Amy Trentham-Dietz1,2, Mehmet Ali Ergun3, Oguzhan Alagoz4,3, Natasha K Stout5, Ronald E Gangnon6,4,7, John M Hampton6,4, Kim Dittus8,9, Ted A James10, Pamela M Vacek9,11, Sally D Herschorn9,12, Elizabeth S Burnside4,13, Anna N A Tosteson14, Donald L Weaver9,15, Brian L Sprague16,17. 1. Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, WARF Room 307, Madison, WI, 53726, USA. trentham@wisc.edu. 2. University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA. trentham@wisc.edu. 3. Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA. 4. University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA. 5. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. 6. Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, WARF Room 307, Madison, WI, 53726, USA. 7. Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. 8. Department of Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA. 9. University of Vermont Cancer Center, Burlington, VT, USA. 10. BreastCare Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. 11. Department of Medical Biostatistics, Larner College of Medicine, University of Vermont, Burlington, VT, USA. 12. Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA. 13. Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. 14. The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA. 15. Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA. 16. University of Vermont Cancer Center, Burlington, VT, USA. brian.sprague@uvm.edu. 17. Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA. brian.sprague@uvm.edu.
Abstract
PURPOSE: Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS. METHODS: A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test. RESULTS: Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population. CONCLUSION: A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.
PURPOSE: Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS. METHODS: A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test. RESULTS: Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population. CONCLUSION: A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.
Entities:
Keywords:
Breast cancer; Costs; Ductal carcinoma in situ; Indolent disease; Prognosis; Quality-adjusted life years
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