4.0 Article

Patterns and Yearly Time Trends in the Use of Radiation Therapy During the Last 30 Days of Life Among Patients With Metastatic Rectal Cancer in the United States From 2004 to 2012

Journal

AMERICAN JOURNAL OF HOSPICE & PALLIATIVE MEDICINE
Volume 35, Issue 2, Pages 336-342

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/1049909117706959

Keywords

colorectal neoplasms; terminal care; palliative care; health services research; rectal cancer; practice guidelines

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Purpose: Although radiation therapy (RT) can provide palliative benefits for patients with metastatic rectal cancer, its role at the end of life remains unclear. The objective of this study was to assess sociodemographic and clinical factors associated with the use of RT during the last 30 days of life and to evaluate yearly time trends in RT utilization among stage IV patients with rectal cancer. Methods: The 2004 to 2012 National Cancer DataBase was queried for patients with metastatic rectal cancer who had a documented death during follow-up. A Bayesian multilevel logistic regression model was used to characterize predictive factors and yearly time trends associated with RT use in the last 30 days of life. Results: Among 10 431 patients who met inclusion criteria, 345 (3%) received RT during the last 30 days of life. Factors independently associated with RT use included older age, female sex, African American race, nonprivate insurance, higher comorbidity burden, and worse grade. The odds of RT use at the end of life decreased by 28% between 2007 and 2009 (odds ratio [OR] = 0.72, 95% Credible Interval (CI) = 0.58-0.93), but then increased by 16% from 2010 to 2012 (OR = 1.16, 95% CI = 1.13-1.33), relative to 2004 to 2006. Conclusion: Radiation therapy use for patients with metastatic rectal cancer is beneficial, and efforts to optimize its appropriate use are important. Several factors associated with RT use during the last 30 days of life included disparities in sociodemographic and clinical subgroups. Research is needed to understand the underlying causes of these inequalities and the role of predictive models in clinical decision-making.

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