Yonsei Med J.  2014 Jul;55(4):853-860. 10.3349/ymj.2014.55.4.853.

Development and Application of Chronic Disease Risk Prediction Models

• Oh SM
• Stefani KM
• Kim HC

• KMID: 2130807
• DOI: http://doi.org/10.3349/ymj.2014.55.4.853

Abstract

Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea.

Keyword

Non-communicable diseases; chronic diseases; risk prediction; disease prediction; health risk appraisal; Korea

MeSH Terms

Cardiovascular Diseases/epidemiology
Chronic Disease/*epidemiology
Communicable Diseases/*epidemiology
Humans
Korea/epidemiology
*Models, Theoretical
Risk Factors

Figure

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  Fig. 1 Simluated receiver operating characteristics curves for two prediction models. AUC, area under the receiver operating characteristics curve.

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  Fig. 2 Simulated calibration charts for two prediction models: one with good calibration performance (A) and the other with poor calibration performance (B).

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  Fig. 3 Simulated scatter plot comparing the performance of two prediction models. Vertical axis indicates the risk predicted by an old model. Horizontal axis indicates the risk predicted by a new model. The red dots are those with an event of interest, and the blue open circles are those without event.

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