Assessing the readability and quality of online information about benign prostatic hyperplasia.

PURPOSE: Benign prostatic hyperplasia (BPH) affects nearly half of men in their fifties. Patients often search the Internet to better understand their diagnosis, but online health information is not well regulated and can be difficult for patients to comprehend. This study aims to evaluate not only readability, but also the quality of online information about BPH, as well as the effect of commercial bias on readability and quality. METHODS: Three search engines (Google, Bing, and DuckDuckGo) were used with broad search terms including "BPH," "BPH treatment," and "BPH surgery," to mimic a patient diagnosed with BPH seeking further information. 204 total websites were identified, of which 62 were unique websites. Among those unique websites, 23 were advertisements. Three readability formulas (Flesch-Kincaid Grade Level, Flesch-Kincaid Reading Ease, SMOG) were used to generate readability scores. DISCERN standardized questionnaire was used to evaluate website quality. RESULTS: Average reading level of online information about BPH was significantly higher than the recommended level by the American Medical Association (AMA) and United States Department of Health and Human Services (USDHHS). Advertisements had significantly easier readability than nonadvertisements. Average website quality was "excellent" for nonadvertisements, but only "fair" for advertisements. CONCLUSION: Although advertisements may hold optimal search result positions and have better readability than nonadvertisements, they have biased and lower quality information. It is important to guide patients to high quality online information of appropriate reading level. Continued efforts should be made to create and share with patients high quality resources with improved readability to facilitate comprehension and minimize misinformation.

Multi-center validation of an artificial intelligence system for detection of COVID-19 on chest radiographs in symptomatic patients.

OBJECTIVES: While chest radiograph (CXR) is the first-line imaging investigation in patients with respiratory symptoms, differentiating COVID-19 from other respiratory infections on CXR remains challenging. We developed and validated an AI system for COVID-19 detection on presenting CXR. METHODS: A deep learning model (RadGenX), trained on 168,850 CXRs, was validated on a large international test set of presenting CXRs of symptomatic patients from 9 study sites (US, Italy, and Hong Kong SAR) and 2 public datasets from the US and Europe. Performance was measured by area under the receiver operator characteristic curve (AUC). Bootstrapped simulations were performed to assess performance across a range of potential COVID-19 disease prevalence values (3.33 to 33.3%). Comparison against international radiologists was performed on an independent test set of 852 cases. RESULTS: RadGenX achieved an AUC of 0.89 on 4-fold cross-validation and an AUC of 0.79 (95%CI 0.78-0.80) on an independent test cohort of 5,894 patients. Delong's test showed statistical differences in model performance across patients from different regions (p < 0.01), disease severity (p < 0.001), gender (p < 0.001), and age (p = 0.03). Prevalence simulations showed the negative predictive value increases from 86.1% at 33.3% prevalence, to greater than 98.5% at any prevalence below 4.5%. Compared with radiologists, McNemar's test showed the model has higher sensitivity (p < 0.001) but lower specificity (p < 0.001). CONCLUSION: An AI model that predicts COVID-19 infection on CXR in symptomatic patients was validated on a large international cohort providing valuable context on testing and performance expectations for AI systems that perform COVID-19 prediction on CXR. KEY POINTS: • An AI model developed using CXRs to detect COVID-19 was validated in a large multi-center cohort of 5,894 patients from 9 prospectively recruited sites and 2 public datasets. • Differences in AI model performance were seen across region, disease severity, gender, and age. • Prevalence simulations on the international test set demonstrate the model's NPV is greater than 98.5% at any prevalence below 4.5%.

Online interest in surgical treatment for benign prostatic hyperplasia using Google trends.

PURPOSE: Online health information-seeking behavior has increased over the past 15 years; however, little is known about the online interest for surgical treatment of men with benign prostatic hyperplasia. We used Google Trends to evaluate online interest for various surgical procedures for benign prostatic hyperplasia in the US and worldwide. METHODS: Transurethral resection of the prostate, photoselective vaporization of the prostate, Holmium laser enucleation of the prostate, Urolift, and Rezum were selected as search terms of interest within Google Trends. Google Trends normalizes the popularity of search terms on a 0-100 scale. ANOVA and the average rate of change in popularity scores were conducted from July 2015 to February 2019. RESULTS: From 2004 to 2019, online interest in transurethral resection of the prostate and photoselective vaporization of the prostate remained stable in the US and worldwide; meanwhile, there was an upward trend for Urolift and Rezum. There was a statistically significant increase in the online interest for Holmium laser enucleation of the prostate in the US and worldwide; however, the mean popularity score was significantly higher worldwide. CONCLUSIONS: A shift in online interest towards minimally invasive surgical therapies for benign prostatic hyperplasia was demonstrated in the US and worldwide and parallels clinical practice patterns, such as HoLEP and Urolift surgical volumes in Indiana and Australia, respectively. Google Trends can be used in real-time to gauge online interest for surgical procedures and help guide physician-patient counseling.

Safety and Feasibility of Cryoablation during Immunotherapy in Patients with Metastatic Soft Tissue Sarcoma.

PURPOSE: Patients with metastatic soft tissue sarcoma (STS) undergo a wide array of treatments, including surgery, radiation, chemotherapy, immunotherapy, and ablative therapies, to control their disease. The combination of cryoablation and immunotherapy may lead to an enhanced antitumor immune response via the abscopal effect. It is hypothesized that the combination of cryoablation and immunotherapy in patients with metastatic STS is safe and feasible. MATERIALS AND METHODS: A single-center retrospective analysis was performed on patients with metastatic STS who underwent cryoablation. Sixteen patients were treated with 27 cryoablation procedures while receiving ipilimumab and nivolumab from April 2017 to July 2020. Response Evaluation Criteria in Solid Tumors, 1.1, were used to determine the outcomes of nontarget tumors. Progression-free survival (PFS) and overall survival (OS) were calculated from the date of the first cryoablation after initiating immunotherapy until progression or death. RESULTS: Thirty-four tumors were cryoablated, 23 of which were intentionally subtotal. The most common tumor subtype was liposarcoma (n = 4). Thirteen (81%) patients had previously demonstrated disease progression on multiple lines of chemotherapy. All tumors cryoablated with a complete intention demonstrated a complete response. Seven patients had a clinical benefit, including 1 with a complete response, 1 with a partial response, and 5 with stable disease. The median OS was 14.1 months, with a median PFS of 2.3 months (95% confidence interval, 1.8-14.3). Five patients had pneumothoraces after cryoablation, 2 of whom required chest tube placement. Eleven patients experienced adverse events related to immunotherapy, 10 of whom experienced grade 1 or 2. CONCLUSIONS: Cryoablation in patients with metastatic STS undergoing immunotherapy is feasible and safe.

Logistical Considerations and Clinical Outcomes Associated With Converting Operating Rooms Into an Intensive Care Unit During the Coronavirus Disease 2019 Pandemic in a New York City Hospital.

BACKGROUND: Coronavirus disease 2019 (COVID-19) emerged as a public health crisis that disrupted normal patterns of health care in the New York City metropolitan area. In preparation for a large influx of critically ill patients, operating rooms (ORs) at NewYork-Presbyterian/Columbia University Irving Medical Center (NYP-Columbia) were converted into a novel intensive care unit (ICU) area, the operating room intensive care unit (ORICU). METHODS: Twenty-three ORs were converted into an 82-bed ORICU. Adaptations to the OR environment permitted the delivery of standard critical care therapies. Nonintensive-care-trained staff were educated on the basics of critical care and deployed in a hybrid staffing model. Anesthesia machines were repurposed as critical care ventilators, with accommodations to ensure reliable function and patient safety. To compare ORICU survivorship to outcomes in more traditional environments, we performed Kaplan-Meier survival analysis of all patients cared for in the ORICU, censoring data at the time of ORICU closure. We hypothesized that age, sex, and obesity may have influenced the risk of death. Thus, we estimated hazard ratios (HR) for death using Cox proportional hazard regression models with age, sex, and body mass index (BMI) as covariables and, separately, using older age (65 years and older) adjusted for sex and BMI. RESULTS: The ORICU cared for 133 patients from March 24 to May 14, 2020. Patients were transferred to the ORICU from other ICUs, inpatient wards, the emergency department, and other institutions. Patients remained in the ORICU until either transfer to another unit or death. As the hospital patient load decreased, patients were transferred out of the ORICU. This process was completed on May 14, 2020. At time of data censoring, 55 (41.4%) of patients had died. The estimated probability of survival 30 days after admission was 0.61 (95% confidence interval [CI], 0.52-0.69). Age was significantly associated with increased risk of mortality (HR = 1.05, 95% CI, 1.03-1.08, P < .001 for a 1-year increase in age). Patients who were ≥65 years were an estimated 3.17 times more likely to die than younger patients (95% CI, 1.78-5.63; P < .001) when adjusting for sex and BMI. CONCLUSIONS: A large number of critically ill COVID-19 patients were cared for in the ORICU, which substantially increased ICU capacity at NYP-Columbia. The estimated ORICU survival rate at 30 days was comparable to other reported rates, suggesting this was an effective approach to manage the influx of critically ill COVID-19 patients during a time of crisis.

Ureteral stenting after routine ureteroscopy: Is earlier stent removal feasible?

INTRODUCTION: Ureteroscopy and laser lithotripsy is a common treatment option for upper urinary tract calculi. Currently, ureteral stents are placed after uncomplicated ureteroscopy for up to 1 week, but the optimal length of placement is not well defined. Ureteral stents are associated with significant morbidity, particularly stent discomfort. This study aims to determine differences in postoperative unplanned clinic or ED visits based on duration of stent placement. MATERIALS AND METHODS: This is a single-institution, IRB-approved, retrospective cohort study of 559 ureteroscopy cases with laser lithotripsy for urinary tract calculi performed from 2016 to 2018. The primary outcome was unplanned ED or clinic visits within 30 days following surgery and there. The patients were separated into three groups based on stent duration: 1 (0-3 days), 2 (4-6 days), and 3 (> 6 days). RESULTS: Fifty-eight (10.31%) patients experienced an unplanned visit within 30 days of the procedure. There was no significant difference in unplanned visits among groups for stent duration (p = 0.45). A Clavien grade analysis showed no difference in grades between groups (p = 0.59). A Cox regression model showed no difference in risk of unplanned visit comparing those in groups 2 and 3 to group 1 (p = 0.157 and 0.374, respectively). This also remains to be the case after adjusting for age, sex, and surgeon (p = 0.166 and 0.376, respectively). CONCLUSIONS: We found no difference in unplanned visits in patients based on the duration of stent placement following routine ureteroscopy. Stent removal within 3 days of surgery appears to be sufficient to minimize morbidity after uncomplicated ureteroscopy.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs. MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set. RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction. CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.