Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Feb 10;23(1):62.
doi: 10.1186/s12882-022-02696-9.

Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease

Qiuxia Zhang #  1 , Junyan Lu #  2 , Li Lei #  1 , Guodong Li  1 , Hongbin Liang  1 , Jingyi Zhang  3 , Yun Li  4 , Xiangqi Lu  1 , Xinlu Zhang  1 , Yaode Chen  1 , Jiazhi Pan  1 , Yejia Chen  1 , Xinxin Lin  1 , Xiaobo Li  1 , Shiyu Zhou  5 , Shengli An  6 , Jiancheng Xiu  7
Affiliations

Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease

Qiuxia Zhang et al. BMC Nephrol. .

Abstract

Background: To develop a reliable model to predict rapid kidney function decline (RKFD) among population at risk of cardiovascular disease.

Methods: In this retrospective study, key monitoring residents including the elderly, and patients with hypertension or diabetes of China National Basic Public Health Service who underwent community annual physical examinations from January 2015 to December 2020 were included. Healthy records were extracted from regional chronic disease management platform. RKFD was defined as the reduction of estimated glomerular filtration rate (eGFR) ≥ 40% during follow-up period. The entire cohort were randomly assigned to a development cohort and a validation cohort in a 2:1 ratio. Cox regression analysis was used to identify the independent predictors. A nomogram was established based on the development cohort. The concordance index (C-index) and calibration plots were calculated. Decision curve analysis was applied to evaluate the clinical utility.

Results: A total of 8455 subjects were included. During the median follow-up period of 3.72 years, the incidence of RKFD was 11.96% (n = 1011), 11.98% (n = 676) and 11.92% (n = 335) in the entire cohort, development cohort and validation cohort, respectively. Age, eGFR, hemoglobin, systolic blood pressure, and diabetes were identified as predictors for RKFD. Good discriminating performance was observed in both the development (C-index, 0.73) and the validation (C-index, 0.71) cohorts, and the AUCs for predicting 5-years RKFD was 0.763 and 0.740 in the development and the validation cohort, respectively. Decision curve analysis further confirmed the clinical utility of the nomogram.

Conclusions: Our nomogram based on five readily accessible variables (age, eGFR, hemoglobin, systolic blood pressure, and diabetes) is a useful tool to identify high risk patients for RKFD among population at risk of cardiovascular disease in primary care. Whereas, further external validations are needed before clinical generalization.

Keywords: Cardiovascular disease; Rapid kidney function decline; Risk prediction model.

PubMed Disclaimer

Conflict of interest statement

None of authors declared potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Fig. 1
Fig. 1
Flow chart of study design and participants. eGFR: estimated glomerular filtration rate
Fig. 2
Fig. 2
Nomogram to predict the 5-year risk of rapid kidney function decline (RKFD). 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 5-year of RKFD probabilities. eGFR: estimated glomerular filtration rate, SBP: systolic blood pressure
Fig. 3
Fig. 3
Receiver operating characteristic curves and predictive value validation of the nomogram. The 5-year AUC in the development cohort (A) and in the validation cohort (B). Validity of the predictive value in the development cohort (C) and in the validation cohort (D) of the 5-year CKD probability. AUC: area under the curve
Fig. 4
Fig. 4
Kaplan-Meier curves of incident rapid kidney function decline for patients in the low- and high-risk groups stratified by total score of 150 points calculated from nomogram. Low-risk group (scores≤150), high-risk group (scores>150)
See this image and copyright information in PMC

References

    1. Luyckx VA, Al-Aly Z, Bello AK, Bellorin-Font E, Carlini RG, Fabian J, et al. Sustainable development goals relevant to kidney health: an update on progress. Nat Rev Nephrol. 2020. 10.1038/s41581-020-00363-6. - PMC - PubMed
    1. Collaborator GDAI. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392:1785–1789. doi: 10.1016/S0140-6736(18)32279-7. - DOI - PMC - PubMed
    1. Matsushita K, Selvin E, Bash LD, Franceschini N, Astor BC, Coresh J. Change in estimated GFR associates with coronary heart disease and mortality. J Am Soc Nephrol. 2009;20(12):2617–2624. doi: 10.1681/ASN.2009010025. - DOI - PMC - PubMed
    1. Al-Shamsi S, Oulhaj A, Regmi D, Govender RD. Use of estimated glomerular filtration rate to predict incident chronic kidney disease in patients at risk of cardiovascular disease: a retrospective study. BMC Nephrol. 2019;20(1):325. doi: 10.1186/s12882-019-1494-8. - DOI - PMC - PubMed
    1. Malhotra R, Katz R, Jotwani V, Agarwal A, Cohen DL, Cushman WC, et al. Estimated GFR variability and risk of cardiovascular events and mortality in SPRINT (systolic blood pressure intervention trial). American journal of kidney diseases : the official journal of the National Kidney Foundation. 2020. 10.1053/j.ajkd.2020.10.016. - PMC - PubMed

Publication types