Using CT-based body composition metrics and frailty index in predicting survival among older adults with cancer.

Abstract

12057 Background: Older adults with cancer are at an increased risk of treatment related toxicities and excess mortality during cancer treatment. While altered body composition and frailty are associated with worse survival among older adults with cancer, no prior study has examined their combined influence on survival prediction. Methods: Prospective study of older adults (≥60 years) undergoing geriatric assessment (GA) at initial visit with a medical oncologist at UAB from 9/2017-07/2021 with available abdominal computed tomography (CT) within 60 days of GA. Using multi-slice CT images from T12 to L5 level, volumetric skeletal muscle (SMV), visceral (VATV) and subcutaneous (SATV) adipose tissues, and skeletal muscle density (SMD), were derived. Sex-specific z-scores for each measure were determined. A 44-item frailty index was obtained, using the deficit accumulation model. Overall survival (OS) was defined as time from GA to death or last follow-up (11/8/2021). Kaplan-Meier estimates of survival rates were compared using log-rank statistics. Multivariable cox regression models were used to predict OS in a random sub-sample (1:1 split of training:validation set), sequentially adding frailty and each body composition measure and assessing improvement with likelihood ratio tests and Harrel’s C statistic. Results: 815 patients were included (median age 68 years, 61% men, and 75% non-Hispanic Whites. 73% had gastrointestinal malignancies (stage III, 25%, stage, IV 48%). 32% were frail, 31% pre-frail. There was a weak negative correlation between height-adjusted SMV and frailty (r = -0.16), particularly among men (r -0.24). Over a median follow-up of 25.7 months (range 0.3-49.6 months), 268 patients (33%) died. The 2-year survival rate was 75.9%, 68.5% and 52.2% among robust, pre-frail and frail (log-rank p <.001), respectively. In multivariable models adjusted for age, sex, race, cancer type and cancer stage, being frail (vs robust) (Hazards Ratio, HR = 2.32; 95%CI: 1.69-3.2; p <.001) and higher skeletal muscle volume (HR = 0.85; 95%CI: 0.72-0.99; p= 0.04, per SD increment) were independently associated with OS. Adding body composition and frailty to clinical variables led to significant improvement in prediction (Harrel’s C increased from 0.69 to 0.74). In the validation set, discrimination was similar (Harrel’s C = 0.72) and plots suggested good model calibration. Conclusions: CT-based body composition metrics and frailty are independent predictors of OS among older adults with cancer and improve survival prediction compared to routine clinical risk factors.