Prediction of the risk of 3-year chronic kidney disease among elderly people: a community-based cohort study

Abstract

Objective

We conducted a community-based cohort study to predict the 3-year occurrence of chronic kidney disease (CKD) among population aged ≥60 years.

Method

Participants were selected from two communities through randomized cluster sampling in Jiading District of Shanghai, China. The two communities were randomly divided into a development cohort (n = 12012) and a validation cohort (n = 6248) with a 3-year follow-up. Logistic regression analysis was used to determine the independent predictors. A nomogram was established to predict the occurrence of CKD within 3 years. The area under the curve (AUC), the calibration curve and decision curve analysis (DCA) curve were used to evaluate the model.

Result

At baseline, participants in development cohort and validation cohort were with the mean age of 68.24 ± 5.87 and 67.68 ± 5.26 years old, respectively. During 3 years, 1516 (12.6%) and 544 (8.9%) new cases developed CKD in the development and validation cohorts, respectively. Nine variables (age, systolic blood pressure, body mass index, exercise, previous hypertension, triglycerides, fasting plasma glucose, glycated hemoglobin and serum creatinine) were included in the prediction model. The AUC value was 0.742 [95% confidence interval (CI), 0.728–0.756] in the development cohort and 0.881(95%CI, 0.867–0.895) in the validation cohort, respectively. The calibration curves and DCA curves demonstrate an effective predictive model.

Conclusion

Our nomogram model is a simple, reasonable and reliable tool for predicting the risk of 3-year CKD in community-dwelling elderly people, which is helpful for timely intervention and reducing the incidence of CKD.

Keywords:

• Chronic kidney disease
• risk prediction model
• nomogram
• elderly people
• community-based cohort study

Acknowledgments

We thank all the participants for their dedication to the study.

Authors’ contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation, or in all these areas, took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure statement

The authors declare no potential conflicts of interest with respect to the research, authorship, or publication of this article.

Additional information

Funding

The research was funded by Shanghai 2023 "Science and Technology Innovation Action Plan" Excellent Technology Leader Project (23XD1433900) and the National Natural Science Foundation of China (Grant No. 81872720)