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Published in: BMC Nephrology 1/2022

Open Access 01-12-2022 | Chronic Kidney Disease | Research

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

Authors: Qiuxia Zhang, Junyan Lu, Li Lei, Guodong Li, Hongbin Liang, Jingyi Zhang, Yun Li, Xiangqi Lu, Xinlu Zhang, Yaode Chen, Jiazhi Pan, Yejia Chen, Xinxin Lin, Xiaobo Li, Shiyu Zhou, Shengli An, Jiancheng Xiu

Published in: BMC Nephrology | Issue 1/2022

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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.
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Metadata
Title
Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease
Authors
Qiuxia Zhang
Junyan Lu
Li Lei
Guodong Li
Hongbin Liang
Jingyi Zhang
Yun Li
Xiangqi Lu
Xinlu Zhang
Yaode Chen
Jiazhi Pan
Yejia Chen
Xinxin Lin
Xiaobo Li
Shiyu Zhou
Shengli An
Jiancheng Xiu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Nephrology / Issue 1/2022
Electronic ISSN: 1471-2369
DOI
https://doi.org/10.1186/s12882-022-02696-9

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