BACKGROUND: Our previous work demonstrated dense physiological data capture in the intensive care unit (ICU), defined a new vital sign Cardiac Volatility Related Dysfunction (CVRD) reflecting reduced heart rate variability, and demonstrated CVRD predicts death during the hospital stay adjusting for age and injury severity score (ISS). We hypothesized a more precise definition of variability in integer heart rate improves predictive power earlier in ICU stay, without adjusting for covariates. METHODS: Approximately 120 million integer heart rate (HR) data points were prospectively collected and archived from 1316 trauma ICU patients, linked to outcome data, and de-identified. HR standard deviation was computed in each 5-min interval (HR(SD5)). HR(SD5) logistic regression identified ranges predictive of death. The study group was randomly divided. Integer heart rate variability (% time HR(SD5) in predictive distribution ranges) models were developed on the first set (N = 658) at 1, 2, 4, 6, 8, 12, and 24 h after ICU admission, and validated on the second set (N = 658). RESULTS: HR(SD5) is bimodal, predicts death at low (0.1-0.9 bpm) and survival at high (1.8-2.6 bpm) ranges. HRV predicts death as early as 12 h (ROC = 0.67). HRV in a moving 1-h window is a simple graphic display technique. CONCLUSIONS: Dense physiological data capture allows calculation of HRV, which: 1) Independently predicts hospital death in trauma patients at 12 h; 2) Shows early differences by mortality in groups of patients when viewed in a moving window; and 3) May have implications for military and civilian triage.