Background: Many older adults with cancer have ≥2 impairments on geriatric assessment, which affects present and future frailty status, treatment tolerability, and outcomes. Our objective was to identify and describe distinct geriatric assessment impairment classes using latent class analysis (LCA) in older patients with gastrointestinal malignancies and assess 1-year mortality.
Methods: We used the Cancer & Aging Resilience Evaluation (CARE) Study, a registry of older adults (≥60 years) at University of Alabama at Birmingham. The analytic cohort included patients with gastrointestinal malignancies who completed a self-administered geriatric assessment (CARE tool) before chemotherapy and had ≥1 geriatric assessment impairment. Thirteen geriatric assessment impairments were used as indicators in LCA. Resultant classes were described, mortality was estimated, and risk contrasts (differences and hazard ratios) were calculated with 95% confidence intervals. For comparison, estimates were provided for frailty categories (robust, prefrail, and frail) determined from 44 items in the CARE tool. Stratified analyses included high-risk (pancreatic, hepatobiliary, and esophageal) versus low-risk gastrointestinal cancers, and stage (IV vs I-III).
Results: Six geriatric assessment impairment classes were identified: Mild impairment (LC1); Social support impairment (LC2); Weight loss alone (LC3); Impaired, low anxiety/depression (LC4); Impaired with anxiety/depression (LC5); and Global impairment (LC6). One-year mortality was 14%, 22%, 29%, 34%, 50%, and 50% for LC1-LC6, respectively. For frailty categories, estimates ranged from 18% (robust) to 40% (frail). In stratified analyses, LC4-LC6 consistently had higher mortality estimates compared to LC1.
Conclusions: The 6 geriatric assessment impairment classes showed a wider spread of mortality estimates compared to frailty categories and could be used to identify vulnerable patients and to plan interventions.
Keywords: Epidemiology; Frailty; Geriatric assessment; Geriatric oncology; Latent class analysis.
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