Evaluation of Reproductive Parameters in Men with Solitary Testis.

PURPOSE: We evaluated reproductive parameters of men with a solitary testis compared to men with bilateral testes. MATERIALS AND METHODS: We conducted a cross-sectional case-control study comparing infertility evaluation parameters in men with a solitary testis to men with 2 testes. Men presenting for fertility consultation with semen analysis data collected within 90 days of clinical and hormonal assessment were included. Differences in semen and hormonal levels were characterized using descriptive statistics, multivariate analysis on matched semen parameters and evaluation of hypogonadism subtypes. RESULTS: A total of 837 men were identified. After applying exclusion criteria, we analyzed 29/39 men (74%) with a solitary testis and compared them to 656/798 men (82%) with 2 testes. A 1:1 match on sperm concentration and motility showed follicle-stimulating hormone was more elevated in men with a solitary testis (median 13.9 mIU/ml, IQR 5.8-24.2) than in men with bilateral testes (5.0, 3.4-9.8, p=0.009). Men with bilateral testes were uniformly azoospermic after a follicle-stimulating hormone level of 25.0 mIU/ml, whereas men with a solitary testis had some sperm in the ejaculate at follicle-stimulating hormone levels as high as 54.6 mIU/ml. Testosterone levels were similar between men with a solitary testis (median 381 ng/dl, IQR 248-500) and 2 testes (387, 296-507). The prevalence of compensated hypogonadism (high luteinizing hormone and normal testosterone) was higher in men with a solitary testis (8/29, 28%) than in men with bilateral testes (54/656, 8.2%, p=0.002). CONCLUSIONS: Men with a solitary testis have compensated pituitary strain as indicated by higher serum follicle-stimulating hormone and luteinizing hormone levels for the same testosterone and semen parameters compared to men with bilateral testes. Followup is needed to evaluate any detrimental consequences of this compensated state that may lead to pituitary burnout and decreased testicular function.

Using Artificial Intelligence to Predict Surgical Shunts in Men with Ischemic Priapism.

PURPOSE: Ischemic priapism is a urological emergency that requires prompt intervention to preserve erectile function. Characteristics that influence escalation to surgical intervention remain unclear. We identified factors and developed machine learning models to predict which men presenting with ischemic priapism will require shunting. MATERIALS AND METHODS: We identified men with ischemic priapism admitted to the emergency department of our large county hospital between January 2010 and June 2019. We collected patient demographics, etiology, duration of priapism prior to intervention, interventions attempted and escalation to shunting. Machine learning models were trained and tested using R to predict which patients require surgical shunting. RESULTS: A total of 334 encounters of ischemic priapism were identified. The majority resolved with intracavernosal phenylephrine injection and/or cavernous aspiration (78%). Shunting was required in 10% of men. Median duration of priapism before intervention was longer for men requiring shunting than for men who did not (48 vs 7 hours, p=0.030). Patients with sickle cell disease as the etiology were less likely to require shunting compared to all other etiologies (2.2% vs 15.2%, p=0.035). CONCLUSIONS: Men with longer duration of priapism before treatment more often underwent shunting. However, phenylephrine injection and aspiration remained effective for priapism lasting more than 36 hours. Having sickle cell disease as the etiology of priapism was protective against requiring shunting. We developed artificial intelligence models that performed with 87.2% accuracy and created an online probability calculator to determine which patients with ischemic priapism may require shunting.

Use of the IDEAL framework in the urological literature: where are we in 2018?

OBJECTIVES: To assess uptake and application of the IDEAL principles in original surgical procedure- or device-related clinical research studies, as well as its reported relevance as characterized by secondary publications, editorials and reviews. MATERIALS AND METHODS: IDEAL (Idea, Development, Exploration, Assessment, Long-term study) is a framework that provides stage-specific guidance for surgical innovation and represented a major advance towards raising evidential standards. We performed a comprehensive literature search of all urology-related publications citing one or more of seven key publications on IDEAL in The Lancet and BMJ using multiple databases up to 31 December 2017. RESULTS: We identified a total of 150 urology-related manuscripts citing IDEAL, of which 83 (55.3%) were original research and 67 (44.7%) were secondary publications. Among the original research articles, 40 (48.2%) did not explicitly apply IDEAL principles or were not surgical innovation studies. The IDEAL phases of the 43 (51.8%) remaining original research studies were IDEAL, in nine (20.9%), 27 (62.8%), four (9.3%), 0 (0%), and three publications (7.0%), respectively. Across IDEAL stages, 30 (75.0%) studies were prospective, 29 (85.3%) reported ethical oversight, and 39 (90.7%) captured treatment-related harms. None of the studies collected information on physician experience. CONCLUSIONS: The IDEAL framework has found widespread adoption in the urology literature as witnessed by a large number of original manuscripts and secondary publications citing IDEAL; however, its application is largely limited to the early stages of surgical innovation, frequently with inappropriate and incomplete implementation. Further efforts are needed to guide investigators in the optimal use of the IDEAL framework as it relates to surgical innovation in urology.

Algorithms for Predicting the Probability of Azoospermia from Follicle Stimulating Hormone: Design and Multi-Institutional External Validation.

PURPOSE: To predict the probability of azoospermia without a semen analysis in men presenting with infertility by developing an azoospermia prediction model. MATERIALS AND METHODS: Two predictive algorithms were generated, one with follicle stimulating hormone (FSH) as the only input and another logistic regression (LR) model with additional clinical inputs of age, luteinizing hormone, total testosterone, and bilateral testis volume. Men presenting between 01/2016 and 03/2020 with semen analyses, testicular ochiodemetry, and serum gonadotropin measurements collected within 120 days were included. An azoospermia prediction model was developed with multi-institutional two-fold external validation from tertiary urologic infertility clinics in Chicago, Miami, and Milan. RESULTS: Total 3,497 participants were included (n=Miami 946, Milan 1,955, Chicago 596). Incidence of azoospermia in Miami, Milan, and Chicago was 13.8%, 23.8%, and 32.0%, respectively. Predictive algorithms were generated with Miami data. On Milan external validation, the LR and quadratic FSH models both demonstrated good discrimination with areas under the receiver-operating-characteristic (ROC) curve (AUC) of 0.79 and 0.78, respectively. Data from Chicago performed with AUCs of 0.71 for the FSH only model and 0.72 for LR. Correlation between the quadratic FSH model and LR model was 0.95 with Milan and 0.92 with Chicago data. CONCLUSIONS: We present and validate algorithms to predict the probability of azoospermia. The ability to predict the probability of azoospermia without a semen analysis is useful when there are logistical hurdles in obtaining a semen analysis or for reevaluation prior to surgical sperm extraction.

Artificial Intelligence Based Machine Learning Models Predict Sperm Parameter Upgrading after Varicocele Repair: A Multi-Institutional Analysis.

PURPOSE: Varicocele repair is recommended in the presence of a clinical varicocele together with at least one abnormal semen parameter, and male infertility. Unfortunately, up to 50% of men who meet criteria for repair will not see meaningful benefit in outcomes despite successful treatment. We developed an artificial intelligence (AI) model to predict which men with varicocele will benefit from treatment. MATERIALS AND METHODS: We identified men with infertility, clinical varicocele, and at least one abnormal semen parameter from two large urology centers in North America (Miami and Toronto) between 2006 and 2020. We collected pre and post-operative clinical and hormonal data following treatment. Clinical upgrading was defined as an increase in sperm concentration that would allow a couple to access previously unavailable reproductive options. The tiers used for upgrading were: 1-5 million/mL (ICSI/IVF), 5-15 million/mL (IUI) and >15 million/mL (natural conception). Thus moving from ICSI/IVF to IUI, or from IUI to natural conception, would be considered an upgrade. AI models were trained and tested using R to predict which patients were likely to upgrade after surgery. The model sorted men into categories that defined how likely they were to upgrade after surgery (likely, equivocal, and unlikely). RESULTS: Data from 240 men were included from both centers. A total of 45.6% of men experienced an upgrade in sperm concentration following surgery, 48.1% did not change, and 6.3% downgraded. The data from Miami were used to create a random forest model for predicting upgrade in sperm concentration. On external validation using Toronto data, the model accurately predicted upgrade in 87% of men deemed likely to improve, and in 49% and 36% of men who were equivocal and unlikely to improve, respectively. Overall, the personalized prediction for patients in the validation cohort was accurate (AUC 0.72). CONCLUSIONS: A machine learning model performed well in predicting clinically meaningful post-varicocelectomy sperm parameters using pre-operative hormonal, clinical, and semen analysis data. To our knowledge, this is the first prediction model to show the utility of hormonal data, as well as the first to use machine learning models to predict clinically meaningful upgrading. This model will be published online as a clinical calculator that can be used in the preoperative counseling of patients.