OBJECTIVES To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN Prospective cohort study. SETTING A total of 306 ICUs from 24 European countries. PARTICIPANTS Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81‐87 y]; 51.8% male). MEASUREMENTS Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30‐day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS The 30‐day‐mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30‐day mortality in 91.1% of all patients who die. CONCLUSION A predictive model of cumulative events predicts 30‐day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision‐making capacity.