WATTS happening? Evaluation of thermal dose during holmium laser lithotripsy in a high-fidelity anatomic model.

PURPOSE: To evaluate the thermal profiles of the holmium laser at different laser parameters at different locations in an in vitro anatomic pelvicalyceal collecting system (PCS) model. Laser lithotripsy is the cornerstone of treatment for urolithiasis. With the prevalence of high-powered lasers, stone ablation efficiency has become more pronounced. Patient safety remains paramount during surgery. It is well recognized that the heat generated from laser lithotripsy has the potential to cause thermal tissue damage. METHODS: Utilizing high-fidelity, 3D printed hydrogel models of a PCS with a synthetic BegoStone implanted in the renal pelvis, laser lithotripsy was performed with the Moses 2.0 holmium laser. At a standard power (40 W) and irrigation pressure (100 cm H2O), we evaluated operator duty cycle (ODC) variations with different time-on intervals at four different laser settings. Temperature was measured at two separate locations-at the stone and away from the stone. RESULTS: Temperatures were highest closest to the laser tip with a decrease away from the laser. Fluid temperatures increased with longer laser-on times and higher ODCs. Thermal doses were greater with increased ODCs and the threshold for thermal injury was reached for ODCs of 75% and 100%. CONCLUSION: Temperature generation and thermal dose delivered are greatest closer to the tip of the laser fiber and are not dependent on power alone. Significant temperature differences were noted between four laser settings at a standardized power (40 W). Temperatures can be influenced by a variety of factors, such as laser-on time, operator duty cycle, and location in the PCS.

Getting hot in here! Comparison of Holmium vs. thulium laser in an anatomic hydrogel kidney model.

As laser technology has advanced, high-power lasers have become increasingly common. The Holmium: yttrium-aluminum-garnet (Ho:YAG) laser has long been accepted as the standard for laser lithotripsy. The thulium fiber laser (TFL) has recently been established as a viable option. The aim of this study is to evaluate thermal dose and temperature for the Ho:YAG laser to the TFL at four different laser settings while varying energy, frequency, operator duty cycle (ODC). Utilizing high-fidelity, 3D-printed hydrogel models of a pelvicalyceal collecting system (PCS) with a synthetic BegoStone implanted in the renal pelvis, laser lithotripsy was performed with the Ho:YAG laser or TFL. At a standard power (40W) and irrigation (17.9 ml/min), we evaluated four different laser settings with ODC variations with different time-on intervals. Temperature was measured at two separate locations. In general, the TFL yielded greater cumulative thermal doses than the Ho:YAG laser. Thermal dose and temperature were typically greater at the stone when compared away from the stone. Regarding the TFL, there was no general trend if fragmentation or dusting settings yielded greater thermal doses or temperatures. The TFL generated greater temperatures and thermal doses in general than the Ho:YAG laser with Moses technology. Temperatures and thermal doses were greater closer to the laser fiber tip. It is inconclusive as to whether fragmentation or dusting settings elicit greater thermal loads for the TFL. Energy, frequency, ODC, and laser-on time significantly impact thermal loads during ureteroscopic laser lithotripsy, independent of power.

From Consensus to Validation: A Multicenter Study for Design and Development of a Holmium Laser Enucleation of the Prostate Hydrogel Simulation Platform.

Background: Holmium laser enucleation of the prostate (HoLEP) has emerged as a new gold standard for treatment of benign prostatic hyperplasia; however, its steep learning curve hinders generalization of this technique. Therefore, there is a need for a benchtop HoLEP simulator to reduce this learning curve and provide training. We have developed a nonbiohazardous HoLEP simulator using modern education theory and validated it in a multicenter study. Materials and Methods: Six experts established key components for a HoLEP simulator through a Delphi consensus over three rounds including 250 questions. After consensus, a digital design was created and approved by experts, then used to fabricate a physical prototype using three-dimensional printing and hydrogel molding. After a process of iterative prototype testing, experts completed a survey assessing the simulator with a 5-point Likert scale for final approval. The approved model was validated with 56 expert and novice participants at seven institutions using subjective and objective performance metrics. Results: Consensus was reached on 85 of 250 questions, and experts found the physical model to adequately replicate 82.5% of required features. Objective metrics were statistically significant (p < 0.0001) when comparing experts and novices for enucleation time (37.4 ± 8.2 vs 16.7 ± 6.8 minutes), adenoma weight (79.6 ± 20.4 vs 36.2 ± 9.9 g), and complications (6 vs 22), respectively. Conclusion: We have effectively completed a multicenter study to develop and validate a nonbiohazardous benchtop simulator for HoLEP through modern education theory. A training curriculum including this simulator is currently under development.

Evaluation of Mixed Reality Technologies for Remote Feedback and Guidance During Transrectal Ultrasound Biopsy Simulation: A Prospective, Randomized, Crossover Study.

OBJECTIVE: To compare equivalency of remote to in-person training during simulated transrectal ultrasound-guided prostate biopsy, we combined three technologies (mixed reality [MR] software, smart glasses, and hydrogel simulation model). Taken together, telemonitoring harnesses data streaming to provide real-time supervision and technical assistance for surgical procedures from an expert at a remote geographical location. METHODS: Nineteen students were randomized into two groups (MR-first and in-person-first) and proctored to measure prostate volume and perform 14-biopsies over seven sessions: pretest, two MR/in-person-guided training sessions, mid-test, crossover into two in-person/MR-guided training sessions, and post-test. MR sessions utilized Vuzix smart glasses with MR software (HelpLightning) to share the student's first-person perspective and Zoom to project the ultrasound screen to a remote instructor. Training and test sessions utilized single-color and seven-color prostate models, respectively. Accuracy of biopsy cores from test sessions were compared. Perception of instruction following each training session using 5-point Likert scales across five domains was assessed. Preference of instruction modality was assessed qualitatively. RESULTS: Comparison of mid-test performance following two training sessions was similar across the two groups (MR-first 63.8% vs in-person-first 57.6%, P = .340). Following crossover, difference in post-test performance of the MR-first group and the in-person-first group approached significance (MR-first 80.2% vs in-person-first 70.8%, P = .050). Student evaluation of MR and in-person instruction following training sessions was similar across the five metrics. CONCLUSION: MR-based remote learning is equally effective when compared to traditional in-person instruction.

Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot-assisted radical prostatectomy using our validated hydrogel-based simulation platform and identify potential metrics of surgical expertise. METHODS: Video, robotic kinematics, and force sensor data were collected from 35 board-certified urologists at the 2022 AUA conference. Video was annotated for surgical gestures. Objective performance indicators were derived from robotic system kinematic data. Force metrics were calculated from hydrogel model integrated sensors. Data were fitted to 3 supervised machine learning models-logistic regression, support vector machine, and k-nearest neighbors-which were used to predict procedure-specific learning curve proficiency. Recursive feature elimination was used to optimize the best performing model. RESULTS: Logistic regression predicted caseload with the highest AUC score for 5/7 possible data combinations (force, 64%; objective performance indicators + gestures, 94%; objective performance indicators + force, 90%; gestures + force, 93%; objective performance indicators + gestures + force, 94%). Support vector machine predicted the highest AUC score for objective performance indicators (82%) and gestures (94%). Logistic regression with recursive feature elimination was the most effective model reaching 96% AUC in predicting case-specific experience. Most contributory features were identified across all model types. CONCLUSIONS: We have created a machine learning-based algorithm utilizing a novel combination of objective performance indicators, gesture analysis, and integrated force metrics to predict surgical experience, capable of discriminating between surgeons with low or high robot-assisted radical prostatectomy caseload with 96% AUC in a standardized, simulation-based environment.

Robotic Assisted Transplant Nephrectomy: Case Series and Training Model for Improving Adoption.

Introduction: Open transplant nephrectomy for failed renal allograft is an invasive procedure associated with significant perioperative morbidity and mortality. Minimally invasive surgical approaches have improved a variety of patient outcomes for many surgeries. Thus, robotic assisted transplant nephrectomy (RATN) potentially offers significant patient benefit. Although previously reported, there remains a paucity of data on RATN outcomes and techniques. Methods: Four perfused, high-fidelity hydrogel models were created using previously described techniques and used for simulated RATN. Subsequently performed institutional cases were included for analysis. Intra- and postoperative variables along with patient demographics were retrospectively obtained through parsing of patient records. Results: Simulated nephrectomy time was 67.33 minutes (35.75 - 98.91). Five patients underwent RATN. There were four male and one female patients. The average age was 47 years. The most common indication was abdominal pain secondary to rejection (3/5). Mean blood loss was 188 mL; mean operative time was 243 minutes, and mean length of stay was 4.5 days. Intraoperatively there were two incidences of small cystotomies. One patient was readmitted within 30 days for intraabdominal abscess. Conclusion: This study adds to the growing literature around RATN, demonstrating the feasibility of the technique and reporting good outcomes for this cohort.