Underuse or unavailability of spirometry is one of the most important factors causing underdiagnosis of COPD. We reported the development of a COPD prediction model to identify at-risk, undiagnosed COPD patients when spirometry was unavailable. This cross-sectional study enrolled subjects aged ≥40 years with respiratory symptoms and a smoking history (≥20 pack-years) in a medical center in two separate periods (development and validation cohorts). All subjects completed COPD assessment test (CAT), peak expiratory flow rate (PEFR) measurement, and confirmatory spirometry. A binary logistic model with calibration (Hosmer-Lemeshow test) and discrimination (area under receiver operating characteristic curve [AUROC]) was implemented. Three hundred and one subjects (development cohort) completed the study, including non-COPD (154, 51.2%) and COPD cases (147; stage I, 27.2%; II, 55.8%; III-IV, 17%). Compared with non-COPD and GOLD I cases, GOLD II-IV patients exhibited significantly higher CAT scores and lower lung function, and were considered clinically significant for COPD. Four independent variables (age, smoking pack-years, CAT score, and percent predicted PEFR) were incorporated developing the prediction model, which estimated the COPD probability (PCOPD). This model demonstrated favorable discrimination (AUROC: 0.866/0.828; 95% CI 0.825-0.906/0.751-0.904) and calibration (Hosmer-Lemeshow P = 0.332/0.668) for the development and validation cohorts, respectively. Bootstrap validation with 1000 replicates yielded an AUROC of 0.866 (95% CI 0.821-0.905). A PCOPD of ≥0.65 identified COPD patients with high specificity (90%) and a large proportion (91.4%) of patients with clinically significant COPD (development cohort). Our prediction model can help physicians effectively identify at-risk, undiagnosed COPD patients for further diagnostic evaluation and timely treatment when spirometry is unavailable.

中文翻译：

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一种准确的预测模型，可识别出未诊断出的COPD高危患者：一项横断面病例调查研究。

肺活量测定仪的使用不足或不可用是导致COPD诊断不足的最重要因素之一。我们报告了COPD预测模型的开发，该模型可在无法使用肺活量测定法时识别高危，未被诊断的COPD患者。这项横断面研究在两个单独的时期（发育和验证队列）在医疗中心招募了≥40岁且患有呼吸道症状和吸烟史（≥20包年）的受试者。所有受试者均完成了COPD评估测试（CAT），呼气峰值流速（PEFR）测量和确定性肺活量测定。实现了具有校准（Hosmer-Lemeshow检验）和辨别力（接收器工作特性曲线[AUROC]下的面积）的二元逻辑模型。301名受试者（发展队列）完成了该研究，其中包括非COPD（154，51。2％）和COPD病例（147；第一阶段，占27.2％；第二阶段，占55.8％；第三至第四阶段，占17％）。与非COPD和GOLD I病例相比，GOLD II-IV患者表现出明显更高的CAT评分和更低的肺功能，并且被认为对COPD具有临床意义。纳入四个独立变量（年龄，吸烟包装年数，CAT分数和预测的PEFR百分比）以建立预测模型，从而估计出COPD概率（PCOPD）。该模型分别针对开发和验证队列显示了良好的区分度（AUROC：0.866 / 0.828; 95％CI 0.825-0.906 / 0.751-0.904）和校准（Hosmer-Lemeshow P = 0.332 / 0.668）。使用1000个重复进行的Bootstrap验证得出的AUROC为0.866（95％CI 0.821-0.905）。PCOPD≥0.65可以识别出高特异性（90％）和高比例（91。4％的患者具有临床上显着的COPD（发育队列）。我们的预测模型可以帮助医生有效地识别高危，未诊断的COPD患者，以便在无法使用肺活量测定法时进行进一步的诊断评估和及时治疗。