Toward Data-Driven Learning Healthcare Systems in Interventional Radiology: Implementation to Evaluate Venous Stent Patency. Journal of digital imaging Cohn, D. M., Mabud, T. S., Arendt, V. A., Souffrant, A. D., Jeon, G. S., An, X., Kuo, W. T., Sze, D. Y., Hofmann, L. V., Rubin, D. L. 2019


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 2min. 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.

View details for DOI 10.1007/s10278-019-00280-6

View details for PubMedID 31650318