Multivariate Risk of CVD in Diverse Populations

This study has been completed.
Sponsor:
Information provided by:
National Heart, Lung, and Blood Institute (NHLBI)
ClinicalTrials.gov Identifier:
NCT00006514
First received: November 20, 2000
Last updated: July 20, 2005
Last verified: July 2005
  Purpose

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


Condition
Cardiovascular Diseases
Heart Diseases

Study Type: Observational
Study Design: Observational Model: Natural History

Further study details as provided by National Heart, Lung, and Blood Institute (NHLBI):

Study Start Date: September 2000
Estimated Study Completion Date: August 2004
Detailed Description:

BACKGROUND:

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.

DESIGN NARRATIVE:

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.

  Eligibility

Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   No
Criteria

No eligibility criteria

  Contacts and Locations
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the Contacts provided below. For general information, see Learn About Clinical Studies.

Please refer to this study by its ClinicalTrials.gov identifier: NCT00006514

Sponsors and Collaborators
Investigators
Investigator: Daniel McGee Florida State University