Browsing School, Graduate by Subject "objective performance criteria"
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The Creation of Objective Performance Criteria and Generation of Predictive Models among Medical Devices in a Vascular SpaceBackground: 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.