Use of Machine Learning To Predict COPD Treatments and Exacerbations in Medicare Older Adults: A Comparison of Multiple Approaches
AuthorLe, Tham Thi
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AbstractBackground: Multiple comorbidities, suboptimal adherence to maintenance medications (MMs), and exacerbations remain clinically important problems among older adults with chronic obstructive pulmonary disease (COPD). To better understand comorbidity profiles and to facilitate risk-based strategies for disease management, this dissertation quantified the prevalence and newly diagnosed rates of comorbidities, and validated predictive models of COPD medication non-adherence and exacerbations in the older Medicare population. Methods: Comorbidities were quantified in COPD beneficiaries and compared with matched non-COPD individuals using multivariable logistic regression. In a cohort of COPD beneficiaries with prevalent and new MM use, logistic and LASSO regressions were used to cross-validate the prediction of one-year non-adherence to MMs using different sets of predictors. A time-varying design was applied to assess improvement in predicting COPD exacerbations of the super learner versus component approaches (logistic regression, elastic net regression, random forest, gradient boosting, and neural network). Results: COPD beneficiaries had significantly increased odds of 40 measured comorbidities relative to matched non-COPD controls. The best-performing models in predicting MM non-adherence were those including initial MM adherence as a predictor, with validated Area Under the ROC Curves (AUC: 0.871-0.881). In predicting COPD exacerbations there were time-varying estimates of predictive accuracy and associations between predictors and the exacerbation outcome. Super learner performed slightly better (AUC: 0.650-0.761) than individual machine learning methods. Conclusions: Comorbidity burden is substantial and increases over time among Medicare older adults with COPD. Generated models achieved good and average discrimination in predicting COPD medication non-adherence and exacerbations, respectively. COPD hospitalization, oxygen supplementation, COPD treatment adherence, and numbers of inpatient visits were the most important predictors of COPD medication non-adherence and exacerbations. Super learner demonstrates a slight improvement compared to component methods, suggesting potential usability in augmenting prediction. Validated models with good discrimination can be adopted using friendly tools to optimizing resources for risk-based management and interventions of COPD.
DescriptionPharmaceutical Health Services Research
University of Maryland, Baltimore
Pulmonary Disease, Chronic Obstructive