Optimizing active surveillance strategies to balance the competing goals of early detection of grade progression and minimizing harm from biopsies

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142555/1/cncr31101.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142555/2/cncr31101_am.pd

Evaluation of a needle disinfectant technique to reduce infection‐related hospitalisation after transrectal prostate biopsy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142074/1/bju13982_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142074/2/bju13982.pd

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men.

BACKGROUND: Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions. OBJECTIVE: We sought to develop a web-based system that uses a prostate cancer (CaP) registry to provide newly diagnosed men with a platform to view predicted treatment decisions based on patients with similar characteristics. DESIGN, SETTING, AND PARTICIPANTS: The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a quality improvement consortium of urology practices that maintains a prospective registry of men with CaP. We used registry data from 45 MUSIC urology practices from 2015 to 2017 to develop and validate a random forest machine learning model. After fitting the random forest model to a derivation cohort consisting of a random two-thirds sample of patients after stratifying by practice location, we evaluated the model performance in a validation cohort consisting of the remaining one-third of patients using a multiclass area under the curve (AUC) measure and calibration plots. RESULTS AND LIMITATIONS: We identified 7543 men diagnosed with CaP, of whom 45% underwent radical prostatectomy, 30% surveillance, 17% radiation therapy, 5.6% androgen deprivation, and 1.8% watchful waiting. The personalized prediction for patients in the validation cohort was highly accurate (AUC 0.81). CONCLUSIONS: Using clinical registry data and machine learning methods, we created a web-based platform for patients that generates accurate predictions for most CaP treatments. PATIENT SUMMARY: We have developed and tested a tool to help men newly diagnosed with prostate cancer to view predicted treatment decisions based on similar patients from our registry. We have made this tool available online for patients to use

Evaluation of Patient- and Surgeon-Specific Variations in Patient-Reported Urinary Outcomes 3 Months After Radical Prostatectomy From a Statewide Improvement Collaborative

Importance: Understanding variation in patient-reported outcomes following radical prostatectomy may inform efforts to reduce morbidity after this procedure. Objective: To describe patient-reported urinary outcomes following radical prostatectomy in the diverse practice settings of a statewide quality improvement program and to explore whether surgeon-specific variations in observed outcomes persist after accounting for patient-level factors. Design, Setting, and Participants: This prospective population-based cohort study included 4582 men in the Michigan Urological Surgery Improvement Collaborative who underwent radical prostatectomy as primary management of localized prostate cancer between April 2014 and July 2018 and who agreed to complete validated questionnaires prior to surgery and at 3, 6, and 12 months after surgery. Data were analyzed from 2019 to June 2019. Exposures: Radical prostatectomy. Main Outcomes and Measures: Patient- and surgeon-level analyses of patient-reported urinary function 3 months after radical prostatectomy. Outcomes were measured using validated questionnaires with results standardized using previously published methods. Urinary function survey scores are reported on a scale from 0 to 100 with good function established as a score of 74 or higher. Results: For the 4582 men undergoing radical prostatectomy within the Michigan Urological Surgery Improvement Collaborative who agreed to complete surveys, mean (SD) age was 63.3 (7.1) years. Survey response rates varied: 3791 of 4582 (83%) responded at baseline, 3282 of 4137 (79%) at 3 months, 2975 of 3770 (79%) at 6 months, and 2213 of 2882 (77%) at 12 months. Mean (SD) urinary function scores were 88.5 (14.3) at baseline, 53.6 (27.5) at 3 months, 68.0 (25.1) at 6 months, and 73.7 (23.0) at 12 months. Regression analysis demonstrated that older age, lower baseline urinary function score, body mass index (calculated as weight in kilograms divided by height in meters squared) of 30 or higher, clinical stage T2 or higher, and lack of bilateral nerve-sparing surgery were associated with a lower probability of reporting good urinary function 3 months after surgery. When evaluating patients with good baseline function, the rate at which individual surgeons\u27 patients reported good urinary function 3 months after surgery varied broadly (0% to 54.5%; P \u3c .001). Patients receiving surgery from top-performing surgeons were more likely to report good 3-month function. This finding persisted after accounting for patient risk factors. Conclusions and Relevance: In this study, patient- and surgeon-level urinary outcomes following prostatectomy varied substantially. Documenting surgeon-specific variations after accounting for patient factors may facilitate identification of surgical factors associated with superior outcomes