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. 2020 Feb;33(1):25-36.
doi: 10.1007/s10278-019-00280-6.

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

David M Cohn  1 , Tarub S Mabud  1 , Victoria A Arendt  2 , Andre D Souffrant  1 , Gyeong S Jeon  3 , Xiao An  4 , William T Kuo  1 , Daniel Y Sze  1 , Lawrence V Hofmann  1 , Daniel L Rubin  5
Affiliations

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

David M Cohn et al. J Digit Imaging. 2020 Feb.

Abstract

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.

Keywords: Data mining; Inguinal ligament; Interventional radiology; Learning healthcare system.

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Figures

Fig. 1
Fig. 1
Structured data is derived using a combination of clinician evaluation (EHR and imaging) and a data acquisition workflow (Data Coordinating Center Data Acquisition Pipeline) and then inputted into the VITAL database using the REDCap user interface (GUI); figure created in Adobe Photoshop CC
Fig. 2
Fig. 2
Data entry form in REDCap
Fig. 3
Fig. 3
Learning healthcare (pre-processing and analysis) system in RStudio Desktop, with its outputted results in the form of dataframes and visualizations (Kaplan-Meier figure shown); figure created in R/RStudio
Fig. 4
Fig. 4
Pre-processing and analysis workflow diagram with significant modules indicated; figure created in Adobe Photoshop CC
Fig. 5
Fig. 5
Limb-level restenosis Kaplan-Meier curves for the supra versus infra-inguinal ligament cohorts; figure created in R/RStudio
Fig. 6
Fig. 6
Limb-level primary patency Kaplan-Meier Curves for the supra versus infra-inguinal ligament cohorts; figure created in R/RStudio
See this image and copyright information in PMC

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