Towards navigation in endoscopic kidney surgery based on preoperative imaging.

Endoscopic renal surgeries have high re-operation rates, particularly for lower volume surgeons. Due to the limited field and depth of view of current endoscopes, mentally mapping preoperative computed tomography (CT) images of patient anatomy to the surgical field is challenging. The inability to completely navigate the intrarenal collecting system leads to missed kidney stones and tumors, subsequently raising recurrence rates. A guidance system is proposed to estimate the endoscope positions within the CT to reduce re-operation rates. A Structure from Motion algorithm is used to reconstruct the kidney collecting system from the endoscope videos. In addition, the kidney collecting system is segmented from CT scans using 3D U-Net to create a 3D model. The two collecting system representations can then be registered to provide information on the relative endoscope position. Correct reconstruction and localization of intrarenal anatomy and endoscope position is demonstrated. Furthermore, a 3D map is created supported by the RGB endoscope images to reduce the burden of mental mapping during surgery. The proposed reconstruction pipeline has been validated for guidance. It can reduce the mental burden for surgeons and is a step towards the long-term goal of reducing re-operation rates in kidney stone surgery.

ASSIST-U: A system for segmentation and image style transfer for ureteroscopy.

Kidney stones require surgical removal when they grow too large to be broken up externally or to pass on their own. Upper tract urothelial carcinoma is also sometimes treated endoscopically in a similar procedure. These surgeries are difficult, particularly for trainees who often miss tumours, stones or stone fragments, requiring re-operation. Furthermore, there are no patient-specific simulators to facilitate training or standardized visualization tools for ureteroscopy despite its high prevalence. Here a system ASSIST-U is proposed to create realistic ureteroscopy images and videos solely using preoperative computerized tomography (CT) images to address these unmet needs. A 3D UNet model is trained to automatically segment CT images and construct 3D surfaces. These surfaces are then skeletonized for rendering. Finally, a style transfer model is trained using contrastive unpaired translation (CUT) to synthesize realistic ureteroscopy images. Cross validation on the CT segmentation model achieved a Dice score of 0.853 ± 0.084. CUT style transfer produced visually plausible images; the kernel inception distance to real ureteroscopy images was reduced from 0.198 (rendered) to 0.089 (synthesized). The entire pipeline from CT to synthesized ureteroscopy is also qualitatively demonstrated. The proposed ASSIST-U system shows promise for aiding surgeons in the visualization of kidney ureteroscopy.

Intraoperative gaze guidance with mixed reality.

Efficient communication and collaboration are essential in the operating room for successful and safe surgery. While many technologies are improving various aspects of surgery, communication between attending surgeons, residents, and surgical teams is still limited to verbal interactions that are prone to misunderstandings. Novel modes of communication can increase speed and accuracy, and transform operating rooms. A mixed reality (MR) based gaze sharing application on Microsoft HoloLens 2 headset that can help expert surgeons indicate specific regions, communicate with decreased verbal effort, and guide novices throughout an operation is presented. The utility of the application is tested with a user study of endoscopic kidney stone localization completed by urology experts and novice surgeons. Improvement is observed in the NASA task load index surveys (up to 25.23%), in the success rate of the task (6.98% increase in localized stone percentage), and in gaze analyses (up to 31.99%). The proposed application shows promise in both operating room applications and surgical training tasks.

Automated Upper Tract Urothelial Carcinoma Tumor Segmentation During Ureteroscopy Using Computer Vision Techniques.

Introduction: Endoscopic tumor ablation of upper tract urothelial carcinoma (UTUC) allows for tumor control with the benefit of renal preservation but is impacted by intraoperative visibility. We sought to develop a computer vision model for real-time, automated segmentation of UTUC tumors to augment visualization during treatment. Materials and Methods: We collected 20 videos of endoscopic treatment of UTUC from two institutions. Frames from each video (N = 3387) were extracted and manually annotated to identify tumors and areas of ablated tumor. Three established computer vision models (U-Net, U-Net++, and UNext) were trained using these annotated frames and compared. Eighty percent of the data was used to train the models while 10% was used for both validation and testing. We evaluated the highest performing model for tumor and ablated tissue segmentation using a pixel-based analysis. The model and a video overlay depicting tumor segmentation were further evaluated intraoperatively. Results: All 20 videos (mean 36 ± 58 seconds) demonstrated tumor identification and 12 depicted areas of ablated tumor. The U-Net model demonstrated the best performance for segmentation of both tumors (area under the receiver operating curve [AUC-ROC] of 0.96) and areas of ablated tumor (AUC-ROC of 0.90). In addition, we implemented a working system to process real-time video feeds and overlay model predictions intraoperatively. The model was able to annotate new videos at 15 frames per second. Conclusions: Computer vision models demonstrate excellent real-time performance for automated upper tract urothelial tumor segmentation during ureteroscopy.

A Safe Framework for Quantitative In Vivo Human Evaluation of Image Guidance.

Goal: We present a new framework for in vivo image guidance evaluation and provide a case study on robotic partial nephrectomy. Methods: This framework (called the "bystander protocol") involves two surgeons, one who solely performs the therapeutic process without image guidance, and another who solely periodically collects data to evaluate image guidance. This isolates the evaluation from the therapy, so that in-development image guidance systems can be tested without risk of negatively impacting the standard of care. We provide a case study applying this protocol in clinical cases during robotic partial nephrectomy surgery. Results: The bystander protocol was performed successfully in 6 patient cases. We find average lesion centroid localization error with our IGS system to be 6.5 mm in vivo compared to our prior result of 3.0 mm in phantoms. Conclusions: The bystander protocol is a safe, effective method for testing in-development image guidance systems in human subjects.

Are 3D Image Guidance Systems Ready for Use? A Comparative Analysis of 3D Image Guidance Implementations in Minimally Invasive Partial Nephrectomy.

Introduction: Three-dimensional image-guided surgical (3D-IGS) systems for minimally invasive partial nephrectomy (MIPN) can potentially improve the efficiency and accuracy of intraoperative anatomical localization and tumor resection. This review seeks to analyze the current state of research regarding 3D-IGS, including the evaluation of clinical outcomes, system functionality, and qualitative insights regarding 3D-IGS's impact on surgical procedures. Methods: We have systematically reviewed the clinical literature pertaining to 3D-IGS deployed for MIPN. For inclusion, studies must produce a patient-specific 3D anatomical model from two-dimensional imaging. Data extracted from the studies include clinical results, registration (alignment of the 3D model to the surgical scene) method used, limitations, and data types reported. A subset of studies was qualitatively analyzed through an inductive coding approach to identify major themes and subthemes across the studies. Results: Twenty-five studies were included in the review. Eight (32%) studies reported clinical results that point to 3D-IGS improving multiple surgical outcomes. Manual registration was the most utilized (48%). Soft tissue deformation was the most cited limitation among the included studies. Many studies reported qualitative statements regarding surgeon accuracy improvement, but quantitative surgeon accuracy data were not reported. During the qualitative analysis, six major themes emerged across the nine applicable studies. They are as follows: 3D-IGS is necessary, 3D-IGS improved surgical outcomes, researcher/surgeon confidence in 3D-IGS system, enhanced surgeon ability/accuracy, anatomical explanation for qualitative assessment, and claims without data or reference to support. Conclusions: Currently, clinical outcomes are the main source of quantitative data available to point to 3D-IGS's efficacy. However, the literature qualitatively suggests the benefit of accurate 3D-IGS for robotic partial nephrectomy.

Machine Learning Models to Predict Kidney Stone Recurrence Using 24 Hour Urine Testing and Electronic Health Record-Derived Features.

Objective: To assess the accuracy of machine learning models in predicting kidney stone recurrence using variables extracted from the electronic health record (EHR). Methods: We trained three separate machine learning (ML) models (least absolute shrinkage and selection operator regression [LASSO], random forest [RF], and gradient boosted decision tree [XGBoost] to predict 2-year and 5-year symptomatic kidney stone recurrence from electronic health-record (EHR) derived features and 24H urine data (n = 1231). ML models were compared to logistic regression [LR]. A manual, retrospective review was performed to evaluate for a symptomatic stone event, defined as pain, acute kidney injury or recurrent infections attributed to a kidney stone identified in the clinic or the emergency department, or for any stone requiring surgical treatment. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified important features for each model. Results: The 2- and 5- year symptomatic stone recurrence rates were 25% and 31%, respectively. The LASSO model performed best for symptomatic stone recurrence prediction (2-yr AUC: 0.62, 5-yr AUC: 0.63). Other models demonstrated modest overall performance at 2- and 5-years: LR (0.585, 0.618), RF (0.570, 0.608), and XGBoost (0.580, 0.621). Patient age was the only feature in the top 5 features of every model. Additionally, the LASSO model prioritized BMI and history of gout for prediction. Conclusions: Throughout our cohorts, ML models demonstrated comparable results to that of LR, with the LASSO model outperforming all other models. Further model testing should evaluate the utility of 24H urine features in model structure.

Quick Sequential Organ Failure Assessment Score Is a Better Predictor of Septic Shock After Percutaneous Nephrolithotomy: A Secondary Analysis of Two Multicenter Prospective Trials.

Introduction: Recent retrospective literature suggests that the quick sequential organ failure assessment (qSOFA) scoring tool is a potentially superior tool over use of the systemic inflammatory response syndrome (SIRS) criteria to predict septic shock after percutaneous nephrolithotomy (PCNL) surgery. Here we examine use of qSOFA and SIRS to predict septic shock within data series collected prospectively on PCNL patients as part of a greater study of infectious complications. Materials and Methods: We performed a secondary analysis of two prospective multicenter studies including PCNL patients across nine institutions. Clinical signs informing SIRS and qSOFA scores were collected no later than postoperative day 1. The primary outcome was sensitivity and specificity of SIRS and qSOFA (high-risk score of greater-or-equal to two points) in predicting admission to the intensive care unit (ICU) for vasopressor support. Results: A total of 218 cases at 9 institutions were analyzed. One patient required vasopressor support in the ICU. The sensitivity/specificity was 100%/72.4% (McNemar's test p < 0.001) for SIRS and was 100%/90.8% (McNemar's test p < 0.001) for qSOFA. Conclusion: Although positive predictive value for both qSOFA and SIRS in prediction of post-PCNL septic shock is low, prospectively collected data demonstrate use of qSOFA may offer greater specificity than SIRS criteria when predicting post-PCNL septic shock.

Eye gaze metrics for skill assessment and feedback in kidney stone surgery.

PURPOSE: Surgical skill assessment is essential for safe operations. In endoscopic kidney stone surgery, surgeons must perform a highly skill-dependent mental mapping from the pre-operative scan to the intraoperative endoscope image. Poor mental mapping can lead to incomplete exploration of the kidney and high reoperation rates. Yet there are few objective ways to evaluate competency. We propose to use unobtrusive eye-gaze measurements in the task space to evaluate skill and provide feedback. METHODS: We capture the surgeons' eye gaze on the surgical monitor with the Microsoft Hololens 2. To enable stable and accurate gaze detection, we develop a calibration algorithm to refine the eye tracking of the Hololens. In addition, we use a QR code to locate the eye gaze on the surgical monitor. We then run a user study with three expert and three novice surgeons. Each surgeon is tasked to locate three needles representing kidney stones in three different kidney phantoms. RESULTS: We find that experts have more focused gaze patterns. They complete the task faster, have smaller total gaze area, and the gaze fewer times outside the area of interest. While fixation to non-fixation ratio did not show significant difference in our findings, tracking the ratio over time shows different patterns between novices and experts. CONCLUSION: We show that a non-negligible difference holds between novice and expert surgeons' gaze metrics in kidney stone identification in phantoms. Expert surgeons demonstrate more targeted gaze throughout a trial, indicating their higher level of proficiency. To improve the skill acquisition process for novice surgeons, we suggest providing sub-task specific feedback. This approach presents an objective and non-invasive method to assess surgical competence.

Feasibility of stone recurrence risk stratification using the recurrence of kidney stone (ROKS) nomogram.

This study seeks to evaluate the recurrence of kidney stones (ROKS) nomogram for risk stratification of recurrence in a retrospective study. To do this, we analyzed the performance of the 2018 ROKS nomogram in a case-control study of 200 patients (100 with and 100 without subsequent recurrence). All patients underwent kidney stone surgery between 2013 and 2015 and had at least 5 years of follow-up. We evaluated ROKS performance for prediction of recurrence at 2- and 5-year via area under the receiver operating curve (ROC-AUC). Specifically, we assessed the nomogram's potential for stratifying patients based on low or high risk of recurrence at: a) an optimized cutoff threshold (i.e., optimized for both sensitivity and specificity), and b) a sensitive cutoff threshold (i.e., high sensitivity (0.80) and low specificity). We found fair performance of the nomogram for recurrence prediction at 2 and 5 years (ROC-AUC of 0.67 and 0.63, respectively). At the optimized cutoff threshold, recurrence rates for the low and high-risk groups were 20 and 45% at 2 years, and 50 and 70% at 5 years, respectively. At the sensitive cutoff threshold, the corresponding recurrence rates for the low and high-risk groups were of 16 and 38% at 2 years, and 42 and 66% at 5 years, respectively. Kaplan-Meier analysis revealed a recurrence-free advantage between the groups for both cutoff thresholds (p < 0.01, Fig. 2). Therefore, we believe that the ROKS nomogram could facilitate risk stratification for stone recurrence and adherence to risk-based surveillance protocols.

Computer Vision Enabled Segmentation of Kidney Stones During Ureteroscopy and Laser Lithotripsy.

Objective: To evaluate the performance of computer vision models for automated kidney stone segmentation during flexible ureteroscopy and laser lithotripsy. Materials and Methods: We collected 20 ureteroscopy videos of intrarenal kidney stone treatment and extracted frames (N = 578) from these videos. We manually annotated kidney stones on each frame. Eighty percent of the data were used to train three standard computer vision models (U-Net, U-Net++, and DenseNet) for automatic stone segmentation during flexible ureteroscopy. The remaining data (20%) were used to compare performance of the three models after optimization through Dice coefficients and binary cross entropy. We identified the highest performing model and evaluated automatic segmentation performance during ureteroscopy for both stone localization and treatment using a separate set of endoscopic videos. We evaluated performance of the pixel-based analysis using area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and positive predictive value both in previously recorded videos and in real time. Results: A computer vision model (U-Net++) was evaluated, trained, and optimized for kidney stone segmentation during ureteroscopy using 20 surgical videos (mean video duration of 22 seconds, standard deviation ±13 seconds). The model showed good performance for stone localization with both digital ureteroscopes (AUC-ROC: 0.98) and fiberoptic ureteroscopes (AUC-ROC: 0.93). Furthermore, the model was able to accurately segment stones and stone fragments <270 µm in diameter during laser fragmentation (AUC-ROC: 0.87) and dusting (AUC-ROC: 0.77). The model automatically annotated videos intraoperatively in three cases and could do so in real time at 30 frames per second (FPS). Conclusion: Computer vision models demonstrate strong performance for automatic stone segmentation during ureteroscopy. Automatically annotating new videos at 30 FPS demonstrate the feasibility of real-time application during surgery, which could facilitate tracking tools for stone treatment.

Kidney Stone Prevalence Based on Self-Report and Electronic Health Records: Insight into the Prevalence of Active Medical Care for Kidney Stones.

OBJECTIVE: To compare rates of patient-reported kidney stone disease to Electronic Health Records (EHR) kidney stone diagnosis using a common dataset to evaluate for socio-demographic differences, including between those with and without active care. METHODS: From the All of Us research database, we identified 21,687 adult participants with both patient-reported and EHR data. We compared differences in age, sex, race, education, employment status and healthcare access between patients with self-reported kidney stone history without EHR data to those with EHR-based diagnoses. RESULTS: In this population, the self-reported prevalence of kidney stones was 8.6% overall (n = 1877), including 4.6% (n = 1004) who had self-reported diagnoses but no EHR data. Among those with self-reported kidney stone diagnoses only, the median age was 66. The EHR-based prevalence of kidney stones was 5.7% (n = 1231), median age 67. No differences were observed in age, sex, education, employment status, rural/urban status, or ability to afford healthcare between groups with EHR diagnosis or self-reported diagnosis only. Of patients who had a self-reported history of kidney stones, 24% reported actively seeing a provider for kidney stones. CONCLUSION: Kidney stone prevalence by self-report is higher than EHR-based prevalence in this national dataset. Using either method alone to estimate kidney stone prevalence may exclude some patients with the condition, although the demographic profile of both groups is similar. Approximately 1 in 4 patients report actively seeing a provider for stone disease.

Do Skills Naturally Transfer Between Multiport and Single-Port Robotic Platforms? A Comparative Study in a Simulated Environment.

Introduction and Objective: With introduction of the da Vinci single-port (SP) system, we evaluated which multiport (MP) robotic skills are naturally transferable to the SP platform. Methods: Three groups of urologists: Group 1 (5 inexperienced in MP and SP), Group 2 (5 experienced in MP without SP experience), and Group 3 (2 experienced in both MP and SP) were recruited to complete a validated urethrovesical anastomosis simulation using MP followed by SP robots. Performance was graded using both GEARS and RACE scales. Subjective cognitive load measurements (Surg-TLX and difficulty ratings [/20] of instrument collisions camera and EndoWrist movement) were collected. Results: GEARS and RACE scores for Groups 1 and 3 were maintained on switching from MP to SP (Group 3 scored significantly higher on both systems). Surg-TLX and difficulty scores were also maintained for both groups on switching from MP and SP except for a significant increase in SP camera movement (+7.2, p = 0.03) in Group 1 compared to Group 3 that maintained low scores on both. Group 2 demonstrated significant lower GEARS (-2.9, p = 0.047) and RACE (-5.1, p = 0.011) scores on SP vs MP. On subanalysis, GEARS subscores for force sensitivity and robotic control (-0.7, p = 0.04; -0.9, p = 0.02) and RACE subscores for needle entry, needle driving, and tissue approximation (-0.9, p = 0.01; -1.0, p = 0.02; -1.0, p < 0.01) significantly decreased. GEARS (depth perception, bimanual dexterity, and efficiency) and RACE subscores (needle positioning and suture placement) were maintained. All participants scored significantly lower in knot tying on the SP robot (-1.0, p = 0.03; -1.2, p = 0.02, respectively). Group 2 reported higher Surg-TLX (+13 pts, p = 0.015) and difficulty ratings on SP vs MP (+11.8, p < 0.01; +13.6, p < 0.01; +14 pts, p < 0.01). Conclusions: The partial skill transference across robots raises the question regarding SP-specific training for urologists proficient in MP. Novices maintained difficulty scores and cognitive load across platforms, suggesting that concurrent SP and MP training may be preferred.

Racial Disparities in Diagnosis and Management of Benign Prostatic Hyperplasia: A Review.

PURPOSE OF REVIEW: We sought to perform a contemporary literature review highlighting the racial disparities which exists in the evaluation and management of benign prostatic hyperplasia (BPH). RECENT FINDINGS: Current literature suggests that racial disparities exist in the diagnosis of BPH and treatment lower urinary tract symptoms (LUTS). This is seen in the presentation and diagnosis of the disease as well as a difference in preventative care with discordant incidences of medical and surgical management among racial groups. The racial disparities that exist in the diagnosis and management of BPH and LUTS require further investigation to better identify the underlying causes. This will ultimately allow for continued improvement in care delivery and a more personalized approach in patient management.

Machine Learning Models to Predict 24 Hour Urinary Abnormalities for Kidney Stone Disease.

OBJECTIVE: To help guide empiric therapy for kidney stone disease, we sought to demonstrate the feasibility of predicting 24-hour urine abnormalities using machine learning methods. METHODS: We trained a machine learning model (XGBoost [XG]) to predict 24-hour urine abnormalities from electronic health record-derived data (n = 1314). The machine learning model was compared to a logistic regression model [LR]. Additionally, an ensemble (EN) model combining both XG and LR models was evaluated as well. Models predicted binary 24-hour urine values for volume, sodium, oxalate, calcium, uric acid, and citrate; as well as a multiclass prediction of pH. We evaluated performance using area under the receiver operating curve (AUC-ROC) and identified predictors for each model. RESULTS: The XG model was able to discriminate 24-hour urine abnormalities with fair performance, comparable to LR. The XG model most accurately predicted abnormalities of urine volume (accuracy = 98%, AUC-ROC = 0.59), uric acid (69%, 0.73) and elevated urine sodium (71%, 0.79). The LR model outperformed the XG model alone in prediction of abnormalities of urinary pH (AUC-ROC of 0.66 vs 0.57) and citrate (0.69 vs 0.64). The EN model most accurately predicted abnormalities of oxalate (accuracy = 65%, ROC-AUC = 0.70) and citrate (65%, 0.69) with overall similar predictive performance to either XG or LR alone. Body mass index, age, and gender were the three most important features for training the models for all outcomes. CONCLUSION: Urine chemistry prediction for kidney stone disease appears to be feasible with machine learning methods. Further optimization of the performance could facilitate dietary or pharmacologic prevention.

Machine Learning Prediction of Kidney Stone Composition Using Electronic Health Record-Derived Features.

Objectives: To assess the accuracy of machine learning models in predicting kidney stone composition using variables extracted from the electronic health record (EHR). Materials and Methods: We identified kidney stone patients (n = 1296) with both stone composition and 24-hour (24H) urine testing. We trained machine learning models (XGBoost [XG] and logistic regression [LR]) to predict stone composition using 24H urine data and EHR-derived demographic and comorbidity data. Models predicted either binary (calcium vs noncalcium stone) or multiclass (calcium oxalate, uric acid, hydroxyapatite, or other) stone types. We evaluated performance using area under the receiver operating curve (ROC-AUC) and accuracy and identified predictors for each task. Results: For discriminating binary stone composition, XG outperformed LR with higher accuracy (91% vs 71%) with ROC-AUC of 0.80 for both models. Top predictors used by these models were supersaturations of uric acid and calcium phosphate, and urinary ammonium. For multiclass classification, LR outperformed XG with higher accuracy (0.64 vs 0.56) and ROC-AUC (0.79 vs 0.59), and urine pH had the highest predictive utility. Overall, 24H urine analyte data contributed more to the models' predictions of stone composition than EHR-derived variables. Conclusion: Machine learning models can predict calcium stone composition. LR outperforms XG in multiclass stone classification. Demographic and comorbidity data are predictive of stone composition; however, including 24H urine data improves performance. Further optimization of performance could lead to earlier directed medical therapy for kidney stone patients.

Machine learning applications to enhance patient specific care for urologic surgery.

PURPOSE: As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care. METHODS: We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms "urologic surgery" as well as "artificial intelligence", "machine learning", "neural network", and "automation" were used. RESULTS: The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy. CONCLUSION: Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.

Patient-specific, touch-based registration during robotic, image-guided partial nephrectomy.

Image-guidance during partial nephrectomy enables navigation within the operative field alongside a 3-dimensional roadmap of renal anatomy generated from patient-specific imaging. Once a process is performed by the human mind, the technology will allow standardization of the task for the benefit of all patients undergoing robot-assisted partial nephrectomy. Any surgeon will be able to visualize the kidney and key subsurface landmarks in real-time within a 3-dimensional simulation, with the goals of improving operative efficiency, decreasing surgical complications, and improving oncologic outcomes. For similar purposes, image-guidance has already been adopted as a standard of care in other surgical fields; we are now at the brink of this in urology. This review summarizes touch-based approaches to image-guidance during partial nephrectomy, as the technology begins to enter in vivo human evaluation. The processes of segmentation, localization, registration, and re-registration are all described with seamless integration into the da Vinci surgical system; this will facilitate clinical adoption sooner.

MOSESTM Technology for Holmium Laser Enucleation of the Prostate: A Prospective Double-Blind Randomized Controlled Trial.

PURPOSE: Holmium laser enucleation of the prostate has proven to be efficacious and safe for the treatment of benign prostatic hyperplasia. New laser technologies, such as the MOSES™ pulse laser system, improve energy delivery and may improve operative times. We sought to prospectively evaluate holmium laser enucleation of the prostate using MOSES technology in a double-blind randomized controlled trial. MATERIALS AND METHODS: This is a single-center, prospective, double-blind, randomized controlled trial comparing holmium laser enucleation of the prostate using MOSES technology to holmium laser enucleation of the prostate. Patients were randomized in a 1:1 fashion. The study was powered to evaluate for a difference in operative time. Secondary end points included enucleation, morcellation, and hemostasis times, as well as blood loss, functional outcomes and complications 6 weeks postoperatively. RESULTS: A total of 60 patients were analyzed without difference in preoperative characteristics in either group (holmium laser enucleation of the prostate using MOSES technology: 30/60, 50%, holmium laser enucleation of the prostate: 30/60, 50%). Shorter total operative time was seen in the holmium laser enucleation of the prostate using MOSES technology group compared to the holmium laser enucleation of the prostate group (mean: 101 vs. 126 minutes, p <0.01). This difference remained significant on multiple linear regression. Additionally, the holmium laser enucleation of the prostate using MOSES technology group had shorter enucleation times (mean: 68 vs. 80 minutes, p=0.03), hemostasis time (mean: 18 vs. 29 minutes, p <0.01), and less blood loss (mean: -6.3 vs. -9.0%, p=0.03), measured by a smaller change in hematocrit postoperatively, compared to the traditional holmium laser enucleation of the prostate. There was no difference in functional or safety outcomes at followup. CONCLUSIONS: We report the results of a prospective, double-blind, randomized controlled trial comparing holmium laser enucleation of the prostate using MOSES technology to traditional holmium laser enucleation of the prostate. MOSES technology resulted in an improvement in operative time and a reduction in blood loss with comparable functional outcomes and complications compared to traditional holmium laser enucleation of the prostate.