Multivariate Risk of CVD in Diverse Populations

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT00006514
Recruitment Status : Completed
First Posted : November 21, 2000
Last Update Posted : February 18, 2016
Information provided by:
National Heart, Lung, and Blood Institute (NHLBI)

Brief Summary:
To statistically examine cardiovascular disease (CVD) risk in different populations based on data from studies representing national samples, cohort studies, and clinical trials.

Condition or disease
Cardiovascular Diseases Heart Diseases

Detailed Description:


Several algorithms have been developed to calculate multivariate risk of CVD based on characteristics associated with the disease. Framingham Heart Study data were used to develop the original algorithms, along with later models, using different mathematical forms, outcomes, and characteristics. Researchers then began to investigate the issue of generalizability, whether these risk estimates could be applied to new populations. For these algorithms to have general application, they must be able to rank risk correctly. And, when Framingham models were compared to new models developed for other studies, resulting orderings of risk were, in fact, similar.

The ability to order risk correctly, however, does not imply that estimated probabilities are right in terms of predicting disease for individuals. Methods are needed to assess individual risk to make treatment decisions, do cost-benefit analyses, and quantify benefits. These methods must be based on the patient's absolute risk, and existing equations may be incapable of establishing absolute risk across populations.

Earlier comparisons of multivariate risk among studies have made comparison populations as homogenous as possible before analysis. However, if multivariate risk estimates are to be truly useful, they must be applicable to the general population, and to be applicable, estimates must be based on comparisons of cohorts that include women and ethnic minorities. Also, in statistical terms, estimates must be robust enough to allow for minor shifts in methodologies for data collection and endpoint definition.


The heterogeneity of multivariate risk in different populations was examined based on data from studies representing national samples, cohort studies, and clinical trials. An analysis of these studies was conducted that included both sexes, various risk profiles, and representatives from several nationalities and ethnic groups. The pooled sample involved 20 studies, 233,833 participants, and over 47,000 deaths. Based on a common statistical approach, proportional hazards models were developed for each study to relate a set of essential characteristics to the prediction of CVD mortality. The characteristics included body mass index, age, blood pressure, serum cholesterol, smoking, and diabetes status. The models were then compared in terms of their ability to predict absolute risk of mortality across studies.

Secondary analyses were conducted to discover factors associated with inaccurate prediction and study characteristics associated with particular findings, such as interaction terms. An empirical examination was conducted of methods for adding newly discovered risk factors to existing prediction equations.

The study completion date listed in this record was obtained from the "End Date" entered in the Protocol Registration and Results System (PRS) record.

Study Type : Observational
Study Start Date : September 2000
Actual Study Completion Date : August 2004

Information from the National Library of Medicine

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Ages Eligible for Study:   up to 100 Years   (Child, Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
No eligibility criteria

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT00006514

Sponsors and Collaborators
National Heart, Lung, and Blood Institute (NHLBI)
OverallOfficial: Daniel McGee Florida State University