Analysis of patent, unstented lower extremity vein segment diameters in 266 patients with venous disease.

OBJECTIVE: The objective of this study was to characterize the average maximum diameters of widely patent lower extremity vein segments in patients with underlying venous disease and the demographic factors that affect these diameters. METHODS: Maximum axial diameters of each deep vein segment from the diaphragm to the knee were measured from computed tomography venography studies for all patients who underwent venous stent placement during a 20-year period at a single quaternary venous referral institution. Limbs containing only widely patent, unstented vein segments without variant anatomy were identified for inclusion. The final analysis involved diameter measurements from 870 imaging studies of 266 patients. Multivariate linear regression was used to identify factors associated with vein segment diameters. RESULTS: Average vein segment diameters ranged from 7.8 mm for the left and right femoral veins to 27.9 mm for the long axis of the suprarenal inferior vena cava. Multivariate linear regression demonstrated that women had larger IVC, common iliac vein, and external iliac vein diameters, whereas men had larger common femoral veins. Laterality, height, weight, and sex also had statistically significant associations with the diameters of select vein segments. CONCLUSIONS: This study provides an estimate of the average diameters of widely patent deep vein segments in the lower extremities from the diaphragm to the knees in patients with underlying venous disease and characterizes covariates that significantly affect vein diameter. These findings may help interventionalists better select devices for endovascular intervention.

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.