Shi-Yi Wang1, Fuad Abujarad2, Tiange Chen3, Suzanne B Evans4, Brigid K Killelea5, Sarah S Mougalian6, Liana Fraenkel7, Cary P Gross8. 1. Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, United States of America; Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, United States of America. Electronic address: shiyi.wang@yale.edu. 2. Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, United States of America. 3. Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, United States of America. 4. Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, United States of America; Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT, United States of America. 5. Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, United States of America; Department of Surgery, Yale University School of Medicine, New Haven, CT, United States of America. 6. Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, United States of America; Section of Medical Oncology, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States of America. 7. Section of Rheumatology, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States of America. 8. Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale Cancer Center, Yale University School of Medicine, New Haven, CT, United States of America; Section of General Internal Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States of America.
Abstract
OBJECTIVE: Among older adult women with early-stage breast cancer who undergo lumpectomy, the benefits of radiotherapy vary according to tumor characteristics and life expectancy. We aimed to develop a risk calculator to predict individualized probability of long-term survival and local recurrence, accounting for these factors. METHODS: We developed a simulation model to estimate an individual patient's risk of local recurrence and all-cause mortality according to age, comorbidities, functional status, tumor characteristics, and radiotherapy status. We integrated two existing prediction models, the Early Breast Cancer Trialist's Collaborative Group prediction model for breast cancer specific outcomes and ePrognosis for life expectancy. An online risk calculator "Radiotherapy for Older Women (ROW)" was developed through an iterative multi-stage process, that included individual consultation and group meetings with an advisory committee (AC) comprised of patients, advocates, clinicians, and researchers. RESULTS: We developed the tool over 40 months and had 15 group meetings. The risk calculator developed as a simulation model with 16 factors (5 tumor-related, 3 demographic, 4 comorbidities, and 4 functional statuses). Across 56,700 simulated scenarios, the benefit of RT in terms of absolute 10-year local recurrence reduction, ranged from 0% to 34%, depending on individual characteristics. Based on feedback from the AC, overall survival and local recurrence were chosen as the output for ROW, with these outcomes displayed numerically (percentages and natural frequencies) and graphically (pictographs). CONCLUSIONS: This tool "ROW" could facilitate shared decision making regarding receipt of radiotherapy for older women with early breast cancer. Additional studies to examine usability testing are underway.
OBJECTIVE: Among older adult women with early-stage breast cancer who undergo lumpectomy, the benefits of radiotherapy vary according to tumor characteristics and life expectancy. We aimed to develop a risk calculator to predict individualized probability of long-term survival and local recurrence, accounting for these factors. METHODS: We developed a simulation model to estimate an individual patient's risk of local recurrence and all-cause mortality according to age, comorbidities, functional status, tumor characteristics, and radiotherapy status. We integrated two existing prediction models, the Early Breast Cancer Trialist's Collaborative Group prediction model for breast cancer specific outcomes and ePrognosis for life expectancy. An online risk calculator "Radiotherapy for Older Women (ROW)" was developed through an iterative multi-stage process, that included individual consultation and group meetings with an advisory committee (AC) comprised of patients, advocates, clinicians, and researchers. RESULTS: We developed the tool over 40 months and had 15 group meetings. The risk calculator developed as a simulation model with 16 factors (5 tumor-related, 3 demographic, 4 comorbidities, and 4 functional statuses). Across 56,700 simulated scenarios, the benefit of RT in terms of absolute 10-year local recurrence reduction, ranged from 0% to 34%, depending on individual characteristics. Based on feedback from the AC, overall survival and local recurrence were chosen as the output for ROW, with these outcomes displayed numerically (percentages and natural frequencies) and graphically (pictographs). CONCLUSIONS: This tool "ROW" could facilitate shared decision making regarding receipt of radiotherapy for older women with early breast cancer. Additional studies to examine usability testing are underway.
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