Toward Data-Driven Learning Healthcare Systems in Interventional Radiology: Implementation to Evaluate Venous Stent Patency

We developed a code and data-driven system (learning healthcare system) for gleaning actionable clinical insight from interventional radiology (IR) data. To this end, we constructed a workflow for the collection, processing and analysis of electronic health record (EHR), imaging, and cancer registry data for a cohort of interventional radiology patients seen in the IR Clinic at our institution over a more than 20-year period. As part of this pipeline, we created a database in REDCap (VITAL) to store raw data, as collected by a team of clinical investigators and the Data Coordinating Center at our university. We developed a single, universal pre-processing codebank for our VITAL data in R; in addition, we also wrote widely extendable and easily modifiable analysis code in R that presents results from summary statistics, statistical tests, visualizations, Kaplan-Meier analyses, and Cox proportional hazard modeling, among other analysis techniques. We present our findings for a test case of supra versus infra-inguinal ligament stenting. The developed pre-processing and analysis pipelines were memory and speed-efficient, with both pipelines running in less than 2 min. Three different supra-inguinal ligament veins had a statistically significant improvement in vein diameters post-stenting versus pre-stenting, while no infra-inguinal ligament veins had a statistically significant improvement (due either to an insufficient sample size or a non-significant p value). However, infra-inguinal ligament stenting was not associated with worse restenosis or patency outcomes in either a univariate (summary-statistics and Kaplan-Meier based) or multivariate (Cox proportional hazard model based) analysis.