Existing prediction algorithms for the identification of patients with heart failure (HF) at high risk of readmission or death after hospital discharge are only modestly effective. We sought to validate a recently developed predictive model of 30-day readmission or death in HF using an Australia-wide sample of patients. This study used data from 1,046 patients with HF at teaching hospitals in 5 Australian capital cities to validate a predictive model of 30-day readmission or death in HF. Besides standard clinical and administrative data, we collected data on individual sociodemographic and socioeconomic status, mental health (Patient Health Questionnaire [PHQ]-9 and Generalized Anxiety Disorder [GAD]-7 scale score), cognitive function (Montreal Cognitive Assessment [MoCA] score), and 2-dimensional echocardiograms. The original sample used to develop the predictive model and the validation sample had similar proportions of patients with an adverse event within 30 days (30% vs 29%, p = 0.35) and 90 days (52% vs 49%, p = 0.36). Applying the predicted risk score to the validation sample provided very good discriminatory power (C-statistic = 0.77) in the prediction of 30-day readmission or death. This discrimination was greater for predicting 30-day death (C-statistic = 0.85) than for predicting 30-day readmission (C-statistic = 0.73). There was a small difference in the performance of the predictive model among patients with either a left ventricular ejection fraction of <40% or a left ventricular ejection fraction of ≥40%, but an attenuation in discrimination when used to predict longer-term adverse outcomes. In conclusion, our findings confirm the generalizability of the predictive model that may be a powerful tool for targeting high-risk patients with HF for intensive management.