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Justice AC, Tate JP, Howland F, Gaziano JM, Kelley MJ, McMahon B, Haiman C, Wadia R, Madduri R, Danciu I, Leppert JT, Leapman MS, Thurtle D, Gnanapragasam VJ. Adaption and National Validation of a Tool for Predicting Mortality from Other Causes Among Men with Nonmetastatic Prostate Cancer. Eur Urol Oncol 2024; 7:923-932. [PMID: 38171965 DOI: 10.1016/j.euo.2023.11.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND An electronic health record-based tool could improve accuracy and eliminate bias in provider estimation of the risk of death from other causes among men with nonmetastatic cancer. OBJECTIVE To recalibrate and validate the Veterans Aging Cohort Study Charlson Comorbidity Index (VACS-CCI) to predict non-prostate cancer mortality (non-PCM) and to compare it with a tool predicting prostate cancer mortality (PCM). DESIGN, SETTING, AND PARTICIPANTS An observational cohort of men with biopsy-confirmed nonmetastatic prostate cancer, enrolled from 2001 to 2018 in the national US Veterans Health Administration (VA), was divided by the year of diagnosis into the development (2001-2006 and 2008-2018) and validation (2007) sets. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Mortality (all cause, non-PCM, and PCM) was evaluated. Accuracy was assessed using calibration curves and C statistic in the development, validation, and combined sets; overall; and by age (<65 and 65+ yr), race (White and Black), Hispanic ethnicity, and treatment groups. RESULTS AND LIMITATIONS Among 107 370 individuals, we observed 24 977 deaths (86% non-PCM). The median age was 65 yr, 4947 were Black, and 5010 were Hispanic. Compared with CCI and age alone (C statistic 0.67, 95% confidence interval [CI] 0.67-0.68), VACS-CCI demonstrated improved validated discrimination (C statistic 0.75, 95% CI 0.74-0.75 for non-PCM). The prostate cancer mortality tool also discriminated well in validation (C statistic 0.81, 95% CI 0.78-0.83). Both were well calibrated overall and within subgroups. Owing to missing data, 18 009/125 379 (14%) were excluded, and VACS-CCI should be validated outside the VA prior to outside application. CONCLUSIONS VACS-CCI is ready for implementation within the VA. Electronic health record-assisted calculation is feasible, improves accuracy over age and CCI alone, and could mitigate inaccuracy and bias in provider estimation. PATIENT SUMMARY Veterans Aging Cohort Study Charlson Comorbidity Index is ready for application within the Veterans Health Administration. Electronic health record-assisted calculation is feasible, improves accuracy over age and Charlson Comorbidity Index alone, and might help mitigate inaccuracy and bias in provider estimation of the risk of non-prostate cancer mortality.
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Affiliation(s)
- Amy C Justice
- VA Connecticut Healthcare, West Haven, CT, USA; Pain Research, Informatics, Multimorbidities, Education (PRIME) Center, VA Connecticut Healthcare System, West Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA; School of Public Health, Yale University, New Haven, CT, USA.
| | - Janet P Tate
- VA Connecticut Healthcare, West Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Frank Howland
- Wabash College Economics Department, Crawfordsville, IN, USA
| | | | - Michael J Kelley
- Durham VA Health Care System, Durham, NC, USA; Cancer Institute and Department of Medicine, Duke University, Durham, NC, USA
| | | | - Christopher Haiman
- Center for Genetic Epidemiology, USC Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Roxanne Wadia
- Department of Anatomic Pathology and Lab Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ravi Madduri
- Data Science Learning Division, Argonne Research Library, Lemont, IL, USA
| | - Ioana Danciu
- Oak Ridge National Laboratory, Oak Ridge, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John T Leppert
- Department of Urology, Stanford University, Stanford, CA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Michael S Leapman
- VA Connecticut Healthcare, West Haven, CT, USA; Department of Urology, Yale School of Medicine, New Haven, CT, USA
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Song Z, Zhang W, Jiang Q, Deng L, Du L, Mou W, Lai Y, Zhang W, Yang Y, Lim J, Liu K, Park JY, Ng CF, Ong TA, Wei Q, Li L, Wei X, Chen M, Cao Z, Wang F, Chen R. Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study. Int J Surg 2023; 109:3848-3860. [PMID: 37988414 PMCID: PMC10720852 DOI: 10.1097/js9.0000000000000862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/22/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa. PATIENTS AND METHODS A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses. RESULTS Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively. CONCLUSIONS The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.
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Affiliation(s)
- Zijian Song
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
| | - Wei Zhang
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Qingchao Jiang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai
| | - Longxin Deng
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Le Du
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai
| | - Weiming Mou
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Yancheng Lai
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Wenhui Zhang
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Yang Yang
- Department of Clinical Laboratory, Nanjing Jinling Hospital, Nanjing University School of Medicine
| | - Jasmine Lim
- Department of Urology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Kang Liu
- SH Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jae Young Park
- Department of Urology, Korea University Ansan Hospital, Soule, Korea
| | - Chi-Fai Ng
- SH Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Teng Aik Ong
- Department of Urology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan
| | - Lei Li
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an Shaanxi
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou
| | - Ming Chen
- Department of Urology, Zhongda Hospital, Southeast University, Nanjing
| | - Zhixing Cao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai
| | - Fubo Wang
- School of Life Sciences, Guangxi Medical University, Nanning, Guangxi
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Guangxi China
| | - Rui Chen
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
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Soerensen SJC, Montez-Rath ME, Cheng I, Gomez SL, Oh DL, Jackson C, Li J, Rehkopf D, Chertow GM, Langston ME, Ganesan C, Pao AC, Chung BI, Leppert JT. Groundwater constituents and the incidence of kidney cancer. Cancer 2023; 129:3309-3317. [PMID: 37287332 DOI: 10.1002/cncr.34898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/26/2023] [Accepted: 04/29/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND Kidney cancer incidence demonstrates significant geographic variation suggesting a role for environmental risk factors. This study sought to evaluate associations between groundwater exposures and kidney cancer incidence. METHODS The authors identified constituents from 18,506 public groundwater wells in all 58 California counties measured in 1996-2010, and obtained county-level kidney cancer incidence data from the California Cancer Registry for 2003-2017. The authors developed a water-wide association study (WWAS) platform using XWAS methodology. Three cohorts were created with 5 years of groundwater measurements and 5-year kidney cancer incidence data. The authors fit Poisson regression models in each cohort to estimate the association between county-level average constituent concentrations and kidney cancer, adjusting for known risk factors: sex, obesity, smoking prevalence, and socioeconomic status at the county level. RESULTS Thirteen groundwater constituents met stringent WWAS criteria (a false discovery rate <0.10 in the first cohort, followed by p values <.05 in subsequent cohorts) and were associated with kidney cancer incidence. The seven constituents directly related to kidney cancer incidence (and corresponding standardized incidence ratios) were chlordane (1.06; 95% confidence interval [CI], 1.02-1.10), dieldrin (1.04; 95% CI, 1.01-1.07), 1,2-dichloropropane (1.04; 95% CI, 1.02-1.05), 2,4,5-TP (1.03; 95% CI, 1.01-1.05), glyphosate (1.02; 95% CI, 1.01-1.04), endothall (1.02; 95% CI, 1.01-1.03), and carbaryl (1.02; 95% CI, 1.01-1.03). Among the six constituents inversely related to kidney cancer incidence, the standardized incidence ratio furthest from the null was for bromide (0.97; 95% CI, 0.94-0.99). CONCLUSIONS This study identified several groundwater constituents associated with kidney cancer. Public health efforts to reduce the burden of kidney cancer should consider groundwater constituents as environmental exposures that may be associated with the incidence of kidney cancer.
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Affiliation(s)
- Simon John Christoph Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, California, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Maria E Montez-Rath
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Scarlett Lin Gomez
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Debora L Oh
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Christian Jackson
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Jinhui Li
- Department of Urology, Stanford University School of Medicine, Stanford, California, USA
| | - David Rehkopf
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Glenn M Chertow
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Marvin E Langston
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
| | - Calyani Ganesan
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Alan C Pao
- Department of Urology, Stanford University School of Medicine, Stanford, California, USA
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Division of Medicine, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
| | - Benjamin I Chung
- Department of Urology, Stanford University School of Medicine, Stanford, California, USA
| | - John T Leppert
- Department of Urology, Stanford University School of Medicine, Stanford, California, USA
- Division of Nephrology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
- Division of Urology, Veterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
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Lin HY, Zhu X, Aucoin AJ, Fu Q, Park JY, Tseng TS. Dietary and Serum Antioxidants Associated with Prostate-Specific Antigen for Middle-Aged and Older Men. Nutrients 2023; 15:3298. [PMID: 37571238 PMCID: PMC10420876 DOI: 10.3390/nu15153298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 07/22/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
High prostate-specific antigen (PSA) levels can indicate potential prostate problems and are a warning sign of prostate cancer. The impact of antioxidants on the PSA of generally healthy men is understudied. This study aims to evaluate 14 dietary and endogenous antioxidants associated with PSA levels for United States (US) men. We assessed 7398 men using the 2003-2010 US population-based National Health and Nutrition Examination Survey (NHANES). The PSA levels were categorized into three groups: Normal, borderline, and elevated levels. We performed analyses for middle-aged and older groups aged 40-64.9 and ≥65, respectively. The weighted multinomial regressions were performed to evaluate antioxidants associated with the PSA status. For results, 0.3% and 3.4% of middle-aged and older men, respectively, had elevated PSA (>10 ng/mL). Men with a higher serum albumin level had a lower risk of an elevated PSA, adjusting for age. The magnitude of albumin's impact on PSA is larger in middle-aged men than in older men (OR of elevated PSA = 0.82 and 0.90, respectively, interaction p = 0.002). Other antioxidants are not associated with PSA. Our findings support men with low serum albumin tend to have an elevated PSA level, so related interventions can be considered to decrease PSA for maintaining prostate health.
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Affiliation(s)
- Hui-Yi Lin
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Xiaodan Zhu
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Alise J. Aucoin
- School of Medicine, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA;
| | - Qiufan Fu
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA
| | - Jong Y. Park
- Department of Cancer Epidemiology, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA;
| | - Tung-Sung Tseng
- Behavior and Community Health Sciences Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA 70112, USA;
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Xu K, Yan Y, Cheng C, Li S, Liao Y, Zeng J, Chen Z, Zhou J. The relationship between serum albumin and prostate-specific antigen: A analysis of the National Health and Nutrition Examination Survey, 2003-2010. Front Public Health 2023; 11:1078280. [PMID: 36950094 PMCID: PMC10025559 DOI: 10.3389/fpubh.2023.1078280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/16/2023] [Indexed: 03/08/2023] Open
Abstract
Background Previous studies have shown that serum albumin is associated with prostate cancer (PCa), but not with prostate-specific antigen (PSA) levels in populations without PCa history. Therefore, we analyzed secondary data provided by the National Health and Nutrition Examination Survey (NHANES) (2003-2010). Methods In total, 5,469 participants were selected from the NHANES database (2003-2010). Serum albumin and PSA levels were serially considered independent and dependent variables, serially. A number of covariates were included in this study, including demographic, dietary, physical examination, and comorbidity data. Using weighted linear regression model and smooth curve fitting, the linear and non-linear relationship between serum albumin and PSA was investigated. Results After modulating underlying interference factors, the weighted multivariate linear regression analysis revealed that serum albumin did not independently predict PSA levels (β = -0.009 95%CI: -0.020, 0.002). Nevertheless, a non-linear relationship was found between serum albumin and PSA, with a point of 41 g/L. Left of the inflection point, the effect size, 95%CI, and P-value were 0.019 (log2 transformation) (-0.006, 0.043) and 0.1335, respectively. We found a negative association between serum albumin and PSA on the right side of the inflection point, with effect size, 95%CI, and a P-value of -0.022 (log2 transformation) (-0.037, -0.007), 0.0036. Conclusion In summary, serum albumin and PSA levels are not linearly related. When serum albumin levels exceed 41 g, serum albumin levels are negatively associated with PSA levels.
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Velaer K, Thomas IC, Yang J, Kapphahn K, Metzner TJ, Golla A, Hoerner CR, Fan AC, Master V, Chertow GM, Brooks JD, Patel CJ, Desai M, Leppert JT. Clinical laboratory tests associated with survival in patients with metastatic renal cell carcinoma: A Laboratory Wide Association Study (LWAS). Urol Oncol 2021; 40:12.e23-12.e30. [PMID: 34580027 DOI: 10.1016/j.urolonc.2021.08.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/03/2021] [Accepted: 08/13/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Prognostic models for patients with metastatic renal cell carcinoma (mRCC) include select laboratory values. These models have important limitations, including reliance on a limited array of laboratory tests, and use of dichotomous ("high-low") cutoffs. We applied a Laboratory-Wide Association Study (LWAS) framework to systematically evaluate common clinical laboratory results associated with survival for patients diagnosed with mRCC. METHODS We used laboratory data for 3,385 patients diagnosed with mRCC from 2002 to 2017. We developed a LWAS framework, to examine the association with 53 common clinical laboratory tests results (641,712 measurements) and overall survival. We employed false-discovery rate to test the association of multiple laboratory tests with survival, and validated these results using 3 separate cohorts to generate a standardized hazard ratio (sHR), reported for a 1 standard deviation unit change in each laboratory test. RESULTS The LWAS approach confirmed the association of laboratory values currently used in prognostic models with survival, including calcium (HR 1.35, 95%CI 1.24-1.48), leukocyte count (HR 1.40, 95%CI 1.30-1.51), platelet count (HR 1.36, 95%CI 1.27-1.51), and hemoglobin (HR 0.79, 95%CI 0.72-0.86). Use of these tests as continuous variables improved model performance. LWAS also identified acute phase reactants associated with survival not typically included in prognostic models, including serum albumin (HR 0.66, 95%CI 0.61-0.72), ferritin (HR 1.25, 95%CI 1.08-1.45), alkaline phosphatase (HR 1.31, 95%CI 1.23-1.40), and C-reactive protein (HR 1.70, 95%CI 1.14-2.53). CONCLUSIONS Routinely measured laboratory tests can refine current prognostic models, facilitate comparisons across clinical trial cohorts, and match patients with specific systemic therapies.
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Affiliation(s)
- Kyla Velaer
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - I-Chun Thomas
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA
| | - Jaden Yang
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Kristopher Kapphahn
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Thomas J Metzner
- Department of Urology, Stanford University School of Medicine, Stanford, CA; Pacific Northwest University of Health Sciences, Yakima, WA
| | - Abhinav Golla
- Department of Ophthalmology, UCLA School of Medicine, Los Angeles, CA
| | - Christian R Hoerner
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Alice C Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA; Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Viraj Master
- Department of Urology, Emory University School of Medicine, Atlanta, GA
| | - Glenn M Chertow
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - James D Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Manisha Desai
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - John T Leppert
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA; Department of Urology, Stanford University School of Medicine, Stanford, CA; Department of Medicine, Stanford University School of Medicine, Stanford, CA.
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