American Journal of Medicine Studies
ISSN (Print): 2333-8881 ISSN (Online): 2333-889X Website: https://www.sciepub.com/journal/ajms Editor-in-chief: Apply for this position
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American Journal of Medicine Studies. 2022, 10(1), 1-7
DOI: 10.12691/ajms-10-1-1
Open AccessArticle

Transradial Access - A Predictive Model to Assess Radial Artery Diameter

Michael Brockman1, , Jarren Adam2, Cassandra De La Torre3, Lucas Achkarian4 and Robert Hanna5

1Texas Tech University Health Sciences Center El Paso

2St. Joseph’s Medical Center-Stockton, California

3Adventist Health White Memorial

4Cornell University

5Banner Desert Medical Center

Pub. Date: November 20, 2022

Cite this paper:
Michael Brockman, Jarren Adam, Cassandra De La Torre, Lucas Achkarian and Robert Hanna. Transradial Access - A Predictive Model to Assess Radial Artery Diameter. American Journal of Medicine Studies. 2022; 10(1):1-7. doi: 10.12691/ajms-10-1-1

Abstract

Purpose: Currently, the femoral artery is the most common vascular access site for interventional procedures. With medical instrument advancements and improved procedure techniques, the radial artery is becoming the preferred vascular access point for interventionalists due to decreased complication risks and improved recovery times. Although transradial procedures have improved clinical outcomes, additional, pre-interventional imaging is often required to assess the capacity of the smaller radial artery when comparing the femoral artery. This study explores an alternative, machine learning-based method to predict radial artery adequacy for interventional procedures without the need for additional, pre-intervention imaging. Materials and Methods: Whole human body computed tomography (CT) scans were collected from New Mexico’s Decedent Image Database. The inclusion criteria were decedents between the ages of 18 to 99, who died of natural causes, and had no identifiable decomposition. The decedent’s femoral artery diameter, sex assigned at birth, age, carcinogen exposure, smoking history, drinking history, substance usage, height, and weight were recorded. Using Horos Project DICOM viewer, the right femoral and right radial artery of each CT scan was measured. The resulting measurements were then passed through the Python 3.9 module, Scikit-Learn, to create a predictive random forest classifier. Results: Femoral and radial artery diameters were measured on 127 decadents. The predetermined threshold for radial artery usability diameter was 2.5 mm, which was based on guidance from Hobby et al’s publication, Transradial Access: A Comprehensive Review. The performed linear regression of femoral artery diameter explains 54.9% of the variability found in radial artery diameter. After one-thousand training iterations, the random forest classifier model achieved a mean testing accuracy of 85.71% in predicting if the radial artery equal to or greater than 2.5mm.

Keywords:
the presented study preliminarily suggests that femoral artery diameter and other parameters such as age height weight and smoking history can be used for training a random forest classifier model to assess radial artery diameter

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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