Is Reddit a reliable source for information on erectile dysfunction treatment?

Patients may turn to social media websites, such as Reddit, for information on erectile dysfunction prior to seeking care from a physician. We sought to identify, characterize, and assess the quality of the erectile dysfunction treatments discussed on the highly influential Reddit website. We assessed 2634 comments from two subreddits, r/AskMen and r/ErectileDysfunction, for positive and negative statements regarding treatments for erectile dysfunction. A total of 45 unique treatments were discussed and consisted of changes in sexual behavior (30%), lifestyle changes (29%), medical interventions (23%), talking with a partner about ED (10%), and use of supplements (8%). Only 24.4% of all treatments discussed are in line with current American Urological Association guidelines. Only 43.8% of all positive statements made endorsed a guidelines-based treatment, indicating a high rate of self-proclaimed success with alternative therapies. Our results indicate that there is active discussion of erectile dysfunction treatment on Reddit with a wide range of therapies recommended, however, the majority of the recommendations are not supported by strong clinical evidence.

Deferring gonadectomy in patients with turner syndrome with a genetic Y component is not a safe practice.

INTRODUCTION: Patients with Turner syndrome who harbor Y chromosome material are known to be at increased risk of developing germ cell neoplasms. The optimal timing to perform gonadectomy to reduce the risk of cancer development in these patients is not well defined. We present outcomes of Turner with a Y component (TSY) patients who underwent gonadectomy at our institution. HYPOTHESIS/OBJECTIVE: We hypothesized that tumors could occur in a significant portion of TSY patients at any age and gonadectomy can be safely performed at diagnosis rather than deferred. STUDY DESIGN: We performed an IRB-approved retrospective single center study in which we queried our institutions electronic health record to identify all patients with TSY who underwent gonadectomy at our institution from 2012 to 2021. RESULTS: In our series of 18 consecutive TSY patients, a tumor was identified in 6 patients (33.3%): 4 (22.2%) with dysgerminoma (DG) [Fig. 1] and 2 (11.1%) with gonadoblastoma (GB). DISCUSSION: Our cohort of 18 consecutive TSY who underwent gonadectomy over a 9-year period is the largest published single site cohort to date. Additionally, our patient who was found to have GB at 40 days is to our knowledge the youngest TSY patient to be diagnosed with GB in the literature. This patient's remarkably early incidence of tumor occurrence illustrates the urgency of protective gonadectomy. Given the high incidence of tumor formation in this population and the minimal morbidity associated with gonadectomy, we do not recommend delaying gonadectomy in this population for any reason. Our study is vulnerable to selection bias and confounding innate to any retrospective study. There was variation with respect to the frequency and timing of pre-operative imaging as a strict preoperative imaging protocol with sequential studies was not in place at our institution. Additionally, we do not have a comparison cohort of patients who are being followed without operative intervention as all TSY patients at our institution have undergone gonadectomy. CONCLUSION: TSY patients cannot be safely observed for tumor formation based on clinical factors such as imaging or age. Gonadectomy is safe with a low complication rate and without tumor recurrence during three-year follow-up. We continue to recommend bilateral gonadectomy in this patient population at the time of diagnosis.

Automated Society of Fetal Urology (SFU) grading of hydronephrosis on ultrasound imaging using a convolutional neural network.

INTRODUCTION: Grading of hydronephrosis severity on postnatal renal ultrasound guides management decisions in antenatal hydronephrosis (ANH). Multiple systems exist to help standardize hydronephrosis grading, yet poor inter-observer reliability persists. Machine learning methods may provide tools to improve the efficiency and accuracy of hydronephrosis grading. OBJECTIVE: To develop an automated convolutional neural network (CNN) model to classify hydronephrosis on renal ultrasound imaging according to the Society of Fetal Urology (SFU) system as potential clinical adjunct. STUDY DESIGN: A cross-sectional, single-institution cohort of postnatal renal ultrasounds with radiologist SFU grading from pediatric patients with and without hydronephrosis of stable severity was obtained. Imaging labels were used to automatedly select sagittal and transverse grey-scale renal images from all available studies from each patient. A VGG16 pre-trained ImageNet CNN model analyzed these preprocessed images. Three-fold stratified cross-validation was used to build and evaluate the model that was used to classify renal ultrasounds on a per patient basis into five classes based on the SFU system (normal, SFU I, SFU II, SFU III, or SFU IV). These predictions were compared to radiologist grading. Confusion matrices evaluated model performance. Gradient class activation mapping demonstrated imaging features driving model predictions. RESULTS: We identified 710 patients with 4659 postnatal renal ultrasound series. Per radiologist grading, 183 were normal, 157 were SFU I, 132 were SFU II, 100 were SFU III, and 138 were SFU IV. The machine learning model predicted hydronephrosis grade with 82.0% (95% CI: 75-83%) overall accuracy and classified 97.6% (95% CI: 95-98%) of the patients correctly or within one grade of the radiologist grade. The model classified 92.3% (95% CI: 86-95%) normal, 73.2% (95% CI: 69-76%) SFU I, 73.5% (95% CI: 67-75%) SFU II, 79.0% (95% CI: 73-82%) SFU III, and 88.4% (95% CI: 85-92%) SFU IV patients accurately. Gradient class activation mapping demonstrated that the ultrasound appearance of the renal collecting system drove the model's predictions. DISCUSSION: The CNN-based model classified hydronephrosis on renal ultrasounds automatically and accurately based on the expected imaging features in the SFU system. Compared to prior studies, the model functioned more automatically with greater accuracy. Limitations include the retrospective, relatively small cohort, and averaging across multiple imaging studies per patient. CONCLUSIONS: An automated CNN-based system classified hydronephrosis on renal ultrasounds according to the SFU system with promising accuracy based on appropriate imaging features. These findings suggest a possible adjunctive role for machine learning systems in the grading of ANH.

Evaluating Factors That Influence Health Care Resource Utilization in Transurethral Resection of Prostate.

Introduction: Transurethral resection of prostate (TURP) remains the gold standard for the treatment of benign prostatic hyperplasia, but it is associated with complications. The association of health care resource utilization (HRU) and TURP has been poorly studied. We seek to evaluate HRU in patients undergoing TURP and identify factors contributing to outcomes. Methods: The National Surgical Quality Improvement Program (NSQIP) database was reviewed from 2012 to 2018 for TURP by Current Procedural Terminology code. All data will be deidentified with IRB exemption. HRU was defined as discharge to continued care, unplanned readmission within 30 days, or prolonged length of stay (LOS) (>75th percentile). We included preoperative variables, including age, body mass index, diabetes, and ASA class (a classification system to assess for fitness of patients perior to surgery). Operative duration (OD) was broken into deciles by minutes. Preoperative characteristics and outcomes were compared against OD. Predictors of HRU were found using a stepwise multivariate logistic regression. Results: Overall, 38,749 patients were included. The following variables were significantly associated with OD (values are three shortest and three longest deciles, respectively): any HRU (35.9%, 32.4%, 31.4% and 32.4%, 33.7%, 37.6%) and prolonged LOS (31.3%, 27.6%, 26.5% and 28.0%, 30.4%, 34.1%). Findings in the first decile seemed to be an outlier, as shown in Figure 1. Complications associated with OD are shown in Figure 2. On multivariable analysis, patients with OD >58 minutes were more likely to have increased HRU; odds ratio 1.22, 1.33, 1.54, and 1.78 for deciles 58-66, 67-78, 78-99, and >100, respectively; p80, chronic obstructive pulmonary disease, dyspnea, hypertension, diabetes, not functionally independent, ASA class III and IV-V, and dirty/infected wound class, p < 0.005. [Figure: see text] [Figure: see text] Conclusions: OD is an independent predictor of HRU in patients undergoing TURP and is more modifiable than other preoperative variables associated with increased HRU. Patients in the longest decile were more likely to have complications and increased HRU. Further study is needed to evaluate causation.