Evaluating Triple Therapy Treatment Pathways in Chronic Obstructive Pulmonary Disease (COPD): A Machine-Learning Predictive Model

Abstract

Purpose

Inhaled triple therapy (TT) comprising a long-acting muscarinic antagonist, long-acting β₂ agonist, and inhaled corticosteroid is recommended for symptomatic chronic obstructive pulmonary disease (COPD) patients, or those at risk of exacerbation. However, it is not well understood which patient characteristics contribute most to future exacerbation risk. This study assessed patient predictors associated with future exacerbation time following initiation of TT.

Patients and Methods

This retrospective cohort study used data from the Optum™ Clinformatics™ Data Mart, a large health claims database in the United States. COPD patients who initiated TT between January 2008 and March 2018 (index) were eligible. Patients were required to be aged ≥18 years at index and have continuous enrollment for the 12 months prior to index (baseline) and the 12 months following index (follow-up). Patients who had received TT during baseline were excluded. Data from eligible patients were analyzed using a reverse engineering forward simulation machine learning platform to predict future COPD exacerbation time.

Results

Data from 73,625 patients were included. The model found that prior exacerbation was largely correlated with post-index exacerbation time; patients who had ≥4 exacerbation episodes during baseline had an average increase of 32.4 days post-index exacerbation, compared with patients with no exacerbations during baseline. Likewise, ≥2 inpatient visits (effect size 27.1 days), the use of xanthines (effect size 11.5 days), or rheumatoid arthritis (effect size 6.4 days) during baseline were associated with increased exacerbation time. Conversely, diagnosis of anemia (effect size –5.68 days), or oral corticosteroids in the past month (effect size –3.43 days) were associated with reduced exacerbation time.

Conclusion

Frequent prior exacerbations, healthcare resource utilization, xanthine use, and rheumatoid arthritis were the strongest factors predicting the future increase of exacerbations. These results improve our understanding of exacerbation risk among COPD patients initiating triple therapy.

Keywords:

• Bayesian modeling
• chronic obstructive pulmonary disease
• exacerbation
• triple therapy
• predictive modeling

Abbreviations

COPD, chronic obstructive pulmonary disease; CT, computed tomography; ER, emergency room; HMG-CoA, β-hydroxy β-methylglutaryl-CoA; HR, hazard ratio; ICS, inhaled corticosteroid; LABA, long-acting β₂ agonist; LAMA, long-acting muscarinic antagonist; Optum CDM, Optum™ Clinformatics™ Data Mart; RA, rheumatoid arthritis; REFS™, reverse engineering forward simulation; SABA, short-acting β₂ agonist; SAMA, short-acting muscarinic agonist; SD, standard deviation.

Data Sharing Statement

The data analyzed in this manuscript are contained in a database owned by Optum Clinformatics and therefore are not publicly available. Access to the data may be available on license from Optum (optum.com/life-sciences-solutions).

Ethics Approval and Informed Consent

Ethics committee approval was not required for this study as the results were presented as aggregate analyses that omit patient identification. Patient informed consent was not required as there was no direct patient contact or primary collection of individual patient data.

Acknowledgments

Editorial support (in the form of writing assistance, including preparation of the draft manuscript under the direction and guidance of the authors, collating and incorporating authors’ comments for each draft, assembling tables and figures, grammatical editing, and referencing) was provided by Fiona Goodwin and Rebecca Cunningham of Aura, a division of Spirit Medical Communications Group Limited, and was funded by GlaxoSmithKline. These data have been presented in abstract/poster form at the American Thoracic Society – 117th International Conference (Bogart M, Oakland T, Liu Y, Enev T. Triple therapy treatment pathways in chronic obstructive pulmonary disease (COPD): a real-world predictive model – American Thoracic Society – 117th International Conference. Am J Respir Crit Care Med. 2021;203:A2313.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

Disclosure

MB is an employee of and/or holds stocks/shares in GlaxoSmithKline. YL is an employee of and/or holds stocks/shares in GNS Healthcare. TO is an employee of and/or holds stocks/shares in GNS Healthcare. MS is an employee of and/or holds stocks/shares in GlaxoSmithKline. The authors report no other conflicts of interest in this work.

Additional information

Funding

This study was funded by GlaxoSmithKline plc (study number 213319). The sponsor was involved in study conception and design, data interpretation, and the decision to submit the article for publication. The sponsor was also given the opportunity to review the manuscript for medical and scientific accuracy as well as intellectual property considerations.