• The Creation of Objective Performance Criteria and Generation of Predictive Models among Medical Devices in a Vascular Space

      Gressler, Laura; Shaya, Fadia T.; 0000-0003-2042-2174 (2021)
      Background: Objective Performance Criteria (OPC) have been explored as a tool to address the growing pressures to expedite device approval and enhance active surveillance. Existing data infrastructures can be employed to develop OPC to evaluate the use of devices, and can be further leveraged to develop predictive models. The objective of this dissertation was to: (1) Develop a framework for the creation of OPC, (2) Compare the use of stent, atherectomy, and combination of stent and atherectomy, and (3) Formulate a predictive model used to predict the probability of undergoing a major adverse limb event (MALE) or experiencing death following the aforementioned treatments. Methods: The framework was developed in 3 phases through (1) Review of the literature, (2) Engagement of key stakeholders, and (3) Feedback from an advisory committee. Retrospective cohort studies were conducted using the Vascular Quality Initiative (2010-2018). Logistic regression and the Fine-Gray subdistribution hazard model were used to compare short- and long-term MALE, respectively. A generalized linear model (GLM), a Least Absolute Shrinkage and Selection Operator (LASSO) regularized GLM, a gradient boosted decision tree, and random forest model were compared when used to predict MALE and mortality. Results: The developed framework consisted of 5 elements: (1) Identification of Medical Devices, (2) Engagement of Key Stakeholders, (3) Selection of Data Source, (4) Performance of Appropriate Statistical Analyses, (5) Reporting of Findings. The odds of short-term MALE (0.94;95%CI:0.77-1.14) and hazards of long-term MALE (0.92;95%CI:0.82-1.04) were not significantly different in the combination stent and atherectomy group when compared to stent alone. The most effective predictive model was the gradient boosted decision tree (Area Under the Curve (AUC)= 0.7539) for MALE and the LASSO regularized GLM (AUC=0.7930) for mortality. Conclusions: The developed framework provides a guide and needed foundation for the continued generation of OPC. Applying the identified statistical steps in the framework to an existing data infrastructure showed that patients receiving combination stent and atherectomy do not experience significantly different rates of MALE compared to stent alone. Predictive models generated using the infrastructure demonstrated the ability of machine learning techniques to generate robust predictive models within the vascular space.
    • Use of Machine Learning To Predict COPD Treatments and Exacerbations in Medicare Older Adults: A Comparison of Multiple Approaches

      Le, Tham Thi; Simoni-Wastila, Linda (2021)
      Background: 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.