings support the development of classification models that can inform more individualized and effective treatment plans, improving patient outcomes in the future.

背景：异质性疾病（如新型冠状病毒肺炎（COVID-19））的临床表现与疾病进程差异显著，即便在住院场景中，患者的临床结局仍存在较大波动。这种异质性凸显了针对不同临床亚组制定个体化治疗方案的必要性。研究目的：本研究旨在利用电子健康档案（electronic health records, EHRs）中的真实世界数据（real-world data, RWD），识别具有独特临床特征的新型冠状病毒肺炎（COVID-19）患者亚组，为个体化治疗方案的制定提供参考依据。材料与方法：本研究采用基于混合数据因子分析（Factor Analysis of Mixed Data, FAMD）的凝聚层次聚类法对真实世界数据进行分析，以区分不同的患者亚组。通过统计学检验评估各聚类簇间的差异，并借助机器学习模型对已识别的亚组进行分类。研究结果：本研究共识别出3个具有独特临床特征的新型冠状病毒肺炎患者聚类簇。分析结果显示，各聚类簇的住院时长与生存率存在显著差异，临床特征越严重的患者预后越差；同时，机器学习分类器在亚组识别任务中取得了较高的分类准确率。研究结论：本研究通过结合真实世界数据与先进的聚类技术，揭示了新型冠状病毒肺炎临床表现的异质性。研究结果支持开发能够指导更个体化、高效治疗方案的分类模型，未来有望改善患者的临床结局。