Wang Xuejie, Villa Carmen, Dobarganes Yadira, Olveira Casilda, Girón Rosa, García-Clemente Marta, Máiz Luis, Sibila Oriol, Golpe Rafael, Menéndez Rosario, Rodríguez-López Juan, Prados Concepción, Martinez-García Miguel Angel, Rodriguez Juan Luis, de la Rosa David, Duran Xavier, Garcia-Ojalvo Jordi, Barreiro Esther
Lung Cancer and Muscle Research Group, Pulmonology Department, Hospital del Mar-IMIM, Parc de Salut Mar, PRBB, C/Dr. Aiguader, 88, 08003 Barcelona, Spain.
Department of Medicine, Universitat Autònoma de Barcelona (UAB), 08035 Barcelona, Spain.
Biomedicines. 2022 Jan 21;10(2):225. doi: 10.3390/biomedicines10020225.
Differential phenotypic characteristics using data mining approaches were defined in a large cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON). Three differential phenotypic clusters (hierarchical clustering, scikit-learn library for Python, and agglomerative methods) according to systemic biomarkers: neutrophil, eosinophil, and lymphocyte counts, C reactive protein, and hemoglobin were obtained in a patient large-cohort ( = 1092). Clusters #1-3 were named as mild, moderate, and severe on the basis of disease severity scores. Patients in cluster #3 were significantly more severe (FEV, age, colonization, extension, dyspnea (FACED), exacerbation (EFACED), and bronchiectasis severity index (BSI) scores) than patients in clusters #1 and #2. Exacerbation and hospitalization numbers, Charlson index, and blood inflammatory markers were significantly greater in cluster #3 than in clusters #1 and #2. Chronic colonization by and COPD prevalence were higher in cluster # 3 than in cluster #1. Airflow limitation and diffusion capacity were reduced in cluster #3 compared to clusters #1 and #2. Multivariate ordinal logistic regression analysis further confirmed these results. Similar results were obtained after excluding COPD patients. Clustering analysis offers a powerful tool to better characterize patients with bronchiectasis. These results have clinical implications in the management of the complexity and heterogeneity of bronchiectasis patients.
利用数据挖掘方法，在来自西班牙在线支气管扩张症登记处（RIBRON）的一大群患者中定义了不同的表型特征。根据全身生物标志物：中性粒细胞、嗜酸性粒细胞和淋巴细胞计数、C反应蛋白和血红蛋白，在一个大的患者队列（n = 1092）中获得了三个不同的表型簇（层次聚类、Python的scikit-learn库和凝聚方法）。根据疾病严重程度评分，将第1-3簇分别命名为轻度、中度和重度。第3簇中的患者比第1和第2簇中的患者病情明显更严重（第一秒用力呼气容积、年龄、定植、病变范围、呼吸困难（FACED）、急性加重（EFACED）和支气管扩张严重指数（BSI）评分）。第3簇中的急性加重和住院次数、查尔森指数和血液炎症标志物明显高于第1和第2簇。第3簇中铜绿假单胞菌的慢性定植率和慢性阻塞性肺疾病（COPD）患病率高于第1簇。与第1和第2簇相比，第3簇中的气流受限和弥散能力降低。多变量有序逻辑回归分析进一步证实了这些结果。排除COPD患者后也获得了类似的结果。聚类分析为更好地表征支气管扩张症患者提供了一个强大的工具。这些结果对支气管扩张症患者复杂性和异质性的管理具有临床意义。
