A Prediction Model for the Risk of Incident Chronic Kidney Disease

Chien, Kuo-Liong; Lin, Hung-Ju; Lee, Bai-Chin; Hsu, Hsiu-Ching; Lee, Yuan-Teh; Chen, Ming-Fong

doi:10.1016/j.amjmed.2010.05.010

« Previous Next »The American Journal of Medicine
Volume 123, Issue 9 , Pages 836-846.e2, September 2010

A Prediction Model for the Risk of Incident Chronic Kidney Disease

Kuo-Liong Chien, MD, PhD
- Institute of Epidemiology & Preventive Medicine, National Taiwan University, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Hung-Ju Lin, MD
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Bai-Chin Lee, MD, PhD
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Hsiu-Ching Hsu, PhD
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
Yuan-Teh Lee, MD, PhD
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Chinese Medical University Hospital, Taichung, Taiwan
Ming-Fong Chen, MD, PhD
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Requests for reprints should be addressed to Ming-Fong Chen, MD, PhD, Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100, Taiwan

Abstract

Background

Chronic kidney disease is a health burden for the general population. We designed a cohort study to construct prediction models for chronic kidney disease in the Chinese population.

Methods

A total of 5168 participants were followed up during a median of 2.2 (interquartile range, 1.5-2.9) years, and 190 individuals (3.7%) developed chronic kidney disease, defined by a glomerular filtration rate of less than 60 mL/min/1.73 m².

Results

We developed a point system to estimate chronic kidney disease risk at 4 years using the following variables: age (8 points), body mass index (2 points), diastolic blood pressure (2 points), and history of type 2 diabetes (1 point) and stroke (4 points) for the clinical model, with the addition of uric acid (2 points), postprandial glucose (1 point), hemoglobin A1c (1 point), and proteinuria 100 mg/dL or greater (6 points) for the biochemical model. Similar discrimination measures were found between the clinical model (area under the receiver operating characteristic curve, 0.768; 95% confidence interval (CI), 0.738-0.798) and the biochemical model (area under the receiver operating characteristic curve, 0.765; 95% CI, 0.734-0.796). The area under the receiver operating characteristic curve of the clinical model was 0.667 (95% CI, 0.631-0.703) for the external validation data from community-based cohort participants. The optimal cutoff value for the clinical model was set as 7, with a sensitivity of 0.76 and a specificity of 0.66.

Conclusion

We constructed a clinical point-based model to predict the 4-year incidence of chronic kidney disease. This prediction tool may help to target Chinese subjects at risk of developing chronic kidney disease.

Keywords: Chronic kidney disease, Cohort study, Population study, Prediction model