Elsevier

The American Journal of Medicine

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

Clinical research study
A Prediction Model for the Risk of Incident Chronic Kidney Disease

https://doi.org/10.1016/j.amjmed.2010.05.010Get rights and content

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.

Section snippets

Study Design and Participants

The study design and participants have been described.29, 30 In brief, a prospective cohort study design was conducted among participants who undertook health examinations at the National Taiwan University Hospital since 2003. All of the subjects received a standard questionnaire and physical examination in the Health Management Center, National Taiwan University Hospital. The protocol was approved by the institutional review board of National Taiwan University, and the written informed

Results

Figure 1 shows the study design and participants. After the exclusion of those providing 1 measurement (n = 19,167) and a filtration rate less than 60 mL/min/1.73 m2 (n = 294), 5168 participants were included in this study. The distribution between participants and nonparticipants was similar. During a median of 2.2 (interquartile range, follow-up 1.5-2.9) years follow-up, 190 individuals (3.7%) developed chronic kidney disease. The basic characteristics and clinical and biochemical measures of the

Discussion

By using a large-scale cohort study of a population with health checkups, we developed a simple point-based model to predict the 4-year risk of incident chronic kidney disease in a Chinese population according to 5 variables: age, body mass index, diastolic blood pressure, history of type 2 diabetes, and history of stroke. These clinical variables are easily obtained in public health and clinical practice, and the points system we developed is simple to use. The availability of the simple

Limitations

To our knowledge, this is the first prediction model for chronic kidney disease specifically in the Chinese population. Because of the large sample size, the estimates from our prediction models were found to be stable, as demonstrated by the external validation data from community-based participants. The use of homogeneous participants from one health center could reduce the possibility of selection bias. However, several potential limitations of this study should be mentioned: First, the

Conclusions

We constructed a clinical point-based model to predict the 4-year incidence of chronic kidney disease, and this model performed similarly to other prediction models based on biochemical measures within the ethnic Chinese population. This simple points-based tool may help to target preventive interventions for subjects at risk of developing chronic kidney disease and to improve prevention and treatment strategies in the Chinese population.

Acknowledgments

The authors thank the staff of the Health Management Center, National Taiwan University Hospital, and the participants for their valuable contribution.

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    Funding:National Science Council (NSC 97-2314-B-002-130 -MY3, 97-3112-B-002-034).

    Conflict of Interest: None of the authors have any conflicts of interest associated with the work presented in this manuscript.

    Authorship: All authors had access to the data and played a role in writing this manuscript.

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