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

Authors Pascoe SJ, Wu W, Collison KA, Nelsen LM, Wurst KE, Lee LA

Received 6 October 2017

Accepted for publication 28 December 2017

Published 12 March 2018 Volume 2018:13 Pages 889—902

DOI https://doi.org/10.2147/COPD.S153426

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Charles Downs

Peer reviewer comments 3

Editor who approved publication: Dr Richard Russell


Steven J Pascoe,1 Wei Wu,2,3 Kathryn A Collison,1 Linda M Nelsen,4 Keele E Wurst,5 Laurie A Lee6

1Respiratory Medicines Development Center, GSK, Research Triangle Park, NC, USA; 2Biostatistics, PAREXEL International, Research Triangle Park, NC, USA; 3Clinical Statistics, GSK, Research Triangle Park, NC, USA; 4Value Evidence and Outcomes, GSK, Collegeville, PA, USA; 5Epidemiology, GSK, Collegeville, PA, USA; 6Research and Development, GSK, Stevenage, UK


Background: 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.
Methods: 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 (FEV1)/forced vital capacity <0.7, and reversibility as a post-albuterol increase in FEV1 ≥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.
Results: 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.
Conclusion: 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.

Keywords: asthma–COPD overlap syndrome, asthma, COPD, differential diagnosis, surveys and questionnaires

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