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Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease
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...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830119/ https://www.ncbi.nlm.nih.gov/pubmed/35144580 http://dx.doi.org/10.1186/s12882-022-02696-9 |
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author | Zhang, Qiuxia Lu, Junyan Lei, Li Li, Guodong Liang, Hongbin Zhang, Jingyi Li, Yun Lu, Xiangqi Zhang, Xinlu Chen, Yaode Pan, Jiazhi Chen, Yejia Lin, Xinxin Li, Xiaobo Zhou, Shiyu An, Shengli Xiu, Jiancheng |
author_facet | Zhang, Qiuxia Lu, Junyan Lei, Li Li, Guodong Liang, Hongbin Zhang, Jingyi Li, Yun Lu, Xiangqi Zhang, Xinlu Chen, Yaode Pan, Jiazhi Chen, Yejia Lin, Xinxin Li, Xiaobo Zhou, Shiyu An, Shengli Xiu, Jiancheng |
author_sort | Zhang, Qiuxia |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02696-9. |
format | Online Article Text |
id | pubmed-8830119 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88301192022-02-11 Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease Zhang, Qiuxia Lu, Junyan Lei, Li Li, Guodong Liang, Hongbin Zhang, Jingyi Li, Yun Lu, Xiangqi Zhang, Xinlu Chen, Yaode Pan, Jiazhi Chen, Yejia Lin, Xinxin Li, Xiaobo Zhou, Shiyu An, Shengli Xiu, Jiancheng BMC Nephrol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-022-02696-9. BioMed Central 2022-02-10 /pmc/articles/PMC8830119/ /pubmed/35144580 http://dx.doi.org/10.1186/s12882-022-02696-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhang, Qiuxia Lu, Junyan Lei, Li Li, Guodong Liang, Hongbin Zhang, Jingyi Li, Yun Lu, Xiangqi Zhang, Xinlu Chen, Yaode Pan, Jiazhi Chen, Yejia Lin, Xinxin Li, Xiaobo Zhou, Shiyu An, Shengli Xiu, Jiancheng Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
title | Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
title_full | Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
title_fullStr | Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
title_full_unstemmed | Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
title_short | Nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
title_sort | nomogram to predict rapid kidney function decline in population at risk of cardiovascular disease |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8830119/ https://www.ncbi.nlm.nih.gov/pubmed/35144580 http://dx.doi.org/10.1186/s12882-022-02696-9 |
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