PCN23 The Development and Validation of a Simulation Model-based Clinical Decision Tool to Support Oncology Practice

Abstract

There are several challenges in treating women diagnosed with node-negative, hormone receptor positive, HER2 negative (“early-stage”) breast cancer. The goal of this study is to demonstrate the application of a novel simulation model-based calculation engine for a clinical decision tool that reflects current clinical needs and facilitates modern personalized treatment decisions for women diagnosed with early-stage breast cancer. We developed a novel breast cancer simulation model within the Cancer Intervention and Surveillance Modeling Network (CISNET) to derive estimates for the risk of distant recurrence, breast cancer specific mortality, other cause mortality and life-years gained with endocrine vs. chemo-endocrine therapy for individual women based on their age, tumor size, grade, 21-gene recurrence score and comorbidity level. The model uses an empiric Bayesian analytical approach to combine information from several data sources to provide individual estimates which capture uncertainty in all predictors’ effects on outcomes and sampling variation. External validation of the model was performed by comparing model-based breast cancer mortality rates and observed rates in the Surveillance Epidemiology and End Results (SEER) registry. The model estimates varied based on a woman’s age, tumor size, grade, comorbidity-level and 21-gene recurrence assay score. For example, in a 65–69-year-old woman diagnosed with a small (≤2cm), intermediate grade tumor and mild comorbidities, the absolute benefit of chemotherapy on the 10-year risk of distant recurrence ranged from 2%-5.5% , and the life-years gained with chemotherapy ranged from 0.34 to 0.93 life-years as the recurrence score ranged from 0 to 40+. In external validation, the model estimated breast cancer-specific mortality rates by recurrence score category was similar to the observed distributions in SEER data. The model can be used to synthesize existing evidence and develop a calculation-engine for a web-based clinical decision tool that could help facilitate shared decision-making between clinicians and patients in early-stage breast cancer settings.