Background and purpose: We wished to test the validity of a stroke probability point system from the Framingham Study for a sample of the population of Copenhagen, Denmark. In the Framingham cohort, the regression model of Cox established the effect on stroke of the following factors: age, systolic blood pressure, the use of antihypertensive therapy, diabetes mellitus, cigarette smoking, prior cardiovascular disease, atrial fibrillation, and left ventricular hypertrophy. Derived from this model, stroke probabilities were computed for each sex based on a point system. The authors claimed that a physician can use this system for individual stroke prediction.
Methods: The Copenhagen City Heart Study is a prospective survey of 19,698 women and men aged 20 years or older invited to two cardiovascular examinations at 5-year intervals. The baseline examination included 3015 men and 3501 women aged 55 to 84 years; 474 stroke events occurred during 10 years of follow-up. In both cohorts initial cases of stroke and transient ischemic attack recorded during 10 years of follow-up were used. We used the statistical model from the Framingham Study to establish a corresponding stroke probability point system using data from the Copenhagen City Heart Study population. We then compared the effects of the relevant risk factors, their combinations, and the corresponding stroke probabilities. We also assessed stroke events during 10 years of follow-up in several subgroups of the Copenhagen population with different combinations of risk factors.
Results: For the Copenhagen City Heart Study population some of the risk factors (diabetes mellitus, cigarette smoking, atrial fibrillation, and left ventricular hypertrophy) had regression coefficients different from those of the Framingham Study population. Consequently, the probability of stroke for persons presenting these risk factors and their combinations varied between the two studies. For some other risk factors (age, blood pressure, and cardiovascular disease), no major differences were found. The recorded frequency of stroke events in subgroups of the Copenhagen population was compatible with the estimated probability intervals of stroke from the Copenhagen City Heart Study and with those from the Framingham Study, but these intervals were very large.
Conclusions: The majority of risk factors for stroke identified by the Framingham Study also had a significant effect in the Copenhagen City Heart Study population. The differences found could be due partly to different definitions of these factors used by the two studies. Although estimated stroke probabilities based on point systems from the Copenhagen City Heart Study and the Framingham Study were similar, the points scored in the two systems did not always correspond to the same combination of risk factors. Such systems can be used for estimating stroke probability in a given population, provided that the statistical confidence limits are known and the definitions of risk factors are compatible. However, because of the large statistical uncertainty, a prognostic index should not be applied for individual prediction unless it is used as an indicator of high relative risk associated with the simultaneous presence of several risk factors.