Frailty is an important health outcomes indicator and valuable for guiding healthcare decisions in older adults, but is rarely collected in a quantitative, systematic fashion in routine healthcare. Using a cohort of 12,000 Veterans with heart failure, we investigated the feasibility of topic modeling to identify frailty topics in clinical notes. Topics were generated through unsupervised learning and then manually reviewed by an expert. A total of 53 frailty topics were identified from 100,000 notes. We further examined associations of frailty with age-, sex-, and Charlson Comorbidity Index-adjusted 1-year hospitalizations and mortality (composite outcome) using logistic regression. Frailty (≤ 4 topics versus <4) was associated with twice the risk of the composite outcome, Odds Ratio: 2.2, 95% Confidence Interval: (2.0-2.4). This study demonstrates the feasibility of identifying frailty indicators from clinical notes and linking these to clinically relevant outcomes. Future work includes integrating frailty indicators into validated predictive tools.