Ericka M Sohlberg1, I-Chun Thomas2, Jaden Yang3, Kristopher Kapphahn3, Timothy J Daskivich4, Ted A Skolarus5, Jeremy B Shelton6, Danil V Makarov7, Jonathan Bergman6, Christine Ko Bang8, Mary K Goldstein9, Todd H Wagner10, James D Brooks1, Manisha Desai11, John T Leppert12. 1. Department of Urology, Stanford University School of Medicine, Stanford, CA. 2. Veterans Affairs Palo Alto Health Care System, Palo Alto, CA. 3. Quantitative Sciences Unit, Stanford University, Stanford, CA. 4. Division of Urology, Cedars-Sinai Medical Center, Los Angeles, CA. 5. Department of Urology, University of Michigan, VA Ann Arbor Healthcare System, Center for Clinical Management and Research, Ann Arbor, MI. 6. Department of Urology, UCLA; West Los Angeles VA Medical Center, LA County Department of Health Services, Los Angeles, CA. 7. Departments of Urology and Population Health, New York University Langone Medical Center, Veterans Affairs New York Harbor Healthcare System, New York, NY. 8. Department of Radiation Oncology, VA Maryland Health Care System, Baltimore, MD. 9. Veterans Affairs Palo Alto Health Care System, Palo Alto, CA; Department of Medicine, Stanford University School of Medicine, Stanford, CA. 10. Department of Surgery, Stanford University School of Medicine, Stanford, CA; VA Center for Innovation to Implementation, Palo Alto, CA. 11. Quantitative Sciences Unit, Stanford University, Stanford, CA; Department of Medicine, Stanford University School of Medicine, Stanford, CA. 12. Department of Urology, Stanford University School of Medicine, Stanford, CA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA; Department of Medicine, Stanford University School of Medicine, Stanford, CA; VA Center for Innovation to Implementation, Palo Alto, CA. Electronic address: jleppert@stanford.edu.
Abstract
PURPOSE: Accurate life expectancy estimates are required to inform prostate cancer treatment decisions. However, few models are specific to the population served or easily implemented in a clinical setting. We sought to create life expectancy estimates specific to Veterans diagnosed with prostate cancer. MATERIALS AND METHODS: Using national Veterans Health Administration electronic health records, we identified Veterans diagnosed with prostate cancer between 2000 and 2015. We abstracted demographics, comorbidities, oncologic staging, and treatment information. We fit Cox Proportional Hazards models to determine the impact of age, comorbidity, cancer risk, and race on survival. We stratified life expectancy estimates by age, comorbidity and cancer stage. RESULTS: Our analytic cohort included 145,678 patients. Survival modeling demonstrated the importance of age and comorbidity across all cancer risk categories. Life expectancy estimates generated from age and comorbidity data were predictive of overall survival (C-index 0.676, 95% CI 0.674-0.679) and visualized using Kaplan-Meier plots and heatmaps stratified by age and comorbidity. Separate life expectancy estimates were generated for patients with localized or advanced disease. These life expectancy estimates calibrate well across prostate cancer risk categories. CONCLUSIONS: Life expectancy estimates are essential to providing patient-centered prostate cancer care. We developed accessible life expectancy estimation tools for Veterans diagnosed with prostate cancer that can be used in routine clinical practice to inform medical-decision making. Published by Elsevier Inc.
PURPOSE: Accurate life expectancy estimates are required to inform prostate cancer treatment decisions. However, few models are specific to the population served or easily implemented in a clinical setting. We sought to create life expectancy estimates specific to Veterans diagnosed with prostate cancer. MATERIALS AND METHODS: Using national Veterans Health Administration electronic health records, we identified Veterans diagnosed with prostate cancer between 2000 and 2015. We abstracted demographics, comorbidities, oncologic staging, and treatment information. We fit Cox Proportional Hazards models to determine the impact of age, comorbidity, cancer risk, and race on survival. We stratified life expectancy estimates by age, comorbidity and cancer stage. RESULTS: Our analytic cohort included 145,678 patients. Survival modeling demonstrated the importance of age and comorbidity across all cancer risk categories. Life expectancy estimates generated from age and comorbidity data were predictive of overall survival (C-index 0.676, 95% CI 0.674-0.679) and visualized using Kaplan-Meier plots and heatmaps stratified by age and comorbidity. Separate life expectancy estimates were generated for patients with localized or advanced disease. These life expectancy estimates calibrate well across prostate cancer risk categories. CONCLUSIONS: Life expectancy estimates are essential to providing patient-centered prostate cancer care. We developed accessible life expectancy estimation tools for Veterans diagnosed with prostate cancer that can be used in routine clinical practice to inform medical-decision making. Published by Elsevier Inc.
Entities:
Keywords:
Comorbidity; Life expectancy; Prostatic neoplasms; Veterans Health
Authors: Timothy J Wilt; Michael K Brawer; Karen M Jones; Michael J Barry; William J Aronson; Steven Fox; Jeffrey R Gingrich; John T Wei; Patricia Gilhooly; B Mayer Grob; Imad Nsouli; Padmini Iyer; Ruben Cartagena; Glenn Snider; Claus Roehrborn; Roohollah Sharifi; William Blank; Parikshit Pandya; Gerald L Andriole; Daniel Culkin; Thomas Wheeler Journal: N Engl J Med Date: 2012-07-19 Impact factor: 91.245
Authors: Mary Beth Landrum; Nancy L Keating; Elizabeth B Lamont; Samuel R Bozeman; Steven H Krasnow; Lawrence Shulman; Jennifer R Brown; Craig C Earle; Michael Rabin; Barbara J McNeil Journal: J Clin Oncol Date: 2012-03-05 Impact factor: 44.544
Authors: Nancy L Keating; Mary Beth Landrum; Elizabeth B Lamont; Samuel R Bozeman; Steven H Krasnow; Lawrence N Shulman; Jennifer R Brown; Craig C Earle; William K Oh; Michael Rabin; Barbara J McNeil Journal: Ann Intern Med Date: 2011-06-07 Impact factor: 25.391
Authors: Waddah B Al-Refaie; Selwyn M Vickers; Wei Zhong; Helen Parsons; David Rothenberger; Elizabeth B Habermann Journal: Ann Surg Date: 2011-09 Impact factor: 12.969
Authors: Timothy J Daskivich; I-Chun Thomas; Michael Luu; Jeremy B Shelton; Danil V Makarov; Ted A Skolarus; John T Leppert Journal: J Urol Date: 2019-08-08 Impact factor: 7.450
Authors: A V D'Amico; R Whittington; S B Malkowicz; D Schultz; K Blank; G A Broderick; J E Tomaszewski; A A Renshaw; I Kaplan; C J Beard; A Wein Journal: JAMA Date: 1998-09-16 Impact factor: 56.272
Authors: Robert T Dess; Holly E Hartman; Brandon A Mahal; Payal D Soni; William C Jackson; Matthew R Cooperberg; Christopher L Amling; William J Aronson; Christopher J Kane; Martha K Terris; Zachary S Zumsteg; Santino Butler; Joseph R Osborne; Todd M Morgan; Rohit Mehra; Simpa S Salami; Amar U Kishan; Chenyang Wang; Edward M Schaeffer; Mack Roach; Thomas M Pisansky; William U Shipley; Stephen J Freedland; Howard M Sandler; Susan Halabi; Felix Y Feng; James J Dignam; Paul L Nguyen; Matthew J Schipper; Daniel E Spratt Journal: JAMA Oncol Date: 2019-07-01 Impact factor: 31.777
Authors: Kristina Vaculik; Michael Luu; Lauren E Howard; William Aronson; Martha Terris; Christopher Kane; Christopher Amling; Matthew Cooperberg; Stephen J Freedland; Timothy J Daskivich Journal: JAMA Netw Open Date: 2021-06-01
Authors: Elizabeth C Chase; Alex K Bryant; Yilun Sun; William C Jackson; Daniel E Spratt; Robert T Dess; Matthew J Schipper Journal: BJU Int Date: 2022-04-24 Impact factor: 5.969
Authors: Simon John Christoph Soerensen; I-Chun Thomas; Bogdana Schmidt; Timothy J Daskivich; Ted A Skolarus; Christian Jackson; Thomas F Osborne; Glenn M Chertow; James D Brooks; David H Rehkopf; John T Leppert Journal: Urology Date: 2021-06-15 Impact factor: 2.633