Geriatric Assessment Predictors of 1-Year Mortality in Older Adults With GI Malignancies: A Survival Tree Analysis

Grant R. Williams, Chen Dai, Smith Giri, Mustafa Al-Obaidi, Christian Harmon, Kelly M. Kenzik, Andrew McDonald, Olumide Gbolahan, Darryl Outlaw, Moh'd Khushman, Joshua Richman, Smita Bhatia

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

PURPOSE: Identifying older patients with GI malignancies who are at increased risk of mortality remains challenging. The goal of our study was to examine geriatric assessment (GA) predictors of 1-year mortality and explore the use of a survival tree analysis in a prospective cohort of older adults (≥ 60 years) with newly diagnosed GI malignancies. METHODS: Survival tree analysis was performed to understand variable interactions and identify predictors of overall survival, computed from time of GA to death or last follow-up. Cox regression was used to estimate associations of 1-year mortality, first using a base model (age, race, cancer stage, cancer risk group, and planned chemotherapy), then using all significant predictors from the univariable analyses, and finally only those identified in survival tree analysis. RESULTS: A total of 478 participants met eligibility, with a mean age of 70 years. The survival tree analysis identified nutrition, cancer stage, physical and emotional health, age, and functional status as predictors of mortality. Older patients without malnutrition or depression had the best 1-year survival, whereas those with malnutrition, stage IV disease, and functional limitations had the worst 1-year survival. Our base model demonstrated good discrimination (area under curve [AUC] 0.76) but was improved with the addition of GA variables (AUC 0.82) or from survival tree analysis (AUC 0.82). CONCLUSION: Measures of function, nutrition, and mental health are important predictors of mortality in older adults with GI cancers. Using GA as part of clinical management can aid in the prediction of survival and help inform treatment decision making.

Original languageEnglish
Pages (from-to)e2200065
JournalJCO Clinical Cancer Informatics
Volume6
DOIs
StatePublished - Sep 1 2022

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