The risk functions incorporated in Riscard 2002: a software for the prediction of cardiovascular risk in the general population based on Italian data.

BACKGROUND: The purpose of this analysis was to produce risk functions for the prediction of cardiovascular diseases based on Italian epidemiological data and suitable for the use in a PC program dedicated to the estimate of risk. METHODS: Three studies were used for the purpose: the Italian Rural Areas of the Seven Countries Study, the Gubbio Population Study and the ECCIS study, for a total of 9771 men and women aged 35 to 74 years and followed for a period lasting 5 to 6 years. The risk factors used for the prediction of cardiovascular events were sex, age, body mass index (derived from height and weight), mean blood pressure (derived from systolic and diastolic blood pressures), non-HDL cholesterol (derived from total and HDL cholesterol), HDL cholesterol, diabetes (yes-no), heart rate, and daily cigarette consumption. The endpoints were the first major coronary event, the first major cerebrovascular event, and the first major cardiovascular event (either one between the previous two plus major peripheral artery diseases). The model employed for the analysis was the accelerated failure time model. RESULTS: Having excluded those already presenting with a cardiovascular disease and those with missing values, a total of 9089 subjects were included in the models. In a period lasting 5 or 6 years, a total of 211 coronary, 64 cerebrovascular and 269 cardiovascular events occurred and were considered for analysis. Coefficients from the coronary model suggested a significant association of all risk factors except body mass index and diabetes (marginal significance). Coefficients from the cerebrovascular model suggested a significant association limited to age and mean blood pressure. Coefficients from the cardiovascular model suggested a significant association of all risk factors except body mass index. The discrimination between cases and non-cases was satisfactory with proportions of 37.0, 52.3 and 37.8% of observed cases in decile 10 of the distribution of the estimated risk for the three endpoints respectively. CONCLUSIONS: The three models were used as a mathematical core for the construction of a PC software for the prediction of major cardiovascular events in Italy.

Riskard 2005. New tools for prediction of cardiovascular disease risk derived from Italian population studies.

BACKGROUND AND AIM: The need to update tools for the estimate of cardiovascular risk prompted the "Gruppo di Ricerca per la Stima del Rischio Cardiovascolare in Italia" to produce a new chart and new software called Riskard 2005. METHODS AND RESULTS: Data from 9 population studies in 8 Italian regions, for a grand total of 17,153 subjects (12,045 men and 5,108 women) aged 35-74 and for a total exposure of about 194,000 person/years were available. A chart for the estimate of cardiovascular risk (major coronary, cerebrovascular and peripheral artery disease events) in 10 years was produced for men and women aged 45-74 free from cardiovascular diseases. Risk factors employed in the estimate were sex, age (6 classes), systolic blood pressure (4 classes), serum cholesterol (5 classes), diabetes, and cigarette smoking (4 classes). Estimates were produced for absolute risk and for relative risk, the latter against levels expected in the general population that produced the risk functions. Software was produced for the separate estimate of major coronary, cerebrovascular and cardiovascular events (the latter made by coronary, cerebrovascular and peripheral artery disease of atherosclerotic origin) for follow-up at 5, 10 or 15 years, in men a women aged 35-74 years at entry and free from cardiovascular diseases. Risk factors employed here were sex, age, body mass index, mean physiological blood pressure, HDL cholesterol, non-HDL cholesterol, cigarette smoking, diabetes and heart rate. The output is based on several indicators: absolute risk, relative risk (as defined above), ideal risk (for a very favourable risk profile), biological age of risk, comparisons among the above indicators, the percent contribution of risk factors to the excess of estimated risk above the level of the ideal risk, and the description of trends in risk estimate in relation to repeated measurements. CONCLUSIONS: These tools represent progress compared to similar tools produced some years ago by the same Research Group.