Aaron A Laviana1, Zhiguo Zhao2, Li-Ching Huang2, Tatsuki Koyama2, Ralph Conwill3, Karen Hoffman4, Michael Goodman5, Ann S Hamilton6, Xiao-Cheng Wu7, Lisa E Paddock8, Antoinette Stroup8, Matthew R Cooperberg9, Mia Hashibe10, Brock B O'Neil11, Sherrie H Kaplan12, Sheldon Greenfield12, David F Penson13, Daniel A Barocas13. 1. Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: aaron.a.laviana@vumc.org. 2. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Office of Patient and Community Education, Patient Advocacy Program, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA. 4. Department of Radiation Oncology, University of Texas M. D. Anderson Center, Huston, TX, USA. 5. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA. 6. Department of Preventative Medicine, Keck School of Medicine at the University of Southern California, Los Angeles, CA, USA. 7. Department of Epidemiology, Louisiana State University New Orleans School of Public Health, New Orleans, LA, USA. 8. Department of Epidemiology, Cancer Institute of New Jersey, Rutgers Health, New Brunswick, NJ, USA. 9. Department of Urology, University of California, San Francisco, CA, USA. 10. Department of Family and Preventative Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA. 11. Department of Urology, University of Utah Health, Salt Lake City, UT, USA. 12. Department of Medicine, University of California Irvine, Irvine, CA, USA. 13. Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA.
Abstract
BACKGROUND: Shared decision making to guide treatment of localized prostate cancer requires delivery of the anticipated quality of life (QOL) outcomes of contemporary treatment options (including radical prostatectomy [RP], intensity-modulated radiation therapy [RT], and active surveillance [AS]). Predicting these QOL outcomes based on personalized features is necessary. OBJECTIVE: To create an easy-to-use tool to predict personalized sexual, urinary, bowel, and hormonal function outcomes after RP, RT, and AS. DESIGN, SETTING, AND PARTICIPANTS: A prospective, population-based cohort study was conducted utilizing US cancer registries of 2563 men diagnosed with localized prostate cancer in 2011-2012. INTERVENTION: Patient-reported urinary, sexual, and bowel function up to 5 yr after treatment. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Patient-reported urinary, sexual, bowel, and hormonal function through 5 yr after treatment were collected using the 26-item Expanded Prostate Index Composite (EPIC-26) questionnaire. Comprehensive models to predict domain scores were fit, which included age, race, D'Amico classification, body mass index, EPIC-26 baseline function, treatment, and standardized scores measuring comorbidity, general QOL, and psychosocial health. We reduced these models by removing the instrument scores and replacing D'Amico classification with prostate-specific antigen (PSA) and Gleason score. For the final model, we performed bootstrap internal validation to assess model calibration from which an easy-to-use web-based tool was developed. RESULTS AND LIMITATIONS: The prediction models achieved bias-corrected R-squared values of 0.386, 0.232, 0.183, 0.214, and 0.309 for sexual function, urinary incontinence, urinary irritative, bowel, and hormonal domains, respectively. Differences in R-squared values between the comprehensive and parsimonious models were small in magnitude. Calibration was excellent. The web-based tool is available at https://statez.shinyapps.io/PCDSPred/. CONCLUSIONS: Functional outcomes after treatment for localized prostate cancer can be predicted at the time of diagnosis based on age, race, PSA, biopsy grade, baseline function, and a general question regarding overall health. Providers and patients can use this prediction tool to inform shared decision making. PATIENT SUMMARY: In this report, we studied patient-reported sexual, urinary, hormonal, and bowel function through 5 yr after treatment with radical prostatectomy, radiation therapy, or active surveillance for localized prostate cancer. We developed a web-based predictive tool that can be used to predict one's outcomes after treatment based on age, race, prostate-specific antigen, biopsy grade, pretreatment baseline function, and a general question regarding overall health. We hope both patients and providers can use this tool to better understand expected outcomes after treatment, further enhancing shared decision making between providers and patients.
BACKGROUND: Shared decision making to guide treatment of localized prostate cancer requires delivery of the anticipated quality of life (QOL) outcomes of contemporary treatment options (including radical prostatectomy [RP], intensity-modulated radiation therapy [RT], and active surveillance [AS]). Predicting these QOL outcomes based on personalized features is necessary. OBJECTIVE: To create an easy-to-use tool to predict personalized sexual, urinary, bowel, and hormonal function outcomes after RP, RT, and AS. DESIGN, SETTING, AND PARTICIPANTS: A prospective, population-based cohort study was conducted utilizing US cancer registries of 2563 men diagnosed with localized prostate cancer in 2011-2012. INTERVENTION: Patient-reported urinary, sexual, and bowel function up to 5 yr after treatment. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Patient-reported urinary, sexual, bowel, and hormonal function through 5 yr after treatment were collected using the 26-item Expanded Prostate Index Composite (EPIC-26) questionnaire. Comprehensive models to predict domain scores were fit, which included age, race, D'Amico classification, body mass index, EPIC-26 baseline function, treatment, and standardized scores measuring comorbidity, general QOL, and psychosocial health. We reduced these models by removing the instrument scores and replacing D'Amico classification with prostate-specific antigen (PSA) and Gleason score. For the final model, we performed bootstrap internal validation to assess model calibration from which an easy-to-use web-based tool was developed. RESULTS AND LIMITATIONS: The prediction models achieved bias-corrected R-squared values of 0.386, 0.232, 0.183, 0.214, and 0.309 for sexual function, urinary incontinence, urinary irritative, bowel, and hormonal domains, respectively. Differences in R-squared values between the comprehensive and parsimonious models were small in magnitude. Calibration was excellent. The web-based tool is available at https://statez.shinyapps.io/PCDSPred/. CONCLUSIONS: Functional outcomes after treatment for localized prostate cancer can be predicted at the time of diagnosis based on age, race, PSA, biopsy grade, baseline function, and a general question regarding overall health. Providers and patients can use this prediction tool to inform shared decision making. PATIENT SUMMARY: In this report, we studied patient-reported sexual, urinary, hormonal, and bowel function through 5 yr after treatment with radical prostatectomy, radiation therapy, or active surveillance for localized prostate cancer. We developed a web-based predictive tool that can be used to predict one's outcomes after treatment based on age, race, prostate-specific antigen, biopsy grade, pretreatment baseline function, and a general question regarding overall health. We hope both patients and providers can use this tool to better understand expected outcomes after treatment, further enhancing shared decision making between providers and patients.
Authors: A V D'Amico; R Whittington; S B Malkowicz; J Fondurulia; M H Chen; I Kaplan; C J Beard; J E Tomaszewski; A A Renshaw; A Wein; C N Coleman Journal: J Clin Oncol Date: 1999-01 Impact factor: 44.544
Authors: Timothy J Wilt; Karen M Jones; Michael J Barry; Gerald L Andriole; Daniel Culkin; Thomas Wheeler; William J Aronson; Michael K Brawer Journal: N Engl J Med Date: 2017-07-13 Impact factor: 91.245
Authors: Daniel A Barocas; JoAnn Alvarez; Matthew J Resnick; Tatsuki Koyama; Karen E Hoffman; Mark D Tyson; Ralph Conwill; Dan McCollum; Matthew R Cooperberg; Michael Goodman; Sheldon Greenfield; Ann S Hamilton; Mia Hashibe; Sherrie H Kaplan; Lisa E Paddock; Antoinette M Stroup; Xiao-Cheng Wu; David F Penson Journal: JAMA Date: 2017-03-21 Impact factor: 56.272
Authors: Aaron A Laviana; Agustin Hernandez; Li-Ching Huang; Zhiguo Zhao; Tatsuki Koyama; Ralph Conwill; Karen Hoffman; Irene D Feurer; Michael Goodman; Ann S Hamilton; Xiao-Cheng Wu; Lisa E Paddock; Antoinette Stroup; Matthew R Cooperberg; Mia Hashibe; Brock B O'Neil; Sherrie H Kaplan; Sheldon Greenfield; David F Penson; Daniel A Barocas Journal: J Urol Date: 2019-06-19 Impact factor: 7.450
Authors: Lindsay A Hampson; Janet E Cowan; Shoujun Zhao; Peter R Carroll; Matthew R Cooperberg Journal: Eur Urol Date: 2015-02-02 Impact factor: 20.096
Authors: Nnenaya Agochukwu-Mmonu; Adharsh Murali; Daniela Wittmann; Brian Denton; Rodney L Dunn; James Montie; James Peabody; David Miller; Karandeep Singh Journal: Eur Urol Open Sci Date: 2022-04-18