Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence.

BACKGROUND: The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. METHODS: A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles. RESULTS: The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels. CONCLUSION: Our study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

An Efficient Framework for Video Documentation of Bladder Lesions for Cystoscopy: A Proof-of-Concept Study.

Processing full-length cystoscopy videos is challenging for documentation and research purposes. We therefore designed a surgeon-guided framework to extract short video clips with bladder lesions for more efficient content navigation and extraction. Screenshots of bladder lesions were captured during transurethral resection of bladder tumor, then manually labeled according to case identification, date, lesion location, imaging modality, and pathology. The framework used the screenshot to search for and extract a corresponding 10-seconds video clip. Each video clip included a one-second space holder with a QR barcode informing the video content. The success of the framework was measured by the secondary use of these short clips and the reduction of storage volume required for video materials. From 86 cases, the framework successfully generated 249 video clips from 230 screenshots, with 14 erroneous video clips from 8 screenshots excluded. The HIPPA-compliant barcodes provided information of video contents with a 100% data completeness. A web-based educational gallery was curated with various diagnostic categories and annotated frame sequences. Compared with the unedited videos, the informative short video clips reduced the storage volume by 99.5%. In conclusion, our framework expedites the generation of visual contents with surgeon's instruction for cystoscopy and potential incorporation of video data towards applications including clinical documentation, education, and research.

Risk of Melanoma With Phosphodiesterase Type 5 Inhibitor Use Among Patients With Erectile Dysfunction, Pulmonary Hypertension, and Lower Urinary Tract Symptoms.

BACKGROUND: Phosphodiesterase type 5 inhibitors (PDE5is), a treatment for erectile dysfunction, pulmonary hypertension (pHTN), and lower urinary tract symptoms (LUTS), have been implicated in melanoma development. AIM: We sought to determine the association between PDE5i use and melanoma development among patients with erectile dysfunction, pHTN, and LUTS. METHODS: This was a retrospective cohort study of subjects contained within the Truven Health MarketScan claims database, which provides information on insurance claims in the United States for privately insured individuals, from 2007-2015. Individuals taking PDE5i were identified through pharmacy claims. A comparison group of men diagnosed with conditions for which PDE5i are prescribed was assembled. OUTCOMES: Cox proportional hazard models were used to estimate the hazard ratio (HR) (95% CI) of incident melanoma, basal cell carcinoma, and squamous cell carcinoma. RESULTS: Of 610,881 subjects prescribed PDE5i, 636 developed melanoma (0.10%). The control group had 8,711 diagnoses of melanoma. There was an association between increased PDE5i tablet use and melanoma (HR 1.05, 95% CI 1.05-1.09). This association was also present between PDE5i use and basal cell carcinoma (HR 1.04, 95% CI 1.02-1.07) and squamous cell carcinoma (HR 1.04, 95% CI 1.01-1.07). In patients with pHTN and LUTS prescribed PDE5is, there was no relationship between exposure and melanoma incidence (HR 0.74, 95% CI 0.48-1.13; and HR 1.03, 95% CI 0.97-1.10, respectively). CLINICAL IMPLICATIONS: There is little evidence for a clinically relevant association between PDE5i use and melanoma incidence. STRENGTHS & LIMITATIONS: Our current work represents the largest study to date evaluating the relationship between PDE5i use and melanoma risk, and the first to examine all current indications of PDE5i use among men and women. Limitations include a patient population limited to commercially insured individuals, unknown patient medication compliance, and lack of information on patient skin type, lifestyle, and sun-exposure habits. CONCLUSION: There is a slight association between higher-volume PDE5i use and development of melanoma, basal cell carcinoma, and squamous cell carcinoma. This association among all skin cancers implies that confounding may account for the observed association. Shkolyar E, Li S, Tang J, et al. Risk of Melanoma With Phosphodiesterase Type 5 Inhibitor Use Among Patients With Erectile Dysfunction, Pulmonary Hypertension, and Lower Urinary Tract Symptoms. J Sex Med 2018;15:982-989.

Teaching mid-urethral sling surgery to residents: Impact on operative time and postoperative outcomes.

AIMS: The purpose of this study was to determine the impact of resident teaching on outcomes of mid-urethral sling surgery. METHODS: A retrospective review of female patients who underwent an outpatient transobturator (TOT) synthetic mid-urethral sling procedure with and without concomitant prolapse repair by two surgeons (JA, KE) in a tertiary female pelvic medicine practice was performed. Total procedure time (TPT = time from incision to closure including sling placement and any prolapse procedure), estimated blood loss (EBL), and postoperative complications including urinary retention, mesh exposure, reoperation, vaginal bleeding, and leg pain were compared between cases with and without the presence of a resident. RESULTS: One hundred thirty-four women underwent an outpatient transobturator sling procedure. Fifty-seven patients (43%) had a concomitant prolapse procedure. A resident was present at 57% (76/134) of cases. The average observed TPT (±SEM) was 60.6 ± 3.1 min when a resident was present and 46.6 ± 2.5 min when a resident was not present (P = 0.001). However, residents were more likely to be present when concomitant procedures were performed (P = 0.003). After adjusting for this, the presence of a resident increased TPT by an estimated 7.9 ± 2.5 min (P = 0.002). There was no statistical difference in EBL or postoperative complications. CONCLUSIONS: Resident participation in transobturator sling procedures resulted in a statistically significant, although clinically small, increase in operative time and had no significant impact on EBL or postoperative complications.

Impact of post prostate biopsy hemorrhage on multiparametric magnetic resonance imaging.

INTRODUCTION: Hemorrhage induced by prostate biopsy can interfere with the interpretation of prostate magnetic resonance imaging (MRI). MATERIALS AND METHODS: We reviewed 101 patients who had prostate multiparametric MRI (MP-MRI) and radical prostatectomy. RESULTS: On MRI obtained within 4 weeks following the biopsy, hemorrhage was seen in 26/36 (72.2%) patients. Patients having a MRI between 4-6 weeks of the biopsy had hemorrhage in 8/14 (57.1%) cases. After 6 weeks, hemorrhage was less common but still present in 24/46 (52%) patients. There were five patients who had prostate MRI prior to biopsy and served as a control group. There was no significant correlation between the length of time beyond 6 weeks and the likelihood of having prostate hemorrhage on MRI. The overall sensitivity and specificity of MRI for predicting extracapsular extension (ECE) were 78.6% and 89%, respectively. However, if the analysis was limited to patients with MRI within 6 weeks from the time of biopsy, the sensitivity and specificity were similar: 80% and 90%, respectively. For patients with MRI obtained after 6 weeks, the sensitivity and specificity were 76.9% and 87.9%. CONCLUSIONS: Prostate hemorrhage is seen in the majority of cases within 6 weeks of biopsy and can be seen in nearly half the patients even beyond 6 weeks. However, hemorrhage within 6 weeks of a biopsy does not interfere with assessment for ECE.

In vitro evaluation of an external compression device for fontan mechanical assistance.

While Fontan palliation in the form of the total cavopulmonary connection has improved the management of congenital single ventricle physiology, long-term outcomes for patients with this disease are suboptimal due to the lack of two functional ventricles. Researchers have shown that ventricular assist devices (VADs) can normalize Fontan hemodynamics. To minimize blood contacting surfaces of the VAD, we evaluated the use of an external compression device (C-Pulse Heart Assist System, Sunshine Heart Inc.) as a Fontan assist device. A mock circulation was developed to mimic the hemodynamics of a hypertensive Fontan circulation in a pediatric patient. The Sunshine C-Pulse compression cuff was coupled with polymeric valves and a compressible tube to provide nonblood-contacting pulsatile flow through the Fontan circulation. The effect of the number, one or two, and placement of valves, before or after the compression cuff, on inferior vena cava pressure (IVCP) was studied. In addition, the effect of device inflation volume and compression rate on maintaining low IVCP was investigated. With one valve located before the cuff, the device was unable to maintain an IVCP below 15.5 mm Hg. With two valves, the C-Pulse was able to maintain IVCP as low as 8.5 mm Hg. The C-Pulse provided pulsatile flow and pressure through the pulmonary branch of the mock circulation with a pulse pressure of 16 mm Hg and 180 mL/min additional flow above unassisted flow. C-Pulse compression reduced IVCP below 12 mm Hg with 13 cc inflation volume and compression rates above 105 bpm. This application of an external compression device combined with two valves has potential for use as an artificial right ventricle by maintaining low IVCP and providing pulsatile flow through the lungs.

Tumor detection under cystoscopy with transformer-augmented deep learning algorithm.

Objective.Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive and cost-effective fashion. The purpose of this work is to develop a cost-effective, transformer-augmented deep learning algorithm for accurate detection of bladder tumors in WLC and to assess its performance on archived patient data.Approach.'CystoNet-T', a deep learning-based bladder tumor detector, was developed with a transformer-augmented pyramidal CNN architecture to improve automated tumor detection of WLC. CystoNet-T incorporated the self-attention mechanism by attaching transformer encoder modules to the pyramidal layers of the feature pyramid network (FPN), and obtained multi-scale activation maps with global features aggregation. Features resulting from context augmentation served as the input to a region-based detector to produce tumor detection predictions. The training set was constructed by 510 WLC frames that were obtained from cystoscopy video sequences acquired from 54 patients. The test set was constructed based on 101 images obtained from WLC sequences of 13 patients.Main results.CystoNet-T was evaluated on the test set with 96.4 F1 and 91.4 AP (Average Precision). This result improved the benchmark of Faster R-CNN and YOLO by 7.3 points in F1 and 3.8 points in AP. The improvement is attributed to the strong ability of global attention of CystoNet-T and better feature learning of the pyramids architecture throughout the training. The model was found to be particularly effective in highlighting the foreground information for precise localization of the true positives while favorably avoiding false alarmsSignificance.We have developed a deep learning algorithm that accurately detects bladder tumors in WLC. Transformer-augmented AI framework promises to aid in clinical decision-making for improved bladder cancer diagnosis and therapeutic guidance.

Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence.

BACKGROUND: The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. METHODS: A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, re-usable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure the compliance with FAIR principles. RESULTS: The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion and frame levels. CONCLUSION: Our study shows that the proposed framework facilitates the storage of the visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: a Pilot Study.

BACKGROUND: Detection of bladder tumors under white light cystoscopy (WLC) is challenging yet impactful on treatment outcomes. Artificial intelligence (AI) holds the potential to improve tumor detection; however, its application in the real-time setting remains unexplored. AI has been applied to previously recorded images for post hoc analysis. In this study, we evaluate the feasibility of real-time AI integration during clinic cystoscopy and transurethral resection of bladder tumor (TURBT) on live, streaming video. METHODS: Patients undergoing clinic flexible cystoscopy and TURBT were prospectively enrolled. A real-time alert device system (real-time CystoNet) was developed and integrated with standard cystoscopy towers. Streaming videos were processed in real time to display alert boxes in sync with live cystoscopy. The per-frame diagnostic accuracy was measured. RESULTS AND LIMITATIONS: Real-time CystoNet was successfully integrated in the operating room during TURBT and clinic cystoscopy in 50 consecutive patients. There were 55 procedures that met the inclusion criteria for analysis including 21 clinic cystoscopies and 34 TURBTs. For clinic cystoscopy, real-time CystoNet achieved per-frame tumor specificity of 98.8% with a median error rate of 3.6% (range: 0 - 47%) frames per cystoscopy. For TURBT, the per-frame tumor sensitivity was 52.9% and the per-frame tumor specificity was 95.4% with an error rate of 16.7% for cases with pathologically confirmed bladder cancers. CONCLUSIONS: The current pilot study demonstrates the feasibility of using a real-time AI system (real-time CystoNet) during cystoscopy and TURBT to generate active feedback to the surgeon. Further optimization of CystoNet for real-time cystoscopy dynamics may allow for clinically useful AI-augmented cystoscopy.

Efficient Augmented Intelligence Framework for Bladder Lesion Detection.

PURPOSE: Development of intelligence systems for bladder lesion detection is cost intensive. An efficient strategy to develop such intelligence solutions is needed. MATERIALS AND METHODS: We used four deep learning models (ConvNeXt, PlexusNet, MobileNet, and SwinTransformer) covering a variety of model complexity and efficacy. We trained these models on a previously published educational cystoscopy atlas (n = 312 images) to estimate the ratio between normal and cancer scores and externally validated on cystoscopy videos from 68 cases, with region of interest (ROI) pathologically confirmed to be benign and cancerous bladder lesions (ie, ROI). The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. RESULTS: Specificity was comparable between four models at frame (range, 30.0%-44.8%) and block levels (56%-67%). Although sensitivity at the frame level (range, 81.4%-88.1%) differed between the models, sensitivity at the block level (100%) and ROI level (100%) was comparable between these models. MobileNet and PlexusNet were computationally more efficient for real-time ROI detection than ConvNeXt and SwinTransformer. CONCLUSION: Educational cystoscopy atlas and efficient models facilitate the development of real-time intelligence system for bladder lesion detection.

Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.

BACKGROUND: Prognostication is essential to determine the risk profile of patients with urologic cancers. METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

Current Trends in Artificial Intelligence Application for Endourology and Robotic Surgery.

With the advent of electronic medical records and digitalization of health care over the past 2 decades, artificial intelligence (AI) has emerged as an enabling tool to manage complex datasets and deliver streamlined data-driven patient care. AI algorithms have the ability to extract meaningful signal from complex datasets through an iterative process akin to human learning. Through advancements over the past decade in deep learning, AI-driven innovations have accelerated applications in health care. Herein, the authors explore the development of these emerging AI technologies, focusing on the application of AI to endourology and robotic surgery.

Bladder cancer risk stratification using a urinary mRNA biomarker panel - A path towards cystoscopy triaging.

OBJECTIVES: The risk of bladder cancer (BCa) diagnosis and recurrence necessitates cystoscopy. Improved risk stratification may inform personalized triage and surveillance strategies. We aim to develop a urinary mRNA biomarker panel for risk stratification in patients undergoing BCa screening and surveillance. METHODS AND MATERIALS: Urine samples were collected from patients undergoing cystoscopy for BCa screening or surveillance. In patients who underwent transurethral resection of bladder tumor, urine samples were categorized based on tumor histopathology, size, and focality. Subjects with intermediate and high-risk BCa based on American Urological Association (AUA) guideline for non-muscle invasive bladder cancer were classified as "increased-risk"; those with no cancer and AUA low-risk BCa were classified as "low-risk". Urine was evaluated for ROBO1, WNT5A, CDC42BPB, ABL1, CRH, IGF2, ANXA10, and UPK1B expression. A diagnostic model to detect "increased-risk" BCa was created using forward logistic regression analysis of cycle threshold values. Model validation was performed with ten-fold cross-validation. Sensitivity and specificity for detection of "increased-risk" BCa was determined and net benefit analysis performed. RESULTS: Urine samples (n = 257) were collected from 177 patients (95 screening, 76 surveillance, 6 both). There were 65 diagnoses of BCa (12 low, 22 intermediate, 31 high risk). ROBO1, CRH, and IGF2 expression correlated with "increased-risk" disease yielding sensitivity of 92.5% (95% CI, 84.9%-98.1%) and specificity of 73.5% (95% CI, 67.7-79.9%). The overall calculated standardized net benefit of the model was 0.81 (95%CI, 0.71-0.90). CONCLUSIONS: A 3-marker urinary mRNA panel allows for non-invasive identification of "increased-risk" BCa and with further validation may prove to be a tool to reduce the need for cystoscopies in low-risk patients.

Development of robust artificial neural networks for prediction of 5-year survival in bladder cancer.

PURPOSE: When exploring survival outcomes for patients with bladder cancer, most studies rely on conventional statistical methods such as proportional hazards models. Given the successful application of machine learning to handle big data in many disciplines outside of medicine, we sought to determine if machine learning could be used to improve our ability to predict survival in bladder cancer patients. We compare the performance of artificial neural networks (ANN), a type of machine learning algorithm, with that of multivariable Cox proportional hazards (CPH) models in the prediction of 5-year disease-specific survival (DSS) and overall survival (OS) in patients with bladder cancer. SUBJECTS AND METHODS: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 program database was queried to identify adult patients with bladder cancer diagnosed between 1995 and 2010, yielding 161,227 patients who met our inclusion criteria. ANNs were trained and tested on an 80/20 split of the dataset. Multivariable CPH models were developed in parallel. Variables used for prediction included age, sex, race, grade, SEER stage, tumor size, lymph node involvement, degree of extension, and surgery received. The primary outcomes were 5-year DSS and 5-year OS. Receiver operating characteristic curve analysis was conducted, and ANN models were tested for calibration. RESULTS: The area under the curve for the ANN models was 0.81 for the OS model and 0.80 for the DSS model. Area under the curve for the CPH models was 0.70 for OS and 0.81 for DSS. The ANN OS model achieved a calibration slope of 1.03 and a calibration intercept of -0.04, while the ANN DSS model achieved a calibration slope of 0.99 and a calibration intercept of -0.04. CONCLUSIONS: Machine learning algorithms can improve our ability to predict bladder cancer prognosis. Compared to CPH models, ANNs predicted OS more accurately and DSS with similar accuracy. Given the inherent limitations of administrative datasets, machine learning may allow for optimal interpretation of the complex data they contain.

Critical Appraisal of Quality Improvement Publications in the Urological Literature.

INTRODUCTION: Quality improvement efforts enable rapid improvement in health care by measuring, analyzing and controlling the delivery of patient care. However, publications on quality improvement initiatives often vary in quality, decreasing their impact and restricting adoption by other institutions. We aim to compare the number, quality and trends of quality improvement publications in the urological literature. METHODS: PubMed®/MEDLINE® and EMBASE® were used to identify relevant quality improvement publications in the urological literature since 1999. Critical appraisal of each publication was performed using the Quality Improvement Minimum Quality Criteria Set. RESULTS: Inclusion criteria were met by 34 publications. Mean Quality Improvement Minimum Quality Criteria Set score ± SD was 10.8 ± 2.2 out of 16. Of the publications 44.1% (15) scored 10/16 or lower reflecting low quality. Only 8.8% (3) used the Standards for Quality Improvement Reporting Excellence. The majority of quality improvement publications consist of process rather than outcome or structural measures. The number of publications per year increased dramatically in 2015. However, average Quality Improvement Minimum Quality Criteria Set score before and after this time showed no change (p=0.88). Overall, 70.6% (24) of publications failed to report the quality improvement intervention's penetration/reach and 64.7% failed to report on a patient health related outcome. CONCLUSIONS: Critical appraisal of quality improvement publications in the urological literature indicates that the number of quality improvement publications is increasing over time. However, the reporting quality of quality improvement publications has stagnated. Adherence to reporting guidelines, quality standards and inclusion of all domains of the Quality Improvement Minimum Quality Criteria Set will potentially improve the quality of quality improvement publications and facilitate adoption of best practices in the field of urology.

Robot-Assisted Radical Prostatectomy Associated with Decreased Persistent Postoperative Opioid Use.

Introduction: Minimally invasive surgery offers reduced pain and opioid use postoperatively compared with open surgery, but large-scale comparative studies are lacking. We assessed the incidence of persistent opioid use after open and robot-assisted radical prostatectomy (RARP). Materials and Methods: We performed a retrospective claims database cohort study of opioid-naive (i.e., no opioid prescriptions 30-180 days before index surgery) adult males who underwent radical prostatectomy for prostate cancer from July 2013 to June 2017. For patients who filled a perioperative opioid prescription (30 days before to 14 days after surgery), we calculated the incidence of new persistent postoperative opioid use (≥1 prescription 90-180 days after surgery). Multivariable logistic regression was performed to investigate the association between the surgical approach, patient risk factors, and persistent opioid use. Results: Twelve thousand two hundred seventy-eight radical prostatectomy patients filled an opioid prescription perioperatively (1510 [12%] open and 10,768 [88%] robot assisted). Of these, 846 (6.9%) patients continued to fill opioid prescription(s) 90 to 180 days after surgery. Patients undergoing RARP were 35% less likely to develop new persistent opioid use compared with those undergoing open radical prostatectomy (6.5% vs 9.7%; adjusted odds ratio 0.65; 95% confidence interval 0.54, 0.79). Other independent risk factors included living in the southern, western, or north central United States; preoperative comorbidity; and tobacco use. Conclusions: Approximately 6.9% of opioid-naive patients continued to fill opioid prescriptions 90 days after radical prostatectomy. The risk of persistent opioid use was significantly lower among patients undergoing a robot-assisted vs open approach. Further efforts are needed to develop postoperative opioid prescription protocols for patients undergoing radical prostatectomy.

Modeling the contribution of the obesity epidemic to the temporal decline in sperm counts.

OBJECTIVE: Total sperm count (TSC) has been declining worldwide over the last several decades due to unknown etiologies. Our aim was to model the contribution that the obesity epidemic may have on declining TSC. MATERIALS AND METHODS: Obesity rates were determined since 1973 using the WHO's Global Health Observatory data. A literature review was performed to determine the association between TSC and obesity. Using the measured obesity rates and published TSC since 1973, a model was created to evaluate the association between temporal trends in obesity/temperature and sperm count. RESULTS: Since 1973, obesity prevalence in the United States was increased from 41% to 67.9%. A review of the literature showed that body mass index (BMI) categories 2, 3, and 4 were associated with TSC (millions) of 164.27, 155.71, and 142.29, respectively. The contribution to change over time for obesity from 1974 to 2011 was modeled at 1.8%. When the model was changed to represent the most extreme possible contribution to obesity reported, the modeled change over time rose to 7.2%. When stratified according to fertility status, the contribution that BMI had to falling sperm counts for all comers was 1.7%, while those presenting for fertility evaluation was 2.1%. CONCLUSIONS: While the decline in TSC may be partially due to rising obesity rates, these contributions are minimal which highlights the complexity of this problem.

SLIPS-LAB-A bioinspired bioanalysis system for metabolic evaluation of urinary stone disease.

Urinary stone disease is among the most common medical conditions. Standard evaluation of urinary stone disease involves a metabolic workup of stone formers based on measurement of minerals and solutes excreted in 24-hour urine samples. Nevertheless, 24-hour urine testing is slow, expensive, and inconvenient for patients, which has hindered widespread adoption in clinical practice. Here, we demonstrate SLIPS-LAB (Slippery Liquid-Infused Porous Surface Laboratory), a droplet-based bioanalysis system, for rapid measurement of urinary stone-associated analytes. The ultra-repellent and antifouling properties of SLIPS, which is a biologically inspired surface technology, allow autonomous liquid handling and manipulation of physiological samples without complicated sample preparation procedures and supporting equipment. We pilot a study that examines key urinary analytes in clinical samples from patients with urinary stone. The simplicity and speed of SLIPS-LAB hold the potential to provide actionable diagnostic information for patients with urinary stone disease and rapid feedback for responses to dietary and pharmacologic treatments.

Ultra-Low-Dose CT: An Effective Follow-Up Imaging Modality for Ureterolithiasis.

Background and Purpose: Classically, abdominal X-ray (KUB), ultrasound, or a combination of both have been routinely used for ureteral stone surveillance after initial diagnosis. More recently, ultra-low-dose CT (ULD CT) has emerged as a CT technique that reduces radiation dose while maintaining high sensitivity and specificity for urinary stone detection. We aim to evaluate our initial experience with ULD CT for patients with ureterolithiasis, measuring real-world radiation doses and stone detection performance. Methods: We reviewed all ULD CT scans performed at the Veterans Affairs Palo Alto Health Care System between 2016 and 2018. We included patients with ureteral stones and calculated the mean effective radiation dose per scan. We determined stone location and size, if the stone was visible on the associated KUB or CT scout film, and if hydronephrosis was present. We performed logistic regression to identify variables associated with visibility on KUB or CT scout film and hydronephrosis. Results: One hundred eighteen ULD scans were reviewed, of which 50 detected ureteral stones. The mean effective radiation dose was 1.04 ± 0.41 mSv. Of the ULD CTs that detected ureterolithiasis, 38% lacked visibility on KUB/CT scout film and had no associated hydronephrosis, suggesting that they would be missed with a combination of KUB and ultrasound. Larger stones (odds ratio [OR]: 1.40, 95% confidence interval [CI]: 1.08, 1.96 for every 1 mm increase in stone size) were more likely to be detected by KUB/CT scout film or ultrasound, while stones in the distal ureter (OR: 0.18, 95% CI: 0.03, 0.81) were more likely to be missed by KUB/CT scout film or hydronephrosis. Conclusions: Based on our institutions' initial experience, ULD CT detects small and distal ureteral stones that would likely be missed by KUB or ultrasound, while maintaining a low effective radiation dose. An ULD CT protocol should be considered when reimaging for ureteral stones is necessary.