Independent of other established risk factors, depressed heart rate variability (HRV) has been shown to be a powerful predictor of cardiac events after MI. Unfortunately, the need of 24-hour ECG recording and subsequent laborious editing of Holter data limits the clinical use of long-term HRV. In order to perform post-MI risk stratification more efficiently, we evaluated the value of short-term HRV estimates for preselection of patients who might benefit from long-term HRV assessment. Two measures were assessed from 24-hour ambulatory ECGs recorded in 729 survivors of acute MI prior to hospital discharge. In addition to a complete 24-hour HRV index, a standard deviation of normal-to-normal RR intervals (SDNN) was obtained from the first stationary and ectopic free 5-minute segment of the Holter recording. Predictive power (relation between positive predictive accuracy and sensitivity) of a complete 24-hour HRV index in identifying patients who suffered from cardiac mortality or arrhythmic events during a 2-year follow-up was compared to the predictive power of assessing the 24-hour HRV index limited to 50%, 40%, or 20% of patients with the lowest values of 5-minute SDNN. The HRV index was significantly lower in patients who died (19 +/- 11 units) or had an arrhythmic event (AE) (18 +/- 11 units) compared to those who survived without an event (28 +/- 10 resp. 27 +/- 11 units; P < 0.001). Similarly, 5-minute SDNN was significantly lower in patients who died (25 +/- 12 ms) or suffered an AE (26 +/- 13 ms) compared to survivors (40 +/- 19 ms resp. 39 +/- 19 ms; P < 0.001). When limited to patients with depressed 5-minute SDNN, assessment of the HRV index performed better than 5-minute SDNN alone in positive prediction of cardiac events. Preselected assessment of the lowest HRV index in 50% to 20% of the total population yielded a 2-year cardiac event prediction rate as high as analysis of the HRV index in all patients. Long-term HRV assessment for risk stratification after MI in patients preselected by depressed short-term SDNN is safe and efficient, and allows a practical identification of patients with the highest likelihood of cardiac events during long-term follow-up.