Nomogram to predict risk of incident chronic kidney disease in high-risk population of cardiovascular disease in China: community-based cohort study

Abstract

Aims: To develop a nomogram for incident chronic kidney disease (CKD) risk evaluation among community residents with high cardiovascular disease (CVD) risk.

Methods: In this retrospective cohort study, 5730 non-CKD residents with high CVD risk participating the National Basic Public Health Service between January 2015 and December 2020 in Guangzhou were included. Endpoint was incident CKD defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m² during the follow-up period. The entire cohorts were randomly (2:1) assigned to a development cohort and a validation cohort. Predictors of incident CKD were selected by multivariable Cox regression and stepwise approach. A nomogram based on these predictors was developed and evaluated with concordance index (C-index) and area under curve (AUC).

Results: During the median follow-up period of 4.22 years, the incidence of CKD was 19.09% (n=1094) in the entire cohort, 19.03% (727 patients) in the development cohort and 19.21% (367 patients) in the validation cohort. Age, body mass index, eGFR 60-89 mL/min/1.73 m², diabetes and hypertension were selected as predictors. The nomogram demonstrated a good discriminative power with C-index of 0.778 and 0.785 in the development and validation cohort. The 3-year, 4-year and 5-year AUCs were 0.817, 0.814 and 0.834 in the development cohort, and 0.830, 0.847 and 0.839 in the validation cohort.

Conclusion: Our nomogram based on five readily available predictors is a reliable tool to identify high-CVD risk patients at risk of incident CKD. This prediction model may help improving the healthcare strategies in primary care.

Keywords: chronic renal failure; coronary heart disease; public health.

Figure 1

Flow chart of study design and participants. *Cardiovascular risk assessment methods according to Thomas a Gaziano et al. eGFR, estimated glomerular filtration rate.

Figure 2

Nomogram to predict the 3 years, 4 years and 5 years risk of chronic kidney disease (CKD). To use the nomogram, find the position of each variable on the relative axis, draw a line to the points axis for the number of points, add the points derived from all the variables together, and refer to the total points axis to determine the 3 years, 4 years or 5 years CKD probabilities. For example, one 75-year-old person with hypertention and diabetes, and his BMI and EGFR are 25 and 80 mL/min per 1.73 m². The points of each item are 50, 22.5, 25, 15, 87.5, respectively. And the total points is 200, it is obtained by adding those points. BMI, body mass index; eGFR, estimated glomerular filtration rate.

Figure 3

Receiver operating characteristic curves for the risk prediction model applied to the study population. The 3-year AUCs in the development cohort (A) and in the validation cohort (B). The 4-year AUCs in the development cohort (C) and in the validation cohort (D). The 5-year AUCs in the development cohort (E) and in the validation cohort (F). AUC, area under the curve.

Figure 4

Validity of the predictive value of the nomogram in estimating the risk of the 3 years, 4 years and 5 years of incident chronic kidney disease (CKD). Validity of the predictive value in the development cohort (A) and in the validation cohort (B) of the 3 years CKD probability. Validity of the predictive value in the development cohort (C) and in the validation cohort (D) of the 4 years CKD probability. Validity of the predictive value in the development cohort (E) and in the validation cohort (F) of the 5 years CKD probability.

Figure 5

Risk stratification of 3 years, 4 years and 5 years incident chronic kidney disease (CKD) based on the nomogram scores (A, B, C). Low-risk group (scores＜106), high-risk group (scores ≥106). The predicted rates of CKD in the validation cohort were closed to those in the development cohort inside each of the three risk groups.