BACKGROUND: Identification of patients who are at high risk of adverse cardiovascular events after an acute coronary syndrome (ACS) remains a major challenge in clinical cardiology. We hypothesized that quantifying variability in electrocardiogram (ECG) morphology may improve risk stratification post-ACS. METHODS AND RESULTS: We developed a new metric to quantify beat-to-beat morphologic changes in the ECG: morphologic variability in beat space (MVB), and compared our metric to published ECG metrics (heart rate variability [HRV], deceleration capacity [DC], T-wave alternans, heart rate turbulence, and severe autonomic failure). We tested the ability of these metrics to identify patients at high risk of cardiovascular death (CVD) using 1082 patients (1-year CVD rate, 4.5%) from the MERLIN-TIMI 36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non-ST-Elevation Acute Coronary Syndrome-Thrombolysis in Myocardial Infarction 36) clinical trial. DC, HRV/low frequency-high frequency, and MVB were all associated with CVD (hazard ratios [HRs] from 2.1 to 2.3 [P<0.05 for all] after adjusting for the TIMI risk score [TRS], left ventricular ejection fraction [LVEF], and B-type natriuretic peptide [BNP]). In a cohort with low-to-moderate TRS (N=864; 1-year CVD rate, 2.7%), only MVB was significantly associated with CVD (HR, 3.0; P=0.01, after adjusting for LVEF and BNP). CONCLUSIONS: ECG morphological variability in beat space contains prognostic information complementary to the clinical variables, LVEF and BNP, in patients with low-to-moderate TRS. ECG metrics could help to risk stratify patients who might not otherwise be considered at high risk of CVD post-ACS.