Ignite Creation Date:
2024-05-05 @ 11:20 AM
Last Modification Date:
2024-10-26 @ 9:05 AM
Study NCT ID:
NCT00006514
Status:
COMPLETED
Last Update Posted:
2016-02-18
First Post:
2000-11-20
Brief Title:
Multivariate Risk of CVD in Diverse Populations
Sponsor:
National Heart Lung and Blood Institute NHLBI
Organization:
National Heart Lung and Blood Institute NHLBI
Study Overview
Official Title:
None
Status:
COMPLETED
Status Verified Date:
2005-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 patients 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 233833 participants and over 47000 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
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 patients 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 233833 participants and over 47000 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 Oversight
Has Oversight DMC:
Is a FDA Regulated Drug?:
Is a FDA Regulated Device?:
Is an Unapproved Device?:
Is a PPSD?:
Is a US Export?:
Is an FDA AA801 Violation?:
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| R01HL067460 | NIH | None | httpsreporternihgovquickSearchR01HL067460 |