A strategy was developed to identify subgroups at high risk for recurrent coronary events in non-diabetic postinfarction patients as a function of metabolic, inflammatory, and thrombogenic blood markers. A graphical screening technique for presumptively identifying high-risk subgroups from outcome prevalence maps was devised that was equally sensitive for all values of risk factors in contrast to traditional approaches where risk is presumed for the highest or the lowest values. Traditional statistical analysis confirms high risk in identified subgroups. Serum glucose and triglyceride served as bivariate search domain. Results demonstrated three high-risk subgroups. One was characterized as pre-diabetic; another as metabolic syndrome-enriched; and the third, with unexpectedly high risk, as normoglycemic and modestly hypertriglyceridemic. Within-subgroup risk as determined by Cox proportional hazards model gave for odds ratios and 95 percentile confidence intervals: glucose, 2.49 (1.17-5.33) in pre-diabetic; PAI-1, 3.95 (1.81-8.61) in metabolic syndrome-enriched; and BMI, 2.79 (1.17-6.63) and fibrinogen, 2.79 (1.29-6.04) in normoglycemic, modestly hypertriglyceridemic patients. We conclude that the graphical approach holds promise in screening for high-risk patient subgroups. Finding different within-subgroup predictors of risk underscores the notion of context-dependent risk, an observation that may be relevant for determining optimal use of emerging risk factors.