Document Detail


On the prediction and prevention of myocardial infarctions: models based on retrospective and doubly censored prospective data.
MedLine Citation:
PMID:  15803447     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Early detection of coronary heart disease (CHD) in its pre-clinical stages may offer a way to reduce the impact of this endemic disease on society. Coronary calcification accompanies the development of an atherosclerotic plaque, the pathological substrate of CHD, and its identification is currently possible by means of electron beam tomography (EBT). This is a non-invasive imaging test that quantifies the extent of coronary artery plaque calcification by means of a calcium score. In this study, we show that an age-sex based calcium score percentile (CS%) provides an invaluable predictor for myocardial infarction (MI), and examine how CS% is related to traditional risk factors. We study two separate groups of patients: 172 patients who underwent EBT screening after surviving an MI (retrospective group); and 676 asymptomatic subjects who were screened and followed for several years for the occurrence of an MI (prospective group). We use CS% with traditional risk factors in logistic regression models to: (1) compare patients in the retrospective and prospective groups, and (2) develop a mortality model for MIs that occurred in the prospective group. These logistic regressions are used to develop a joint model for the relative log-odds of an MI, which is non-linear in the covariates of the mortality model. We also use baseline covariates in the prospective group to fit an event-time regression model and estimate probabilities for 2 and 3 year exposure periods. The event-time regression provides independent estimates of the relative odds that are associated with risk factors. CS%, smoking, and their interaction were preeminent as predictors in the joint model for the relative odds of an MI and in the event-time regression, although the effects of smoking and CS% were generally subadditive. These models provide important information on the risks associated with elevated CS% levels.
Authors:
Bruce Cooil; Paolo Raggi
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Statistics in medicine     Volume:  24     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2005 Jun 
Date Detail:
Created Date:  2005-05-31     Completed Date:  2005-09-09     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  1897-918     Citation Subset:  IM    
Copyright Information:
Copyright 2005 John Wiley & Sons, Ltd.
Affiliation:
Owen Graduate School of Management, Vanderbilt University, Nashville, TN 37203, USA. bruce.cooil@owen.vanderbilt.edu
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MeSH Terms
Descriptor/Qualifier:
Coronary Disease / diagnosis
Humans
Incidence
Logistic Models
Male
Models, Statistical*
Myocardial Infarction / epidemiology,  etiology,  prevention & control*
Prospective Studies
Retrospective Studies
Risk Factors
United States / epidemiology

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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