Predictive modeling of COPD exacerbation rates using baseline risk factors. (July 2022)

• Record Type: Journal Article
• Authors: Singh, Dave; Hurst, John R.; Martinez, Fernando J.; Rabe, Klaus F.; Bafadhel, Mona; Jenkins, Martin; Salazar, Domingo; Dorinsky, Paul; Darken, Patrick
• Is Part Of: Therapeutic advances in respiratory disease. Volume 16(2022)
• Publication Date: 2022-07
• Subjects: chronic obstructive pulmonary disease -- exacerbations -- ICS/LAMA/LABA -- machine learning -- prediction model -- triple therapy; Respiratory organs -- Diseases -- Periodicals; Respiratory agents -- Periodicals; Pulmonary pharmacology -- Periodicals; Respiratory Tract Diseases -- Periodicals; Respiratory System Agents -- therapeutic use -- Periodicals; Respiratory Tract Diseases -- drug therapy -- Periodicals; Lung Diseases -- drug therapy -- Periodicals; Appareil respiratoire -- Maladies -- Traitement -- Périodiques; Agents respiratoires -- Périodiques; Pharmacologie pulmonaire -- Périodiques; 616.2005
• DOI: 10.1177/17534666221107314
• Languages: English
• ISSNs: 1753-4658

Abstract

Background: Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual's rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD. Methods: Exacerbation data from 20, 054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation. Results: Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use. Conclusion: These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients. Clinical trials registered withwww.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878)