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.

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.

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.

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 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.

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.

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.

Accuracy of Touch-Based Registration During Robotic Image-Guided Partial Nephrectomy Before and After Tumor Resection in Validated Phantoms.

Aim: Image-guided surgery (IGS) allows for accurate, real-time localization of subsurface critical structures during surgery. No prior IGS systems have described a feasible method of intraoperative reregistration after manipulation of the kidney during robotic partial nephrectomy (PN). We present a method for seamless reregistration during IGS and evaluate accuracy before and after tumor resection in two validated kidney phantoms. Materials and Methods: We performed robotic PN on two validated kidney phantoms-one with an endophytic tumor and one with an exophytic tumor-with our IGS system utilizing the da Vinci Xi robot. Intraoperatively, the kidney phantoms' surfaces were digitized with the da Vinci robotic manipulator via a touch-based method and registered to a three-dimensional segmented model created from cross-sectional CT imaging of the phantoms. Fiducial points were marked with a surgical marking pen and identified after the initial registration using the robotic manipulator. Segmented images were displayed via picture-in-picture in the surgeon console as tumor resection was performed. After resection, reregistration was performed by reidentifying the fiducial points. The accuracy of the initial registration and reregistration was compared. Results: The root mean square (RMS) averages of target registration error (TRE) were 2.53 and 4.88 mm for the endophytic and exophytic phantoms, respectively. IGS enabled resection along preplanned contours. Specifically, the RMS averages of the normal TRE over the entire resection surface were 0.75 and 2.15 mm for the endophytic and exophytic phantoms, respectively. Both tumors were resected with grossly negative margins. Point-based reregistration enabled instantaneous reregistration with minimal impact on RMS TRE compared with the initial registration (from 1.34 to 1.70 mm preresection and from 1.60 to 2.10 mm postresection). Conclusions: We present a novel and accurate registration and reregistration framework for use during IGS for PN with the da Vinci Xi surgical system. The technology is easily integrated into the surgical workflow and does not require additional hardware.

Transurethral Anastomosis after Transurethral Radical Prostatectomy: A Phantom Study on Intraluminal Suturing With Concentric Tube Robots.

Current surgical approaches to radical prostatectomy are associated with high rates of erectile dysfunction and incontinence. These complications occur secondary to the disruption of surrounding healthy tissue, which is required to expose the prostate. The urethra offers the least invasive access to the prostate, and feasibility has been demonstrated of enucleating the prostate with an endoscope using Holmium laser, which can itself be aimed by concentric tube robots. However, the transurethral approach to radical prostatectomy has thus far been limited by the lack of a suitable means to perform an anastomosis of the urethra to the bladder after prostate removal. Only a few intraluminal anastomotic devices currently exist, and none are small enough to pass through the urethra. In this paper we describe a new way to perform an anastomosis in the small luminal space of the urethra, harnessing the dexterity and customizability of concentric tube manipulators. We demonstrate a successful initial proof-of-concept anastomosis in an anthropomorphic phantom of the urethra and bladder.

Comparing the accuracy of the da Vinci Xi and da Vinci Si for image guidance and automation.

BACKGROUND: Current laparoscopic surgical robots are teleoperated, which requires high fidelity differential motions but does not require absolute accuracy. Emerging applications, including image guidance and automation, require absolute accuracy. The absolute accuracy of the da Vinci Xi robot has not yet been characterized or compared to the Si system, which is now being phased out. This study compares the accuracy of the two. METHODS: We measure robot tip positions and encoder values assessing accuracy with and without robot calibration. RESULTS: The Si is accurate if the setup joints are not moved but loses accuracy otherwise. The Xi is always accurate. CONCLUSION: The Xi can achieve submillimetric average error. Calibration improves accuracy, but excellent baseline accuracy of the Xi means that calibration may not be needed for some applications. Importantly, the external tracking systems needed to account for setup joint error in the Si are no longer required with the Xi.

Association of Chronic Kidney Disease Stage with 24-Hour Urine Values Among Patients with Nephrolithiasis.

Introduction: Nephrolithiasis is a known risk factor for chronic kidney disease (CKD); however, it is unknown how CKD affects urinary parameters related to stone risk. The purpose of this study was to assess the relationship of diminishing glomerular filtration rate (GFR) and kidney stone-related 24-hour urine (24H urine) composition. Materials and Methods: A single-institution retrospective review of patients (n = 2057) who underwent 24H urine analysis was performed. The serum creatinine within 1 year of the first 24H urine was used to determine estimated GFR and stratify patients by CKD stage. We performed analysis of variance and multivariable linear regression to assess the relationship of GFR and urinary analytes. Results: Among all patients, there were 184 (8.9%), 1537 (74.7%), 245 (11.9%), 70 (3.4%), 17 (0.8%), and 4 (0.2%) in CKD stage I, II, IIIa, IIIb, IV, and V groups, respectively. On analysis of 24H urine composition, as CKD increased, changes in urinary parameters protective against crystallization included decreased calcium and uric acid (UA) (P < 0.001). In addition, parameters favoring crystallization included decreased citrate and magnesium (P = 0.002 and P < 0.001, respectively). The net effect with increasing GFR was decreasing supersaturation of calcium oxalate and phosphate. On linear regression, urinary excretion of calcium, oxalate, citrate, UA, phosphate, and ammonia all decreased with decreasing GFR (all P < 0.05). Conclusions: Higher CKD stage was associated with changes in urinary analytes that both promoted and inhibited stone formation, with the net effect of decreasing calcium oxalate and phosphate supersaturation. These patients may benefit from medical therapy that targets improving urinary citrate instead of lowering calcium or UA.

Design and validation of a low-cost, high-fidelity model for robotic pyeloplasty simulation training.

INTRODUCTION/BACKGROUND: Owing to restrictions in operative experiences, urology residents can no longer solely rely on 'hands-on' operative time to master their surgical skills by the end of residency. Simulation training could help residents master basic surgical skills and steps of a procedure to maximize time in the operative room. However, simulators can be expensive or tedious to set up, limiting the availability to residents and training programs. OBJECTIVE: The authors sought to develop and validate an inexpensive, high-fidelity training model for robotic pyeloplasty. STUDY DESIGN: Pyeloplasty models were created using Dragon Skin® FX-Pro tissue-mimicking silicone cast over 3-dimensional molds. Urology faculty and trainees completed a demographic questionnaire. The participants viewed a brief instructional video and then independently performed robotic dismembered pyeloplasty on the model. Acceptability and content validity were evaluated via post-task evaluation of the model. Construct validity was evaluated by comparing procedure completion time, the Global Evaluative Assessment of Robotic Skills (GEARS) score, blinded subjective physical evaluation of repair quality (1-10 scale), and flow rate between experts and novices. RESULTS: In total, 5 urology faculty, 6 fellows, and 14 residents participated. The median robotic console experience among faculty, fellows, and residents was 8 years (interquartile range [IQR] = 6-11), 3.5 years (IQR = 2-4 years), and 0 years (IQR = 0-0.5 years), respectively. The median procedure completion time was 29 min (IQR = 26-40 min), and the median flow rate was 1.11 mL/s (IQR = 0-1.34 mL/s). All faculty had flow rates >1.25 mL/s and procedure times <30 min compared with 2 of 6 fellows and none of the residents (P < 0.001). All faculty, half of the fellows, and none of the residents achieved a GEARS score ≥20, with a median resident score of 12.5 (IQR = 8-13) (P < 0.001). For repair quality, all faculty scored ≥9 (out of 10), all fellows scored ≥8, and the median score among residents was 6 (IQR = 2-6) (P < 0.001). The material cost was $1.32/model, and the average production time was 0.12 person-hours/model. DISCUSSION AND CONCLUSION: This low-cost pyeloplasty model exhibits acceptability and content validity. Construct validity is supported by significant correlation between participant expertise and simulator performance across multiple assessment domains. The model has excellent potential to be used as a training tool in urology and allows for repetitive practice of pyeloplasty skills before live cases.

Toward Practical and Accurate Touch-Based Image Guidance for Robotic Partial Nephrectomy.

Partial nephrectomy involves removing a tumor while sparing surrounding healthy kidney tissue. Compared to total kidney removal, partial nephrectomy improves outcomes for patients but is underutilized because it is challenging to accomplish minimally invasively, requiring accurate spatial awareness of unseen subsurface anatomy. Image guidance can enhance spatial awareness by displaying a 3D model of anatomical relationships derived from medical imaging information. It has been qualitatively suggested that the da Vinci robot is well suited to facilitate image guidance through touch-based registration. In this paper we validate and advance this concept toward real-world use in several important ways. First, we contribute the first quantitative accuracy evaluation of touch-based registration with the da Vinci. Next, we demonstrate real-time touch-based registration and display of medical images for the first time. Lastly, we perform the first experiments validating use of touch-based image guidance to improve a surgeon's ability to localize subsurface anatomical features in a geometrically realistic phantom.

Are Urine Cultures Necessary Prior to Urologic Prosthetic Surgery?

INTRODUCTION: A preoperative negative urine culture is generally advised before implantation of urologic prosthetics to prevent device infection. However, a review of the medical literature indicates sparse evidence to support this practice. AIM: To describe outcomes for patients undergoing prosthetic implantation without preoperative urine cultures. METHODS: The cases of men undergoing artificial urinary sphincter (AUS) and/or inflatable penile prosthesis (IPP) placement at a tertiary care center from 2007 through 2015 were reviewed. Of 713 devices implanted in 681 patients (337 AUSs in 314 patients, 376 IPPs in 367), 259 cases without preoperative urine cultures were analyzed (41%). Patients received standard perioperative antibiotics. MAIN OUTCOME MEASURES: Device infection was diagnosed clinically. Average follow-up was 15 months. RESULTS: Device infection occurred in 4 of 259 patients (1.5%) with no difference noted in infection rate between device groups (AUS = 3 of 174 [2%]; IPP = 1 of 85 [1%]; P = .99); this rate appears to be consistent with the infection rate of numerous other published prosthetic series. Common skin organisms were implicated as the infectious agents in half the infected devices. Only one patient (0.4%) developed an Escherichia coli infection. CONCLUSION: This study suggests that prosthetic urologic surgery can be safely performed without preoperative urine cultures. Kavoussi NL, Viers BR, Pagilara TL, et al. Are Urine Cultures Necessary Prior to Urologic Prosthetic Surgery? Sex Med Rev 2018;6:157-161.

"Stiction Syndrome": Non-Operative Management of Patients With Difficult AMS 700 Series Inflation.

INTRODUCTION: Static friction (stiction) is a mechanical phenomenon in which a state of increased resistance exists across a control valve mechanism. AIM: To present a strategy for non-operative management of inflatable penile prosthesis (IPP) cases with pump malfunction from pump valve stiction. METHODS: All patients had American Medical Systems (AMS; Minnetonka, MN, USA) 700 series Momentary Squeeze IPPs with transient pump malfunction owing to pump valve stiction after extended periods of device inactivity. MAIN OUTCOME MEASURES: Our evolving non-operative management experience with the "forced deflation" maneuver is described. This technique has successfully prevented the need for surgical pump replacement. Of patients with IPP who were instructed to inflate and deflate daily to prevent stiction recurrence, none have re-presented with difficult inflation. RESULTS: Of 306 patients receiving the AMS 700 series IPP at our institution from 2007 through 2015, 6 (1.9%) presented with difficulty activating the Momentary Squeeze pump (from 2011 through 2015). Four additional patients were referred from outside institutions with the same complaint. All patients (10 of 10, 100%) presented after a prolonged period of inactivity (minimum = 6 weeks) during which the IPP was not cycled and remained stagnant. Although the initial four patients (40%) underwent surgical exploration with pump mobilization and replacement, the six most recent patients (60%) were successfully instructed in the forced deflation maneuver in the office, which enabled the device to cycle normally thereafter. CONCLUSION: Device inactivity, for as little as 6 weeks, can predispose to Momentary Squeeze pump valve malfunction; and a regimen of daily IPP cycling could prevent stiction-related malfunction. Our findings should encourage practitioners to attempt conservative management of patients with "stiction syndrome" whenever possible, thereby avoiding unnecessary surgery. Kavoussi NL, Viers BR, VanDyke ME, et al. "Stiction Syndrome": Non-Operative Management of Patients With Difficult AMS 700 Series Inflation. J Sex Med 2017;14:1079-1083.