To provide a training tool to address the technical challenges of robot-assisted laparoscopic partial nephrectomy, we created silicone renal tumor models using 3-dimensional printed molds of a patient's kidney with a mass. In this study, we assessed the face, content, and construct validity of these models.
Materials and Methods
Surgeons of different training levels completed 4 simulations on silicone renal tumor models. Participants were surveyed on the usefulness and realism of the model as a training tool. Performance was measured using operation-specific metrics, self-reported operative demands (NASA Task Load Index [NASA TLX]), and blinded expert assessment (Global Evaluative Assessment of Robotic Surgeons [GEARS]).
Twenty-four participants included attending urologists, endourology fellows, urology residents, and medical students. Post-training surveys of expert participants yielded mean results of 79.2 on the realism of the model's overall feel and 90.2 on the model's overall usefulness for training. Renal artery clamp times and GEARS scores were significantly better in surgeons further in training (P ≤.005 and P ≤.025). Renal artery clamp times, preserved renal parenchyma, positive margins, NASA TLX, and GEARS scores were all found to improve across trials (P <.001, P = .025, P = .024, P ≤.020, and P ≤.006, respectively).
Face, content, and construct validity were demonstrated in the use of a silicone renal tumor model in a cohort of surgeons of different training levels. Expert participants deemed the model useful and realistic. Surgeons of higher training levels performed better than less experienced surgeons in various study metrics, and improvements within individuals were observed over sequential trials. Future studies should aim to assess model predictive validity, namely, the association between model performance improvements and improvements in live surgery.
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Published online: February 05, 2018
Accepted: January 26, 2018
Received: December 1, 2017
Financial Disclosure: The authors declare that they have no relevant financial interests.
© 2018 Elsevier Inc. All rights reserved.