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Updated: Feb 7, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using
Srinivas Annavarapu1, Seth Goldfarb1, Melissa Gelb2
1Comprehensive Health Insights, Louisville, KY, srinivas.annavarapu@gmail.com.
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.
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