Background New York City was the international epicenter of the COVID-19 pandemic. Health care providers responded by rapidly transitioning from in-person to video consultations. Telemedicine (ie, video visits) is a potentially disruptive innovation; however, little is known about patient satisfaction with this emerging alternative to the traditional clinical encounter. Objective This study aimed to determine if patient satisfaction differs between video and in-person visits. Methods In this retrospective observational cohort study, we analyzed 38,609 Press Ganey patient satisfaction survey outcomes from clinic encounters (620 video visits vs 37,989 in-person visits) at a single-institution, urban, quaternary academic medical center in New York City for patients aged 18 years, from April 1, 2019, to March 31, 2020. Time was categorized as pre–COVID-19 and COVID-19 (before vs after March 4, 2020). Wilcoxon-Mann-Whitney tests and multivariable linear regression were used for hypothesis testing and statistical modeling, respectively. Results We experienced an 8729% increase in video visit utilization during the COVID-19 pandemic compared to the same period last year. Video visit Press Ganey scores were significantly higher than in-person visits (94.9% vs 92.5%; P<.001). In adjusted analyses, video visits (parameter estimate [PE] 2.18; 95% CI 1.20-3.16) and the COVID-19 period (PE 0.55; 95% CI 0.04-1.06) were associated with higher patient satisfaction. Younger age (PE –2.05; 95% CI –2.66 to –1.22), female gender (PE –0.73; 95% CI –0.96 to –0.50), and new visit type (PE –0.75; 95% CI –1.00 to –0.49) were associated with lower patient satisfaction. Conclusions Patient satisfaction with video visits is high and is not a barrier toward a paradigm shift away from traditional in-person clinic visits. Future research comparing other clinic visit quality indicators is needed to guide and implement the widespread adoption of telemedicine.

Background While Prostate Imaging Reporting and Data System (PI‐RADS) 4 and 5 lesions typically warrant prostate biopsy and PI‐RADS 1 and 2 lesions may be safely observed, PI‐RADS 3 lesions are equivocal. Purpose To construct and cross‐validate a machine learning model based on radiomics features from T2‐weighted imaging (T2WI) of PI‐RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group ≥ 2. Study type Single‐center retrospective study. Population A total of 240 patients were included (training cohort, n = 188, age range 43–82 years; test cohort, n = 52, age range 41–79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)‐targeted biopsy between 2015 and 2020; 2) PI‐RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively. Field strength/sequence A 3 T; T2WI turbo‐spin echo, diffusion‐weighted spin‐echo echo planar imaging, dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging. Assessment Multislice volumes‐of‐interest (VOIs) were drawn in the PI‐RADS 3 index lesions on T2WI. A total of 107 radiomics features (first‐order histogram and second‐order texture) were extracted from the segmented lesions. Statistical Tests A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis. Results The trained random forest classifier constructed from the T2WI radiomics features good and statistically significant area‐under‐the‐curves (AUCs) of 0.76 (P = 0.022) for prediction of csPCa in the test set. Prostate volume and PSA density showed moderate and nonsignificant performance (AUC 0.62, P = 0.275 and 0.61, P = 0.348, respectively) for csPCa prediction in the test set. Conclusion The machine learning classifier based on T2WI radiomic features demonstrated good performance for prediction of csPCa in PI‐RADS 3 lesions. Evidence Level 4 Technical Efficacy 2

ObjectivesCryoablation for prostate cancer is typically performed under general anaesthesia. We explore the safety, feasibility and costs of in-office MRI-targeted prostate partial gland cryoablation (PGC) under local anaesthesia. We hypothesise that an office-based procedure under local anaesthesia may yield greater patient convenience and lower health costs with similar outcomes to a general anaesthesia approach.Design/participants/setting/interventionsRetrospective study of men diagnosed with clinically significant prostate cancer (grade group (GG) ≥2) who elected to undergo in-office PGC under local anaesthesia.Main outcome measuresA total of 55 men with GG ≥2 prostate cancer underwent PGC under local anaesthesia, and 35 of 43 men (81.4%) who attained ≥6 months of follow-up post-treatment underwent MRI-targeted surveillance biopsy. We used MRI findings and targeted biopsy to characterise post-PGC oncological outcomes. Complications were categorised using Common Terminology Criteria for Adverse Events (CTCAE). Expanded Prostate Cancer Index-Clinical Practice was used to characterise urinary and sexual function scores at baseline, 4 and 9 months post-PGC. Time-driven activity-based costing was used to determine healthcare costs of in-office PGC.ResultsFive (9.1%) men experienced CTCAE score 3 adverse events. Urinary and sexual function did not change significantly from baseline to 4 months (p=0.20 and p=0.08, respectively) and 9 months (p=0.23 and p=0.67, respectively). Twenty-two men (62.9%) had no cancer or GG1 and 13 (37.1%) men had GG≥2 on post-PGC biopsy. Moreover, the median cost of in-office PGC was US$4,463.05 (range US$4,087.19–US7,238.16) with disposables comprising 69% of the cost.ConclusionsIn-office PGC is feasible under local anaesthesia with favourable functional outcome preservation and adverse events profile at significantly lower costs compared with a general anaesthesia approach.

No abstract

No abstract

urologists with different levels of experience. Further studies are required to test decision support in more complex or advanced situations.

No abstract

Background Central to a transplant registry’s quality are accuracy and completeness of the clinical information being captured, especially for important outcomes, such as graft failure or death. Effects of more limited access to external sources of death data for transplant registries are compounded by different data management processes at the United Network for Organ Sharing (UNOS), the Scientific Registry of Transplant Recipients (SRTR), and the United States Renal Data System (USRDS). Methods This cross-sectional registry study examined differences in reported deaths among kidney transplant candidates and recipients of kidneys from deceased and living donors in 2000 through 2019 in three transplant datasets, based on data current as of 2020. We assessed annual death rates and survival estimates to visualize trends in reported deaths between sources. Results The UNOS dataset included 77,605 deaths among 315,346 recipients and 61,249 deaths among 275,000 nonpreemptively-waitlisted candidates who were never transplanted. The SRTR dataset included 87,149 deaths among 315,152 recipients and 60,042 deaths among 259,584 waitlisted candidates. The USRDS dataset included 89,515 deaths among 311,955 candidates and 63,577 deaths among 238,167 waitlisted candidates. Annual death rates among the prevalent transplant population show accumulating differences across datasets—2.31%, 4.00%, and 4.03% by 2019 from UNOS, SRTR, and USRDS, respectively. Long-term survival outcomes were similar among nonpreemptively-waitlisted candidates but showed more than 10% discordance between USRDS and UNOS among transplanted patients. Conclusions Large differences in reported patient outcomes across datasets appear to be increasing, raising questions about their completeness. Understanding the differences between these datasets is essential for accurate, reliable interpretation of analyses that use these data for policy development, regulatory oversight, and research.