Cargando…
Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index
BACKGROUND: We aimed to investigate the accuracy of different equations in evaluating estimated glomerular filtration rate (eGFR) in a Chinese population with different BMI levels. METHODS: A total of 837 Chinese patients were enrolled, and the eGFRs were calculated by three Chronic Kidney Disease E...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145837/ https://www.ncbi.nlm.nih.gov/pubmed/34034674 http://dx.doi.org/10.1186/s12882-021-02395-x |
_version_ | 1783697261508689920 |
---|---|
author | Li, Jiayong Xu, Xiang Luo, Jialing Chen, Wenjing Yang, Man Wang, Ling Zhu, Nan Yuan, Weijie Gu, Lijie |
author_facet | Li, Jiayong Xu, Xiang Luo, Jialing Chen, Wenjing Yang, Man Wang, Ling Zhu, Nan Yuan, Weijie Gu, Lijie |
author_sort | Li, Jiayong |
collection | PubMed |
description | BACKGROUND: We aimed to investigate the accuracy of different equations in evaluating estimated glomerular filtration rate (eGFR) in a Chinese population with different BMI levels. METHODS: A total of 837 Chinese patients were enrolled, and the eGFRs were calculated by three Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, three full-age spectrum (FAS) equations and two Modification of Diet in Renal Disease (MDRD) equations. Results of measured GFR (mGFR) by the 99Tcm-diathylenetriamine pentaacetic acid (99Tcm-DTPA) renal dynamic imaging method were the reference standards. According to BMI distribution, the patients were divided into three intervals: below 25th(BMI(P25)), 25th to 75th(BMI(P25–75)) and over 75th percentiles (BMI(P75)). RESULTS: The medium BMI of the three BMI intervals were 20.9, 24.8 and 28.9 kg/m(2), respectively. All deviations from mGFR (eGFR) were correlated with BMI (p < 0.05). The percentage of cases in which eGFR was within mGFR ±30% (P30) was used to represent the accuracy of each equation. Overall, eGFR(FAS_Cr_CysC) and eGFREPI_Cr_2009 performed similarly, showing the best agreement with mGFR among the eight equations in Bland-Altman analysis (biases: 4.1 and − 4.2 mL/min/1.73m(2), respectively). In BMI(P25) interval, eGFR(FAS_Cr) got − 0.7 of the biases with 74.2% of P30, the kappa value was 0.422 in classification of CKD stages and the AUC(60) was 0.928 in predicting renal insufficiency, and eGFREPI_Cr_2009 got 2.3 of the biases with 71.8% of P30, the kappa value was 0.418 in classification of CKD stages and the AUC(60) was 0.920 in predicting renal insufficiency. In BMI(P25–75) interval, the bias of eGFR(FAS_Cr_CysC) was 4.0 with 85.0% of P30, the kappa value was 0.501 and the AUC(60) was 0.941, and eGFR(FAS_Cr_CysC) showed balanced recognition ability of each stage of CKD (62.3, 63.7, 68.0, 71.4 and 83.3% respectively). In BMI(P75) interval, the bias of eGFR(EPI_Cr_CysC_2012) was 3.8 with 78.9% of P30, the kappa value was 0.484 the AUC(60) was 0.919, and eGFR(EPI_Cr_CysC_2012) equation showed balanced and accurate recognition ability of each stage (60.5, 60.0, 71.4, 57.1 and 100% respectively). In BMI(P75) interval, the bias of eGFR(FAS_Cr_CysC) was − 1.8 with 78.5% of P30, the kappa value was 0.485, the AUC(60) was 0.922. However, the recognition ability of each stage of eGFR(FAS_Cr_CysC) eq. (71.1, 61.2, 70.0, 42.9 and 50.0% respectively) was not as good as GFR(EPI_Cr_CysC_2012) equation. CONCLUSION: For a Chinese population, we tend to recommend choosing eGFR(FAS_Cr) and eGFR(EPI_Cr_2009) when BMI was around 20.9, eGFR(FAS_Cr_CysC) when BMI was near 24.8, and eGFR(EPI_Cr_CysC_2012) when BMI was about 28.9. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-021-02395-x. |
format | Online Article Text |
id | pubmed-8145837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81458372021-05-25 Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index Li, Jiayong Xu, Xiang Luo, Jialing Chen, Wenjing Yang, Man Wang, Ling Zhu, Nan Yuan, Weijie Gu, Lijie BMC Nephrol Research BACKGROUND: We aimed to investigate the accuracy of different equations in evaluating estimated glomerular filtration rate (eGFR) in a Chinese population with different BMI levels. METHODS: A total of 837 Chinese patients were enrolled, and the eGFRs were calculated by three Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, three full-age spectrum (FAS) equations and two Modification of Diet in Renal Disease (MDRD) equations. Results of measured GFR (mGFR) by the 99Tcm-diathylenetriamine pentaacetic acid (99Tcm-DTPA) renal dynamic imaging method were the reference standards. According to BMI distribution, the patients were divided into three intervals: below 25th(BMI(P25)), 25th to 75th(BMI(P25–75)) and over 75th percentiles (BMI(P75)). RESULTS: The medium BMI of the three BMI intervals were 20.9, 24.8 and 28.9 kg/m(2), respectively. All deviations from mGFR (eGFR) were correlated with BMI (p < 0.05). The percentage of cases in which eGFR was within mGFR ±30% (P30) was used to represent the accuracy of each equation. Overall, eGFR(FAS_Cr_CysC) and eGFREPI_Cr_2009 performed similarly, showing the best agreement with mGFR among the eight equations in Bland-Altman analysis (biases: 4.1 and − 4.2 mL/min/1.73m(2), respectively). In BMI(P25) interval, eGFR(FAS_Cr) got − 0.7 of the biases with 74.2% of P30, the kappa value was 0.422 in classification of CKD stages and the AUC(60) was 0.928 in predicting renal insufficiency, and eGFREPI_Cr_2009 got 2.3 of the biases with 71.8% of P30, the kappa value was 0.418 in classification of CKD stages and the AUC(60) was 0.920 in predicting renal insufficiency. In BMI(P25–75) interval, the bias of eGFR(FAS_Cr_CysC) was 4.0 with 85.0% of P30, the kappa value was 0.501 and the AUC(60) was 0.941, and eGFR(FAS_Cr_CysC) showed balanced recognition ability of each stage of CKD (62.3, 63.7, 68.0, 71.4 and 83.3% respectively). In BMI(P75) interval, the bias of eGFR(EPI_Cr_CysC_2012) was 3.8 with 78.9% of P30, the kappa value was 0.484 the AUC(60) was 0.919, and eGFR(EPI_Cr_CysC_2012) equation showed balanced and accurate recognition ability of each stage (60.5, 60.0, 71.4, 57.1 and 100% respectively). In BMI(P75) interval, the bias of eGFR(FAS_Cr_CysC) was − 1.8 with 78.5% of P30, the kappa value was 0.485, the AUC(60) was 0.922. However, the recognition ability of each stage of eGFR(FAS_Cr_CysC) eq. (71.1, 61.2, 70.0, 42.9 and 50.0% respectively) was not as good as GFR(EPI_Cr_CysC_2012) equation. CONCLUSION: For a Chinese population, we tend to recommend choosing eGFR(FAS_Cr) and eGFR(EPI_Cr_2009) when BMI was around 20.9, eGFR(FAS_Cr_CysC) when BMI was near 24.8, and eGFR(EPI_Cr_CysC_2012) when BMI was about 28.9. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-021-02395-x. BioMed Central 2021-05-25 /pmc/articles/PMC8145837/ /pubmed/34034674 http://dx.doi.org/10.1186/s12882-021-02395-x Text en © The Author(s) 2021 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 Li, Jiayong Xu, Xiang Luo, Jialing Chen, Wenjing Yang, Man Wang, Ling Zhu, Nan Yuan, Weijie Gu, Lijie Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
title | Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
title_full | Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
title_fullStr | Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
title_full_unstemmed | Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
title_short | Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
title_sort | choosing an appropriate glomerular filtration rate estimating equation: role of body mass index |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145837/ https://www.ncbi.nlm.nih.gov/pubmed/34034674 http://dx.doi.org/10.1186/s12882-021-02395-x |
work_keys_str_mv | AT lijiayong choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT xuxiang choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT luojialing choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT chenwenjing choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT yangman choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT wangling choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT zhunan choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT yuanweijie choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex AT gulijie choosinganappropriateglomerularfiltrationrateestimatingequationroleofbodymassindex |