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Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data

Authors Annavarapu S, Goldfarb S, Gelb M, Moretz C, Renda A, Kaila S

Received 1 November 2017

Accepted for publication 5 April 2018

Published 11 July 2018 Volume 2018:13 Pages 2121—2130

DOI https://doi.org/10.2147/COPD.S155773

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Charles Downs

Peer reviewer comments 2

Editor who approved publication: Dr Richard Russell


Srinivas Annavarapu,1 Seth Goldfarb,1 Melissa Gelb,2 Chad Moretz,1 Andrew Renda,3 Shuchita Kaila2

1Comprehensive Health Insights, Louisville, KY, 2Boehringer Ingelheim, Ridgefield, CT, 3Humana, Louisville, KY, USA

Background: Patients with COPD often experience severe exacerbations involving hospitalization, which accelerate lung function decline and reduce quality of life. This study aimed to develop and validate a predictive model to identify patients at risk of developing severe COPD exacerbations using administrative claims data, to facilitate appropriate disease management programs.
Methods: A predictive model was developed using a retrospective cohort of COPD patients aged 55–89 years identified between July 1, 2010 and June 30, 2013 using Humana’s claims data. The baseline period was 12 months postdiagnosis, and the prediction period covered months 12–24. Patients with and without severe exacerbations in the prediction period were compared to identify characteristics associated with severe COPD exacerbations. Models were developed using stepwise logistic regression, and a final model was chosen to optimize sensitivity, specificity, positive predictive value (PPV), and negative PV (NPV).
Results: Of 45,722 patients, 5,317 had severe exacerbations in the prediction period. Patients with severe exacerbations had significantly higher comorbidity burden, use of respiratory medications, and tobacco-cessation counseling compared to those without severe exacerbations in the baseline period. The predictive model included 29 variables that were significantly associated with severe exacerbations. The strongest predictors were prior severe exacerbations and higher Deyo–Charlson comorbidity score (OR 1.50 and 1.47, respectively). The best-performing predictive model had an area under the curve of 0.77. A receiver operating characteristic cutoff of 0.4 was chosen to optimize PPV, and the model had sensitivity of 17%, specificity of 98%, PPV of 48%, and NPV of 90%.
Conclusion: This study found that of every two patients identified by the predictive model to be at risk of severe exacerbation, one patient may have a severe exacerbation. Once at-risk patients are identified, appropriate maintenance medication, implementation of disease-management programs, and education may prevent future exacerbations.

Keywords: Medicare, observational study, COPD risk factors

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