Copyright © 2004 by the Johns Hopkins Bloomberg School of Public Health
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Updated Risk Factor Values and the Ability of the Multivariable Risk Score to Predict Coronary Heart Disease
1 Department of Epidemiology and Biostatistics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
2 Division of Clinical Epidemiology, Montreal General Hospital, Montreal, Quebec, Canada.
3 Department of Medicine, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
4 Division of Clinical Epidemiology, Royal Victoria Hospital, Montreal, Quebec, Canada.
5 Division of Internal Medicine, Montreal General Hospital, Montreal, Quebec, Canada.
Most existing coronary risk assessment methods are based on baseline data only. The authors compared the predictive ability of coronary multivariable risk scores based on updated versus baseline risk factors and investigated the optimal frequency of updating. Data from 16 biennial examinations of 4,962 subjects from the original Framingham Heart Study (1948–1978) were used. The predictive ability of three multivariable risk scores was evaluated through 10-fold cross-validation. The baseline-only multivariable risk score was computed using baseline values of coronary risk factors applied to a Cox model estimated from baseline data. The two other approaches relied on updated risk factors and included them in the models estimated from, respectively, baseline and updated data. All analyses were stratified by sex and age. For 30, 14, and 10 years of follow-up, the predictive ability of the baseline-only multivariable risk score was substantially poorer than that of the models using updated risk factors. Between the two latter models, the one estimated from updated data ensured better prediction than the one estimated from baseline data for 30 years of follow-up among younger subjects only. The results suggest that coronary risk assessment can be improved by utilizing updated risk factors and that the optimal frequency of updating may vary across subpopulations.
cohort studies; coronary disease; logistic models; proportional hazards models; risk assessment; risk factors; validation studies [publication type]
Abbreviations: Abbreviation: MRS, multivariable risk score.
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