OBJECTIVES: To develop a prognostic model for hospital admissions over a 1-year period among community-dwelling older adults with self-reported hearing and/or vision impairments based on readily obtainable clinical predictors. DESIGN: Retrospective cohort study. SETTING: Medicare Current Beneficiary Survey from 1999 to 2006. PARTICIPANTS: Community-dwelling Medicare beneficiaries, aged 65 years and older, with self-reported hearing and/or vision impairment (N = 15,999). MEASUREMENTS: The primary outcome was any hospital admission over a predefined 1-year study period. Candidate predictors included demographic factors, prior healthcare utilization, comorbidities, functional impairment, and patient-level factors. We analyzed the association of all candidate predictors with any hospital admission over the 1-year study period using multivariable logistic regression. The final model was created using a penalized regression method known as the least absolute shrinkage and selection operator. Model performance was assessed by discrimination (concordance statistic (c-statistic)) and calibration (evaluated graphically). Internal validation was performed via bootstrapping, and results were adjusted for overoptimism. RESULTS: Of the 15,999 participants, the mean age was 78 years and 55% were female. A total of 2,567 participants (16.0%) had at least one hospital admission in the 1-year study period. The final model included seven variables independently associated with hospitalization: number of inpatient admissions in the previous year, number of emergency department visits in the previous year, activities of daily living difficulty score, poor self-rated health, and self-reported history of myocardial infarction, stroke, and nonskin cancer. The c-statistic of the final model was 0.717. The optimism-corrected c-statistic after bootstrap internal validation was 0.716. A calibration plot suggested that the model tended to overestimate risk among patients at the highest risk for hospitalization. CONCLUSION: This prognostic model can help identify which community-dwelling older adults with sensory impairments are at highest risk for hospitalization and may inform allocation of healthcare resources.