Key result
A 20-year risk prediction model for first major cardiovascular event demonstrated good discrimination (AUC 0.737 in men, 0.801 in women) and reduced missed events compared to risk factor counting.
Why the study?
Does a 20-year risk prediction model improve risk stratification for major cardiovascular events compared to traditional risk factor counting in a low-incidence European population?
Cohort (n=5,247)
Yes
Does a 20-year risk prediction model improve risk stratification for major cardiovascular events compared to traditional risk factor counting in a low-incidence European population?
A 20-year cardiovascular risk prediction model demonstrates good discrimination and clinical utility for primary prevention in a low-incidence Southern European population.
May support 20-year risk stratification in low-incidence populations; leaves open whether it improves outcomes over risk factor counting.
OBJECTIVE: To develop a long-term prediction model of first major cardiovascular event and to assess its clinical utility in a low-incidence European population. SETTING: Four independent population-based cohorts enrolled between 1986 and 1993 in Northern Italy. PARTICIPANTS AND METHODS: N=5247 35-year-old to 69-year-old men and women free of cardiovascular disease at baseline. Absolute 20-year risk of first fatal or non-fatal coronary or ischaemic stroke event (monitoring trends and determinants in cardiovascular disease (MONICA) validated) was estimated from gender-specific Cox models. MAIN OUTCOME MEASURES: Model discrimination (area under the receiver operating characteristic (ROC)-curve, AUC). 'High-risk' subjects were identified based on several threshold values for the 20-year predicted risk. Clinical utility was defined in terms of fraction of missed events (events among those considered at low-risk) and unnecessary treatment (false:true positive ratio). A net benefit curve was also provided. RESULTS: Kaplan-Meier 20-year risk was 16.1% in men (315 events) and 6.1% in women (123 events). Model discrimination (AUC=0.737 in men, 0.801 in women) did not change significantly as compared to 10-year prediction time interval. In men, with respect to risk stratification based on the number of risk factors, a 20% predicted risk cut-off would miss less events (36% vs 50%) and reduce unnecessary treatment (false:true positive ratio 2.2 vs 3.0); the net benefit was higher over the whole range of threshold values. Similar considerations hold for women. CONCLUSIONS: Long-term prediction has good discrimination ability and is clinically useful for risk stratification in primary prevention. A clinical utility analysis is recommended to identify the optimal stratification according to different public health goals.
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Veronesi et al. (2013) conducted a cohort in Cardiovascular disease prevention (n=5,247). 20-year risk prediction model vs. Risk stratification based on number of risk factors was evaluated on Model discrimination (AUC) for first fatal or non-fatal coronary or ischaemic stroke event. A 20-year risk prediction model for first major cardiovascular event demonstrated good discrimination (AUC 0.737 in men, 0.801 in women) and reduced missed events compared to risk factor counting.
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