Korean Circ J.  2012 Apr;42(4):223-228. 10.4070/kcj.2012.42.4.223.

Clinical Utility of Novel Biomarkers in the Prediction of Coronary Heart Disease

Affiliations
  • 1Department of Preventive Medicine, Division of Cardiology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. hckim@yuhs.ac
  • 2Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Abstract

Coronary heart disease (CHD) is a significant cause of morbidity and mortality worldwide. Many risk prediction models have been developed in an effort to assist clinicians in risk assessment and the prevention of CHD. However, it is unclear whether the existing CHD prediction tools can improve clinical performance, and recently, there has been a lot of effort being made to improve the accuracy of the prediction models. A large number of novel biomarkers have been identified to be associated with cardiovascular risk, and studied with the goal of improving the accuracy and clinical utility of CHD risk prediction. Yet, controversy still remains with regard to the utility of novel biomarkers in CHD risk assessment, and in finding the best statistical methods to assess the incremental value of the biomarkers. This article discusses the statistical approaches that can be used to evaluate the predictive values of new biomarkers, and reviews the clinical utility of novel biomarkers in CHD prediction, specifically in the Korean population.

Keyword

Biomarkers; Prevention; Risk assessment; Coronary heart disease

MeSH Terms

Biomarkers
Coronary Disease
Risk Assessment

Figure

  • Fig. 1 CHD risk classification according to the ATP-III guidelins. Coronary heart disease risk equivalents include peripheral arterial disease, abdominal aortic aneurysm, and carotid artery disease (i.e., transient ischemic attacks, stroke of carotid origin, or >50% obstruction), or diabetes mellitus. Risk factors include cigarette smoking, hypertension, low HDL-C (<40 mg/dL), family history of premature CHD, and age (men ≥45 years; women ≥55 years). CHD: coronary heart disease, ATP-III: Adult Treatment Panel III, HDL-C: high density lipoprotein-cholesterol.

  • Fig. 2 Simulated area under the receiver operating characteristic curves (AUC or C-statistic). AUC: area under the curve.

  • Fig. 3 Simulated calibration charts for 10-year coronary heart disease prediction. A shows good calibration, while B shows poor calibration. CHD: coronary heart disease.

  • Fig. 4 Simulated reclassification tables comparing two prediction models. Net reclassification index (NRI)=Proportion of cases who were reclassified into higher category (11.7+1.7+1.0=14.4%)-Proportion of cases who were reclassified into lower category (0.7+0.3+0.7=1.7%)+Proportion of controls who were reclassified into lower category (0.8+0.3+0.5=1.6%)-Proportion of controls who were reclassified into higher category (3.3+0.8+0.5=4.6%)=9.8%.

  • Fig. 5 Simulated scatter plot showing the performance of two prediction models. Both X and Y axes are in logarithmic scales. Red dots indicate people who developed CHD, and blacks dots indicate people who did not. Blue lines indicate cut-off points determining risk categories. CHD: coronary heart disease.


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