Introduction: Hip replacement (HR) operations are increasing. Short term mortality is an indicator of quality; few studies include risk adjustment models to predict HR outcomes. We evaluated in-hospital and 30-day mortality in hospitalized patients for HR and compared the performance of two risk adjustment algorithms. Materials and methods: A retrospective cohort study on hospital discharge records of patients undergoing HR from 2000 to 2005 in Tuscany Region, Italy, applied All-Patient Refined Diagnosis Related Groups (APR-DRG) and Elixhauser Index (EI) risk adjustment models to predict outcomes. Logistic regression was used to analyse the performance of the two models; C statistic (C) was used to define their discriminating ability. Results: 25,850 hospital discharge records were studied. In-hospital and 30-day crude mortality were 1.3% and 3%, respectively. Female gender was a significant (p<0.001) protective factor under both models and had the following Odds Ratios (OR): 0.64 for in-hospital and 0.51 for 30-day mortality using APR-DRG and 0.55 and 0.48, respectively, with EI. Among EI comorbidities, heart failure and liver disease were associated with in-hospital (OR 9.29 and 5.60; p<0.001) and 30-day (OR 6.36 and 3.26; p<0.001) mortality. Increasing age and APR-DRG risk class were predictive of all the outcomes. Discriminating ability for in-hospital and 30-day mortality was reasonable with EI (C 0.79 and 0.68) and good with APR-DRG (C 0.86 and 0.82). Conclusions: Our study found that gender, age, EI comorbidities and APR-DRG risk of death are predictive factors of in-hospital and 30-day mortality outcomes in patients undergoing HR. At least one risk adjustment algorithm should always be implemented in patient management.