Related Experiment Videos
[Identification of subjects at high risk of coronary disease in a working population using a prediction model]
1Unité de recherches biomathématiques et biostatistiques, INSERM U263, Université Paris VII.
Insights
Identifying high-risk individuals for coronary heart disease is crucial for prevention. This study adapted a multifactorial model to assess coronary risk in French men, aiding early detection of those with borderline factors.
Area of Science:
- Cardiology
- Epidemiology
- Preventive Medicine
Background:
- Coronary heart disease (CHD) prevention relies on identifying high-risk individuals.
- France exhibits a relatively low prevalence of CHD compared to other populations.
- Existing risk prediction models require adaptation for specific demographic and epidemiological contexts.
Purpose of the Study:
- To implement and evaluate a multifactorial prediction model for identifying high-risk subjects for coronary morbidity in a French male population.
- To adapt an existing prediction model (from Framingham study) to account for the lower CHD prevalence in France.
- To provide a tool for detecting individuals with borderline risk factors who may otherwise be overlooked.
Main Methods:
- Utilized the PCV-METRA (Prévention Cardiovasculaire en Médecine du Travail) study data, comprising 4,131 active men aged 30-65 years.
- Adapted a prediction model from K.M. Anderson et al. (Framingham study) incorporating 7 risk factors: age, total cholesterol, HDL-cholesterol, systolic blood pressure, smoking, diabetes, and left ventricular hypertrophy.
- Developed a risk table for estimating individual 5-year coronary risk and identified high-risk subjects based on the 80th percentile of the risk distribution.
Main Results:
- The average 5-year coronary risk in the study population was estimated at 1.6%.
- High-risk subjects (above the 80th percentile) typically presented with elevated blood pressure and cholesterol.
- Notably, nearly 30% of high-risk individuals were not hypertensive or hypercholesterolemic, with 75% being smokers and often having low HDL-cholesterol levels.
Conclusions:
- The adapted multifactorial model effectively identifies high-risk subjects for coronary morbidity in the French male population.
- The model is particularly valuable for detecting individuals with multiple borderline risk factors.
- This tool aids in targeted preventive strategies for cardiovascular disease within occupational health settings.
Abstract:
Identification of subjects at high risk of coronary morbidity is of major interest in the prevention of cardiovascular disease. This report describes the use of a multifactorial prediction model for the identification of high risk subjects in a French male population. The PCV-METRA study (Prévention Cardiovasculaire en Médecine du Travail) monitors risk factors of cardiovascular morbidity in a population of men and women employed in big companies in the Paris region. A model adapted from a prediction model conceived by K.M. Anderson et al. in the Framingham study was used. The modified model enables an estimation of individual coronary risk based on 7 factors: age, total cholesterol, HDL-cholesterol, systolic blood pressure, smoking, diabetes and presence of left ventricular hypertrophy, taking into account the relatively low prevalence of coronary heart disease in France. The population comprised 4,131 active men aged 30 to 65 years. The average risk at 5 years was estimated to be 1.6%. Subjects at high risk (over the 80th percentile of the risk distribution curve) usually had high blood pressures and cholesterol levels. However, nearly 30% of these subjects were neither hypertensive nor hypercholesteraemic. It is important to note that 3/4 of these smoked. Moreover, they also had low HDL-cholesterol levels. A risk table, derived from the Framingham model, is presented. This table allows estimation of individual risk at 5 years in men aged 30 to 65 years. In each age group, the comparison of individual risk with the percentiles of risk distribution in the PCV-METRA population allows identification of high-risk subjects. This study proposes a tool for identifying subjects at high risk of coronary morbidity in a French male population. This multifactorial model is particularly useful for detecting subjects with several borderline factors none of which overstep the usually accepted limits.