Abstract TP121: Simulated Inanimate Model for Physical Learning Experience (SIMPLE) of Carotid Endarterectomy Using a 3-D Printing Technology

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Introduction: Surgical education relies heavily on operative exposure with live patients. However, the carotid endarterectomy (CEA) demands an experienced surgeon with a very low complication rate. The high-risk nature of this procedure and the decline in number of CEAs performed annually has created a training gap. Current surgical skill simulators do not provide a comparable surgical experience.

Hypothesis: The SIMPLE-CEA is a comprehensive and realistic operative exercise that replicates all vital steps in a CEA and permits measurement of clinically significant metrics.

Methods: Using a novel method, anatomically accurate models of the human neck and carotid artery were created using poly-vinyl alcohol hydrogels. Relevant structures were also fabricated including the vagus and hypoglossal nerves, internal jugular and facial veins, sternocleidomastoid and sternohyoid muscles, and carotid sheath. 3D models were created based on an amalgamation of patient data using CAD software. Injection molds of the modeled tissues were 3D printed. Graded polymerization of the hydrogel was achieved by inducing crosslinks during freeze/thaw cycles, which stiffened the simulated tissues to realistic tactile properties. Bleeding was simulated using pressure bags and a ventricular assistive device was used to create pulsatile flow in the carotid.

Results: Realistic models were produced using 3D printed injection models and polymer hydrogels. Preliminary findings show an average of 4/5 for face validity surveys according to experts who performed preliminary testing. Comparison of surgeon performance was feasible using construct metrics such as carotid clamp time, estimated blood loss, and nerve injury.

Conclusion: SIMPLE-CEA is a realistic, inexpensive model of high fidelity, offering comprehensive training for CEA and allows residents to master required skills prior to operating on a live patient. Next steps include testing content and construct validity of the model.

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