An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study

Kang-Cheng Su,Diahn-Warng Perng,Kun-Ta Chou,Yu Ru Kou,Hsin-Kuo Ko,Vincent Yi-Fong Su,Yi-Han Hsiao

doi:10.1038/s41533-019-0135-9

Abstract

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.

Highlights

Chronic obstructive pulmonary disease (COPD) is a key cause of morbidity and mortality worldwide.[1,2] the disease has been considerably underdiagnosed.[3]
A high proportion of underdiagnosis occurs in primary care settings.[5,6,7]
Patients with clinically significant COPD may benefit from available treatments.[11,12]

Summary

Introduction

Chronic obstructive pulmonary disease (COPD) is a key cause of morbidity and mortality worldwide.[1,2] the disease has been considerably underdiagnosed.[3]. A high proportion of underdiagnosis occurs in primary care settings.[5,6,7] Underuse or unavailability of spirometry is the most common cause of underdiagnosis in primary care settings.[4,8,9] In Taiwan, a recent nationwide telephone interview survey of the general population for COPD prevalence revealed that up to 6.1% might have COPD, but less than 2% had undergone spirometry examination.[10] an effective COPD case-finding strategy other than spirometry is urgently required. Among the identified cases of COPD, symptomatic COPD cases with more severe airflow limitations have been termed as “clinically significant COPD”. Patients with clinically significant COPD may benefit from available treatments.[11,12] The U.S National Heart, Lung, and Blood

Objectives

Findings

Methods