Assessment of absolute risk of death after myocardial infarction by use ofmultiple-risk-factor assessment equations - GISSI-Prevenzione mortality risk chart

Aims To present and discuss a comprehensive and ready to use prediction mod el of risk of death after myocardial infarction based on the very recently concluded follow-up of the large GISSI-Prevenzione cohort and on the integr ated evaluation of different categories of risk factors: those that are non -modifiable. and those related to lifestyles, co-morbidity, background. and other conventional clinical complications produced by the index myocardial infarction. Methods The 11 324 men and women recruited in the study within 3 months fro m their index myocardial infarction have been followed-up to 4 years. The f ollowing risk factors have been used in a Cox proportional hazards model: n on-modifiable risk factors: age and sex, complications after myocardial inf arction: indicators of left ventricular dysfunction (signs or symptoms of a cute left ventricular failure during hospitalization. ejection fraction, NY HA class and extent or ventricular asynergy at echocardiography), indicator s of electrical instability (number of premature ventricular beats per hour , sustained or repetitive arrhythmias during 24-h Holler monitoring), indic ators of residual ischaemia (spontaneous angina pectoris after myocardial i nfarction, Canadian Angina Classification class, and exercise testing resul ts); cardiovascular risk factors: smoking habits, history of diabetes melli tus and arterial hypertension, systolic and diastolic blood pressure. blood total and HDL cholesterol, triglycerides, fibrinogen, leukocytes count, in termittent claudication. and heart rate. Multiple regression modelling was assessed by receiver operating characteristic (ROC) analysis. Generalizabil ity of the models was assessed through cross validation and bootstrapping t echniques. Population and Results During the 4 years of follow-up, a total of 1071 pat ients died. Age and left ventricular dysfunction were the most relevant pre dictors of death. Because of pharmacological treatments, total blood choles terol, triglycerides, and blood pressure values were not significantly asso ciated with prognosis. Sex-specific prediction equations were formulated to predict risk of death according to age, simple indicators of left ventricu lar dysfunction., electrical instability, and residual ischaemia along with the following cardiovascular risk factors: smoking habits, history of diab etes mellitus and arterial hypertension, blood HDL cholesterol, fibrinogen, leukocyte count, intermittent claudication, and heart rate. The predictive models produced on the basis of information available in the routine condi tions of clinical care after myocardial infarction provide ready to use and highly discriminant criteria to guide secondary prevention strategies. Conclusions and Implications Besides documenting what should be the preferr ed and practicable focus of clinical attention for today's patients, the ex perience of GISSI-Prevenzione suggests that periodically and prospectively collected databases on 'naturalistic' cohorts could be an important option for updating and verifying the impact of guidelines, which should incorpora te the different components of the complex profile of cardiovascular risk. The GISSI Prevenzione risk function is a simple tool to predict risk of dea th and to improve clinical management of subjects with recent myocardial in farction. The use of predictive risk algorithms can favour the shift from m edical logic, based on the treatment of single risk factors, to one (C) 200 1 The European Society of Cardiology.