Date of Award
8-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Bioengineering
Committee Chair/Advisor
Dr. Ravikiran Singapogu
Committee Member
Dr. Joe Bible
Committee Member
Dr. Jeremy Mercuri
Committee Member
Dr. Prabir Roy-Chaudhury
Abstract
End Stage Kidney Disease (ESKD) is a significant public health burden in the United States. Life-sustaining hemodialysis, required up to three times a week for ESKD patients, necessitates a functioning vascular access. For proper cannulation, cannulators have many factors to consider, including an AVF’s geometry, depth, diameter, and other details. Poor cannulation can lead to infiltration, where the needle punctures the vessel wall. Infiltration occurs in as many as 50% of cannulations, causing complications ranging from hematoma and swelling to delayed treatment or loss of vascular access. This highlights the need for objective, standardized training methods to improve cannulation skills in dialysis clinics. Simulators offer a solution by quantifying skill objectively and systematizing training for novice medical trainees and experienced professionals alike to train in safe, risk-free environments.
In this work, a review on the state-of-the-art for palpation research is first presented with a special focus on the sensors used and subjective and objective metrics in palpation-based simulation. Following this, three studies on the CanSim -- a smart simulator for hemodialysis cannulation -- are presented. A study on the palpation behavior on our simulator of 52 clinicians identified the differences between high performing and low performing subjects. High performers completed the task more confidently and intentionally, taking shorter amounts of time, having a higher ratio of correct movement, and having a shorter Path Length. These metrics can be applied in the training of palpation skill by providing objective quantification of palpation behavior.
Then, the development and validation of improvements made to the CanSim are presented including hardware and software improvements for increased accuracy, usability, and realism towards automated skills training for hemodialysis cannulation. Finally, a clinical study is described and analyzed towards understanding behavior on the simulator while using an ultrasound device to inform cannulation. Machine learning methods were employed to identify what features are most important for achieving good outcomes on the simulator as well as exploring whether stratifying data based on fistula geometry or ultrasound use affected the classification of cannulation skill. Results demonstrated that outcome performance on the CanSim can be predicted through the use of machine learning methods. However, the effect of ultrasound use for cannulation or fistula geometry was limited for classification of cannulation outcomes. This work provides potential methods to identify salient cannulation features to classify cannulation skill and simulator-based outcomes.
Recommended Citation
Petersen, Lydia, "Measuring Cannulation Behavior on a Sensorized Simulator: Development, Verification, and Testing of System Components" (2024). All Dissertations. 3753.
https://open.clemson.edu/all_dissertations/3753
Author ORCID Identifier
0000-0002-5707-0144