Metting Esther I, In 't Veen Johannes C C M, Dekhuijzen P N Richard, van Heijst Ellen, Kocks Janwillem W H, Muilwijk-Kroes Jacqueline B, Chavannes Niels H, van der Molen Thys
Dept of General Practice, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; GRIAC Research Institute, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Sint Franciscus Gasthuis, Rotterdam, The Netherlands.
ERJ Open Res. 2016 Jan 22;2(1). doi: 10.1183/23120541.00077-2015. eCollection 2016 Jan.
The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma-COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool.
本研究的目的是开发并探索源自大量真实初级保健人群的决策树的诊断准确性。9297例疑似患有阻塞性肺疾病的初级保健患者（45%为男性，平均年龄53±17岁）的数据来自哮喘/慢性阻塞性肺疾病（COPD）服务机构，在该机构中，患者接受了肺活量测定、哮喘控制问卷、临床COPD问卷、病史数据及用药情况的评估。所有患者均由肺科医生通过互联网进行诊断。采用卡方自动交互检测方法构建决策树。该决策树在另一真实初级保健人群（n = 3215）中进行了外部验证。我们的决策树正确诊断出79%的哮喘患者、85%的COPD患者以及32%的哮喘-COPD重叠综合征（ACOS）患者。外部验证显示出类似的模式（正确诊断率：哮喘78%，COPD 83%，ACOS 24%）。我们的决策树被认为很有前景，因为它基于有专科医生诊断的真实初级保健患者。在大多数患者中，诊断能够被正确预测。然而，预测ACOS仍然是一项挑战。完整的决策树可应用于针对个体患者的计算机辅助诊断系统。该决策树的简化版本可作为日常临床实践中的案头工具使用。
