Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records
Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records
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DOI：
10.1186/s12911-019-0805-0
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发表时间：
2019-04-18
影响因子：
3.5
通讯作者：
Denaxas, Spiros
中科院分区：
文献类型：
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作者：
Pikoula, Maria;Quint, Jennifer Kathleen;Denaxas, Spiros
COPD是一种高度异质性疾病，由不同的表型组成，具有不同的病因和预后特征，目前的分类系统不能完全捕获这种异质性。在这项研究中，我们试图发现，描述和验证COPD亚型使用聚类分析的数据来自电子健康records.MethodsWe应用两个无监督的学习算法（k-均值和层次聚类）在30,961当前和以前的吸烟者诊断为COPD，使用链接的国家结构化的电子健康记录在英格兰可通过CALIBER资源。我们使用了15个临床特征，包括危险因素和合并症，并使用多重对应分析进行降维。我们比较了群组成员资格与COPD急性加重以及呼吸道和心血管死亡之间的相关性，在146，466人-年的随访中记录了10，736例死亡。我们还实施和测试了一个过程，分配看不见的患者到集群使用决策树classification.ResultsWe确定和特点5 COPD患者集群与人口统计学，合并症，死亡和恶化的风险方面的独特的患者特征。这四个亚组与1）焦虑/抑郁; 2）严重气流阻塞和虚弱; 3）心血管疾病和糖尿病以及4）肥胖/特应性相关。第五集群与低患病率的最comorbid conditions.ConclusionsCOPD患者可以细分为不同的危险因素，合并症和预后的组，包括在他们的初级保健记录的数据的基础上。所确定的聚类证实了先前聚类研究的结果，并提请注意焦虑和抑郁是年轻女性患者疾病的重要驱动因素。
BackgroundCOPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records.MethodsWe applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier.ResultsWe identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions.ConclusionsCOPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients.