Complexity is a measure of variation and randomness potentially indicating improvement or deterioration in critically ill patients. Previously, we have shown integer heart rate (HR) multiscale entropy (MSE), an indicator of complexity, predicts death based on long duration (12 h) and dense (>or=0.4 Hz) windows of HR data. However, such restrictions reduce the use of MSE in the clinical setting. We hypothesized MSE predicts death using HR data of shorter duration and lower density. During the initial 24 h of intensive care unit stay, 3,154 patients had at least 3 h of continuous integer HR sampled. The first continuous window of 3, 6, 9, and 12 h was selected for each patient regardless of density, and an open-source MSE algorithm was applied (M. Costa, www.physionet.org; m = 2; r = 0.15). Risk of death based on MSE, alone and with covariates (age, sex, injury severity score), was assessed using randomly selected logistic regression in half of the cases. Area under the receiver operator curve (AUC) was computed in the other half in subgroups having various durations and densities of HR data. At days 2.3 (median) and 4.9 (mean), 441 patients (14%) died. Multiscale entropy stratified patients by mortality and was an independent predictor of death using 3 h or more of data. Multiscale entropy alone (AUC = 0.66 - 0.71) predicted death comparably to covariates alone (AUC = 0.72). We conclude: (1) Heart rate MSE within hours of admission predicts death occurring days later. (2) Multiscale entropy is robust to variation in bedside data duration and density occurring in a working intensive care unit. (3) Complexity may be a new clinical biomarker of outcome.