Pneumology and Thoracic Surgery Service, University Hospital Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain.
University Institute of Tropical Diseases and Public Health of the Canary Islands, University of La Laguna, Santa Cruz de Tenerife, Spain.
Clin Respir J. 2022 Jul;16(7):504-512. doi: 10.1111/crj.13518. Epub 2022 Jun 22.
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition, in which taking into consideration clinical phenotypes and multimorbidity is relevant to disease management. Network analysis, a procedure designed to study complex systems, allows to represent connections between the distinct features found in COPD.
Network analysis was applied to a cohort of patients with COPD in order to explore the degree of connectivity between different diseases, taking into account the presence of two phenotypic traits commonly used to categorize patients in clinical practice: chronic bronchitis (CB /CB ) and the history of previous severe exacerbations (Ex /Ex ). The strength of association between diseases was quantified using the correlation coefficient Phi (ɸ).
A total of 1726 patients were included, and 91 possible links between 14 diseases were established. Although the four phenotypically defined groups presented a similar underlying comorbidity pattern, with special relevance for cardiovascular diseases and/or risk factors, classifying patients according to the presence or absence of CB implied differences between groups in network density (mean ɸ: 0.098 in the CB group and 0.050 in the CB group). In contrast, between-group differences in network density were small and of questionable significance when classifying patients according to prior exacerbation history (mean ɸ: 0.082 among Ex subjects and 0.072 in the Ex group). The degree of connectivity of any given disease with the rest of the network also varied depending on the selected phenotypic trait. The classification of patients according to the CB /CB groups revealed significant differences between groups in the degree of conectivity between comorbidities. On the other side, grouping the patients according to the Ex /Ex trait did not disclose differences in connectivity between network nodes (diseases).
The multimorbidity network of a patient with COPD differs according to the underlying clinical characteristics, suggesting that the connections linking comorbidities between them vary for different phenotypes and that the clinical heterogeneity of COPD could influence the expression of latent multimorbidity. Network analysis has the potential to delve into the interactions between COPD clinical traits and comorbidities and is a promising tool to investigate possible specific biological pathways that modulate multimorbidity patterns.
慢性阻塞性肺疾病（COPD）是一种复杂且异质的疾病，考虑到临床表型和多种合并症与疾病管理相关。网络分析是一种旨在研究复杂系统的方法，可用于表示 COPD 中不同特征之间的连接。
对 COPD 患者队列进行网络分析，以探索不同疾病之间的连通程度，同时考虑到临床上常用于对患者进行分类的两种表型特征：慢性支气管炎（CB / CB）和既往严重加重史（Ex / Ex）。使用相关系数 Phi（ɸ）量化疾病之间的关联强度。
共纳入 1726 例患者，建立了 14 种疾病之间 91 种可能的联系。尽管根据表型定义的四个组具有相似的潜在合并症模式，特别是心血管疾病和/或危险因素的相关性较大，但根据 CB 的有无对患者进行分类意味着组间网络密度存在差异（存在 CB 组的平均 ɸ为 0.098，不存在 CB 组的平均 ɸ为 0.050）。相反，根据既往加重史对患者进行分类时，组间网络密度差异较小，且无统计学意义（Ex 组的平均 ɸ为 0.082，Ex 组的平均 ɸ为 0.072）。任何给定疾病与网络其余部分的连接程度也因所选表型特征而异。根据 CB / CB 组对患者进行分类，发现各组之间合并症之间的连通程度存在显著差异。另一方面，根据 Ex / Ex 特征对患者进行分组并未揭示网络节点（疾病）之间的连接差异。
COPD 患者的合并症网络根据潜在的临床特征而有所不同，这表明它们之间的合并症连接因不同的表型而不同，COPD 的临床异质性可能会影响潜在合并症的表达。网络分析有可能深入研究 COPD 临床特征与合并症之间的相互作用，是研究可能调节合并症模式的特定生物学途径的有前途的工具。
