Despite remarkable advances in the treatment of acute myocardial infarction, substantial early patient mortality remains. Appropriate choices among alternative therapies and the use of clinical resources depend on an estimate of the patient's risk. Individual patients reflect a combination of clinical features that influence prognosis, and these factors must be appropriately weighted to produce an accurate assessment of risk. Prior studies to define prognosis either were performed before widespread use of thrombolysis or were limited in sample size or spectrum of data. Using the large population of the GUSTO-I trial, we performed a comprehensive analysis of relations between baseline clinical data and 30-day mortality and developed a multivariable statistical model for risk assessment in candidates for thrombolytic therapy. For the 41,021 patients enrolled in GUSTO-I, a randomized trial of four thrombolytic strategies, relations between clinical descriptors routinely collected at initial presentation, and death within 30 days (which occurred in 7% of the population) were examined with both univariable and multivariable analyses. Variables studied included demographics, history and risk factors, presenting characteristics, and treatment assignment. Risk modeling was performed with logistic multiple regression and validated with bootstrapping techniques. Multivariable analysis identified age as the most significant factor influencing 30-day mortality, with rates of 1.1% in the youngest decile ( 75 (adjusted chi 2 = 717, P < .0001). Other factors most significantly associated with increased mortality were lower systolic blood pressure (chi 2 = 550, P < .0001), higher Killip class (chi 2 = 350, P < .0001), elevated heart rate (chi 2 = 275, P < .0001), and anterior infarction (chi 2 = 143, P < .0001). Together, these five characteristics contained 90% of the prognostic information in the baseline clinical data. Other significant though less important factors included previous myocardial infarction, height, time to treatment, diabetes, weight, smoking status, type of thrombolytic, previous bypass surgery, hypertension, and prior cerebrovascular disease. Combining prognostic variables through logistic regression, we produced a validated model that stratified patient risk and accurately estimated the likelihood of death. The clinical determinants of mortality in patients treated with thrombolytic therapy within 6 hours of symptom onset are multifactorial and the relations complex. Although a few variables contain most of the prognostic information, many others contribute additional independent prognostic information. Through consideration of multiple characteristics, including age, medical history, physiological significance of the infarction, and medical treatment, the prognosis of an individual patient can be accurately estimated.