Department of Pulmonary Diseases and Tuberculosis, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, 779 00 Olomouc, Czech Republic.
Department of Respiratory Medicine, University Hospital, 625 00 Brno, Czech Republic.
Viruses. 2022 Oct 31;14(11):2422. doi: 10.3390/v14112422.
Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.
分析复杂数据集，同时保持临床医生和患者对结果的可解释性和可理解性，这不仅在病毒感染中是具有挑战性的。这些数据集通常包含各种异质的临床、人口统计学、实验室和个人数据，导致患者特征和宿主反应的因素不是单一的，而是多种因素的组合。因此，需要采用多变量方法来分析这些复杂的患者数据集，而这些数据集是无法通过单变量比较（例如，一个免疫细胞亚群与一个临床因素）进行分析的。以 SARS-CoV-2 感染为例，我们采用患者相似性网络（PSN）方法来评估宿主免疫因素与感染临床过程之间的关系，并进行可视化和数据分析。对 250 名经 PCR 确认的 SARS-CoV-2 感染后 4 至 8 周采集的患者的约 85 个免疫（细胞和体液）和约 70 个临床因素进行 PSN 分析，确定了一个最小的免疫特征，以及与疾病严重程度强烈相关的临床和实验室因素。我们的研究表明，实施多变量网络方法来识别 SARS-CoV-2 感染中的相关因素并可视化它们之间的关系具有优势，但该模型通常适用于任何复杂数据集。
