Risk Factors And Prediction Of Myocardial Infarction

A myocardial infarction, risk technology, applied in measurement devices, biological tests, instruments, etc., can solve problems such as imperfection

Inactive Publication Date: 2012-12-05
BG MEDICINE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Although reliable, this two-step prevention approach is far from perfect in its current form, as established risk factors, even when combined, have preferential predictive power (Wald NJ, Morris JK, Rish S. The efficacy of combining several risk factors as a screening test. J. Med. Screen 12:197-201 (2005)), and progression of subclinical disease to clinical MI events occurs regardless of initiation of recommended "optimal" therapy (Koenig W. Treating residual cardiovascular risk: will lipoprotein-associated phospholipase A2inhibition live up to its promise? J. Am. Coll. Cardiol .51:1642-4(2008)

Method used

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  • Risk Factors And Prediction Of Myocardial Infarction
  • Risk Factors And Prediction Of Myocardial Infarction
  • Risk Factors And Prediction Of Myocardial Infarction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] Example 1: Identification of putative biomarkers

[0059] method

[0060] research group

[0061] The aim of this study was to improve detection of individuals at highest risk by focusing on individuals who developed MI within 4 years of risk assessment. Risk factors and predictors for this type of recent MI have not been reported previously, mainly because most studies were too small to accrue a sufficient number of recent MI events. Clearly, the risk factors for short-term events like MI, dominated by thrombosis superimposed on inflamed and ruptured atherosclerotic plaques, are different from the risks of longer-term events dominated by the slow progression of atherosclerosis factor. To this end, a large community-based prospective nested case-control study was used, a combination of the Copenhagen General Population Study and the Copenhagen City Heart Study utilizing 45,735 men and women.

[0062] Participants were from the Copenhagen City Heart Study 2001-2003...

Embodiment 2

[0130] The study population, established risk factors, and inferred biomarkers are as described in Example 1. Variables representing established risk factors were treated as in Example 1, and variables representing inferred biomarkers were treated as continuous variables.

[0131] Associations between baseline variable levels and MI incidence were examined with logistic regression models. The model included the 13 biomarkers identified in Example 1 and listed in Table 6.

[0132] An exemplary equation for determining a risk score indicative of a person's risk of developing MI is calculated by multiplying the measured level of each biomarker by a coefficient reflecting its relative contribution to risk, and linearizing each product and , to produce a risk score. The measured levels of each biomarker were natural logarithmically transformed (ie, base e, where e is about 2.71828183) before multiplication by the coefficient. As will be appreciated by those skilled in the art, a...

Embodiment 3

[0144] The study population, established risk factors, and inferred biomarkers are as described in Example 1. Variables representing established risk factors were treated as described in Example 1, and variables representing inferred biomarkers were treated as continuous variables.

[0145] Associations between baseline variable levels and MI incidence were examined with logistic regression models. This example initially considered the 13 biomarkers identified in Example 1 (same starting set as used in Example 2 above).

[0146] To select a subset of informative biomarkers from this initial 13 biomarkers, a backward stepwise variable selection technique was used. At each stepwise selection step, a biomarker was retained if the p-value associated with its coefficient was p<0.05. In this way, 7 biomarkers were retained for the final model. Table 8 lists their coefficients:

[0147] Table 8: Biomarker Coefficients of Example 3

[0148] Biomarkers

Coefficient

...

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PUM

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Abstract

Biomarkers and methods are disclosed for diagnosing the risk of a myocardial infarction in an individual by measuring the levels of a set of biomarkers in a sample from an individual. A risk score is calculated for the individual by weighting the measured levels of the biomarkers. The risk score then is used to identify whether the individual is likely to experience a myocardial infarction. In addition, kits are disclosed that include a set of reagents for specifically measuring biomarker levels in a sample from an individual.

Description

[0001] References to related applications [0002] This application claims priority to and benefit of US Provisional Application No. 61 / 261,155, filed November 13, 2009, the entire contents of which are incorporated herein by reference. Background technique [0003] Myocardial infarction (MI), commonly referred to as heart attack, is associated with modifiable risk factors but nevertheless remains a leading cause of death and severe disability worldwide (Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with MI in 52 countries(the INTERHEART study): case-controls study. Lancet .364:937-52 (2004)). For prevention, modern guidelines in both the US and Europe recommend an integrated two-step approach in which, if needed, risk assessment (prediction) is followed by individualized risk reduction (treatment); the higher the risk, the prescribed The more aggressive the preventive care in A dults (Third Report of the National Cholesterol Ed...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/50
CPCG01N2800/324G01N33/6893G01N2800/60G01N33/68
Inventor 阿拉姆·S·阿多立安郭昱李晓红皮尔特·曼特恩戴姆
Owner BG MEDICINE
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