Faculty Bibliography

Cardiovascular disease (CVD) and cancer remain the leading causes of mortality in the United States, where poor diet has surpassed smoking as the leading risk factor for death, and life expectancy has hit a plateau as CVD mortality has stagnated over the past decade. Although the pathophysiology of CVD and cancer is complex and multifactorial, lifestyle factors including diet often contribute significantly to their pathogenesis. There is a wealth of observational data as well as emerging trial data supporting the benefits of a predominantly whole-food plant-based diet in the prevention of CVD and cancer. However, there is a need for implementation science to effectuate existing knowledge. Given the shortcomings of the standard American diet, characterized by excessive intake of red meat and ultraprocessed foods, while deficient in fiber and phytonutrients, it will be necessary to shift default patterns of eating to make healthy choices the path of least resistance.

BACKGROUND AND OBJECTIVE/OBJECTIVE:Prostate cancer (PC) is the second most common cancer and a leading cause of death among males. In this systematic review we evaluated cohort studies and randomized controlled trials (RCTs) on the relationship between dietary patterns and PC risk, progression, mortality, and biomarkers. METHODS:A systematic search of MEDLINE, Embase, and Cochrane Central was conducted through June 2024 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 63 studies (49 cohort studies, 14 RCTs reports) examining dietary patterns and PC outcomes were included. Study quality was assessed using Critical Appraisal Skills Programme checklists. KEY FINDINGS AND LIMITATIONS/UNASSIGNED:Among males without PC at baseline, plant-based and healthy dietary patterns (eg, higher Healthy Eating Index, lower dietary inflammatory and hyperinsulinemic scores) were generally associated with lower total PC risk. Among patients with PC, Mediterranean, plant-based, and low-inflammatory diets were more consistently linked to lower risk of progression and PC-specific mortality. RCTs testing various diet patterns showed mixed effects on prostate-specific antigen or tumor markers. Limitations include variations in diet definitions, outcomes, and follow-up duration, and residual confounding. CONCLUSIONS AND CLINICAL IMPLICATIONS/CONCLUSIONS:Healthy dietary patterns that support cardiometabolic health may also benefit PC prevention and management. While evidence appears stronger for diet in slowing PC progression after diagnosis, the impact of diet on reducing the risk of other PC outcomes should not be overlooked (eg, risk of developing PC or risk of PC death). Integrated strategies are needed to promote healthy eating, particularly for patients at risk of PC progression, as this population often has higher risk of cardiovascular disease and metabolic disorders such as diabetes.

BACKGROUND:The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as Chatbot Health Advice (CHA) studies. METHODS:CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. RESULTS:CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of CHA studies. These include Title (subitem 1a), Abstract/Summary (subitem 1b), Background (subitems 2ab), Model Identifiers (subitems 3ab), Model Details (subitems 4abc), Prompt Engineering (subitems 5ab), Query Strategy (subitems 6abcd), Performance Evaluation (subitems 7ab), Sample Size (subitem 8), Data Analysis (subitem 9a), Results (subitems 10abc), Discussion (subitems 11abc), Disclosures (subitem 12a), Funding (subitem 12b), Ethics (subitem 12c), Protocol (subitem 12d), and Data Availability (subitem 12e). CONCLUSION/CONCLUSIONS:The CHART checklist and corresponding methodological diagram were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of CHA studies.

BACKGROUND AND OBJECTIVE/OBJECTIVE:The health care sector is a significant contributor to greenhouse gas (GHG) emissions, and assessments of the environmental impacts of health services are essential. We aimed to evaluate the environmental impact of a highly common but controversial urology-specific blood test: the prostate-specific antigen (PSA) test. METHODS:e). The secondary outcome was the health impact attributed to the environmental harm of the test. KEY FINDINGS AND LIMITATIONS/UNASSIGNED:e, equivalent to driving 14.5 million miles, with a resulting human health impact of 6.6 disability-adjusted life years annually. This study focused on the PSA test itself, and not on emissions from staff, patient, or sample transportation; building infrastructure; or cleaning. CONCLUSIONS AND CLINICAL IMPLICATIONS/CONCLUSIONS:Although the carbon footprint of a single PSA test is small, the cumulative impact of the estimated total of 30 million PSA tests performed annually in the USA is substantial, especially when considering that a notable proportion of these tests may be performed on men who are unlikely to benefit.

The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as Chatbot Health Advice (CHA) studies. CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of CHA studies. These include Title (subitem 1a), Abstract/Summary (subitem 1b), Background (subitems 2ab), Model Identifiers (subitem 3ab), Model Details (subitems 4abc), Prompt Engineering (subitems 5ab), Query Strategy (subitems 6abcd), Performance Evaluation (subitems 7ab), Sample Size (subitem 8), Data Analysis (subitem 9a), Results (subitems 10abc), Discussion (subitems 11abc), Disclosures (subitem 12a), Funding (subitem 12b), Ethics (subitem 12c), Protocol (subitem 12d), and Data Availability (subitem 12e). The CHART checklist and corresponding methodological diagram were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of CHA studies.

IMPORTANCE/UNASSIGNED:The rise in chatbot health advice (CHA) studies is accompanied by heterogeneity in reporting standards, impacting their interpretability. OBJECTIVE/UNASSIGNED:To provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, 3 synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist. RESULTS/UNASSIGNED:CHART includes 12 items and 39 subitems to promote transparent and comprehensive reporting of CHA studies. These include title (subitem 1a), abstract or summary (subitem 1b), background (subitems 2ab), model identifiers (subitem 3ab), model details (subitems 4abc), prompt engineering (subitems 5ab), query strategy (subitems 6abcd), performance evaluation (subitems 7ab), sample size (subitem 8), data analysis (subitem 9a), results (subitems 10abc), discussion (subitems 11abc), disclosures (subitem 12a), funding (subitem 12b), ethics (subitem 12c), protocol (subitem 12d), and data availability (subitem 12e). CONCLUSIONS AND RELEVANCE/UNASSIGNED:The CHART checklist and corresponding methodological diagram were designed to support key stakeholders including clinicians, researchers, editors, peer reviewers, and readers in reporting, understanding, and interpreting the findings of CHA studies.

BACKGROUND:Potential rural-urban differences in prostate cancer care are understudied, particularly regarding the utilization of advanced diagnostic tests. Herein we examined variations in Positron Emission Tomography (PET) utilization for prostate cancer care, including diagnosis, staging and treatment planning, across residential regions in the United States. METHODS:Patients newly diagnosed with prostate cancer between 2019 and 2021 and post-diagnostic PETs were identified using full Medicare claims data. PET use was assessed in all newly diagnosed patients, though indications vary by risk. Patients' counties were categorized as metro, urban, or rural, from most to least urbanized. Regional PET utilization was further examined at the level of hospital referral regions. A multivariable logistic regression model was performed to assess the impact of rurality on PET imaging. A secondary analysis included an interaction term for race to explore the effect of residence on PET imaging by racial group. RESULTS:Overall, 495 865 patients were included in the analysis: 393 861 (79.4%) lived in metro, 56 698 (11.4%) in urban and 39 707 (8.0%) in rural counties. Patients in metro counties underwent PET imaging more often (8.4%) than patients in urban (7.3%) or rural counties (7.2%), p < 0.0001. At a level of hospital referral region, PET utilization rates ranged from 2.2 to 20.8%. PET imaging was more commonly performed in White compared to Black or Hispanic patients. Rural patients were less likely to undergo PET imaging compared to metro patients (odds ratio [OR] 0.87, 95% Confidence interval [CI]: 0.82-0.92 p < 0.0001). Rural Black (OR 0.69, 95%CI 0.57-0.83, p < 0.0001) and rural White patients (OR 0.89, 95%CI 0.83-0.94 p < 0.0001) were less likely to obtain PET imaging compared to their metro counterparts, p-interaction < 0.0001. CONCLUSION/CONCLUSIONS:Rural patients were less likely to undergo PET imaging than metro patients. The effect of rurality was most pronounced among Black patients. Our findings underscore the need for strategies to support equitable use of PET imaging.

Germline Testing (GT) for prostate cancer (PCA) is now central to PCA care and hereditary cancer assessment, with a rising role in PCA screening approaches. Guidelines have significantly expanded to include testing patients with metastatic PCA, advanced PCA or with high-risk features, and for males with or without PCA with a strong family cancer history to identify hereditary cancer syndromes for patients and their families. However, the expansion of GT has overwhelmed genetic counselling programs, necessitating the development and evaluation of alternate genetic delivery models. Furthermore, disparities in engagement in PCA GT are of major concern for impacting PCA-related and overall cancer-related outcomes for patients and their families. This review focuses on integrating PCA GT guidelines with implementation strategies and addressing PCA GT disparities to help inform current and future strategies to enhance the benefits of GT across populations.

BACKGROUND:Personalized therapeutic approaches for localized prostate cancer have evolved significantly, with tissue-based biomarker tests supplementing traditional risk stratification tools. However, national testing patterns and geographic variability remain limited a decade after coverage implementation. We aimed to assess current nationwide utilization and urban-rural differences in tissue-based biomarker testing. METHODS:Using full Medicare claims data, we retrospectively identified patients with newly diagnosed prostate cancer and tissue-based biomarker testing claims from 2019 to 2023. Patients' county of residence was categorized as metro, urban, or rural. Regional testing rates were further assessed across hospital referral regions. A multivariable logistic regression model was performed to assess the effect of residence on test receipt. RESULTS:Our final cohort included 749,202 patients, of whom 79.5% lived in metro, 11.4% in urban and 8.00% in rural counties. Overall, 86,908 (11.6%) patients underwent tissue-based biomarker tests. Hospital referral region-level testing rates ranged from 2.4% to 42.7%. Rural patients were 18% less likely to undergo testing compared to metro patients (Odds Ratio [OR] 0.82 95% Confidence Interval [CI] 0.73-0.91). Independently, the odds of undergoing testing were lower among Black (OR 0.82, 95% CI 0.77-0.88) and Hispanic patients (OR 0.80, 95% CI 0.73-0.88) compared to White patients. CONCLUSION/CONCLUSIONS:This study reveals high geographic variability in tissue-based biomarker testing for prostate cancer. Further, Black and Hispanic patients were less likely to receive testing. Our findings highlight regional practice variation in the use of advanced, not routinely recommended tests and underscore the need to minimize disparities in diagnostic access.