Use of clinical characteristics to predict spirometric classification of obstructive lung disease

• Published in: International journal of chronic obstructive pulmonary disease Vol. 13; pp. 889 - 902
• Main Authors: Pascoe, Steven J, Wu, Wei, Collison, Kathryn A, Nelsen, Linda M, Wurst, Keele E, Lee, Laurie A
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2018; Dove Medical Press Ltd; Dove Medical Press
• Subjects: Adult; Age differences; Aged; Algorithms; Area Under Curve; Asthma; Asthma - classification; Asthma - diagnosis; Asthma - physiopathology; Asthma-COPD overlap syndrome; Chronic obstructive lung disease; Chronic obstructive pulmonary disease; Classification; Clinical trials; COPD; Demographics; Diagnosis; Diagnosis, Differential; differential diagnosis; Discriminant Analysis; Female; Forced Expiratory Volume; Health Status; Humans; Least-Squares Analysis; Logistic Models; Lung - physiopathology; Lung diseases; Lung volume measurement; Male; Medical research; Middle Aged; Multivariate Analysis; Odds Ratio; Original Research; Patients; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive - classification; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - physiopathology; Questionnaires; Regression analysis; Reproducibility of Results; Respiratory tract diseases; ROC Curve; Severity of Illness Index; Spirometry; Surveys and Questionnaires; Syndrome; Variables; Vital Capacity; differential diagnosis; asthma; asthma–COPD overlap syndrome; surveys and questionnaires; COPD
• ISSN: 1178-2005; 1176-9106; 1178-2005
• DOI: 10.2147/COPD.S153426

There is no consensus on how to define patients with symptoms of asthma and chronic obstructive pulmonary disease (COPD). A diagnosis of asthma-COPD overlap (ACO) syndrome has been proposed, but its value is debated. This study (GSK Study 201703 [NCT02302417]) investigated the ability of statistical modeling approaches to define distinct disease groups in patients with obstructive lung disease (OLD) using medical history and spirometric data. Patients aged ≥18 years with diagnoses of asthma and/or COPD were categorized into three groups: 1) asthma (nonobstructive; reversible), 2) ACO (obstructive; reversible), and 3) COPD (obstructive; nonreversible). Obstruction was defined as a post-bronchodilator forced expiratory volume in 1 second (FEV )/forced vital capacity <0.7, and reversibility as a post-albuterol increase in FEV ≥200 mL and ≥12%. A primary model (PM), based on patients' responses to a health care practitioner-administered questionnaire, was developed using multinomial logistic regression modeling. Other multivariate statistical analysis models for identifying asthma and COPD as distinct entities were developed and assessed using receiver operating characteristic (ROC) analysis. Partial least squares discriminant analysis (PLS-DA) assessed the degree of overlap between groups. The PM predicted spirometric classifications with modest sensitivity. Other analysis models performed with high discrimination (area under the ROC curve: asthma model, 0.94; COPD model, 0.87). PLS-DA identified distinct phenotypic groups corresponding to asthma and COPD. Within the OLD spectrum, patients with asthma or COPD can be identified as two distinct groups with a high degree of precision. Patients outside these classifications do not constitute a homogeneous group.