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

Introduction: Despite increased acceptance of asthma and COPD overlap, data on the relationship between clinical and spirometric features are limited. Aims and objectives: To quantify the relationship between patient-reportable facets of obstructive lung disease and spirometric classification of patients. Methods: Patients with asthma and/or COPD (n=1248) were divided into three groups: (i) asthma (non-obstructive {post-bronchodilator forced expiratory volume in 1 second/forced vital capacity ≥0.7} and reversible {response to salbutamol ≥200 mL and ≥12%}); (ii) asthma-COPD overlap syndrome (ACOS; obstructive and reversible); and (iii) COPD (obstructive and non-reversible). A questionnaire was created to record patient demographics, symptoms, morbidity and medical history. Multi-tier nominal logistic regression modelling identified discriminatory variables, which were assessed using model-based predictions relative to spirometry-based classification. Results: The modelled variables were consistent with ACOS in 369/530 patients with spirometry-classified ACOS, and consistent with no ACOS in 476/682 patients without spirometry-classified ACOS (Table). The model performed with 70% sensitivity and specificity (predictive values: negative 75%, positive 64%). Conclusions: Selective clinical questioning has modest predictive value for spirometric classification of obstructive lung disease. Funding: GSK (NCT02302417; GSK study 201703).