Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics
Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics
复制标题
无监督学习技术可识别具有不同临床特征的支气管扩张表型
DOI：
10.5588/ijtld.15.0500
复制
发表时间：
2016-03-01
影响因子：
4
通讯作者：
Zhong, N-S.
中科院分区：
文献类型：
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作者：
Guan, W-J.;Jiang, M.;Zhong, N-S.
背景：无监督学习技术使研究人员能够识别具有复杂表现的疾病的不同表型。目的：鉴别支气管扩张症的表型，探讨其临床表现和预后。方法：我们进行了分层聚类分析，以确定最能区分支气管扩张临床特征的聚类。比较患者的人口统计学、肺功能、痰细菌学、病因学、放射学、疾病严重程度、生活质量、咳嗽量表和辣椒素敏感性、运动耐量、医疗保健使用情况和恶化频率。结果：对148例成人稳定型支气管扩张患者的数据进行了分析。确定了四个集群。第1组（n = 69）包括以轻度和特发性支气管扩张为主的年龄最小的患者，医疗资源使用较少。以感染后支气管扩张为主的第2组患者（n = 22）症状持续时间最长，疾病严重程度更高，肺功能较差，气道铜绿假单胞菌定植，频繁使用医疗资源。第3组（n = 16）为症状持续时间较短的老年患者，以特发性支气管扩张为主，以重度支气管扩张为主。第4组（n = 41）为大多数疾病严重程度中等的老年患者。第2组和第3组发生支气管扩张加重的风险高于第1组和第4组（P = 0.06）。结论：鉴别不同的表型将有助于更深入地了解支气管扩张的特征和预后。
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.OBJECTIVES: To identify bronchiectasis phenotypes and characterise their clinical manifestations and prognosis.METHODS: We conducted hierarchical cluster analysis to identify clusters that best distinguished clinical characteristics of bronchiectasis. Demographics, lung function, sputum bacteriology, aetiology, radiology, disease severity, quality-of-life, cough scale and capsaicin sensitivity, exercise tolerance, health care use and frequency of exacerbations were compared.RESULTS: Data from 148 adults with stable bronchiectasis were analysed. Four clusters were identified. Cluster 1 (n = 69) consisted of the youngest patients with predominantly mild and idiopathic bronchiectasis with minor health care resource use. Patients in cluster 2 (n = 22), in which post-infectious bronchiectasis predominated, had the longest duration of symptoms, greater disease severity, poorer lung function, airway Pseudomonas aeruginosa colonisation and frequent health care resource use. Cluster 3 (n = 16) consisted of elderly patients with shorter duration of symptoms and mostly idiopathic bronchiectasis, and predominantly severe bronchiectasis. Cluster 4 (n = 41) constituted the most elderly patients with moderate disease severity. Clusters 2 and 3 tended to have a greater risk of bronchiectasis exacerbations (P = 0.06) than clusters 1 and 4.CONCLUSION: Identification of distinct phenotypes will lead to greater insight into the characteristics and prognosis of bronchiectasis.