Impact of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) GFR Estimating Equations on CKD Prevalence and Classification Among Asians
BackgroundIn 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evalua...
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| Vydané v: | Frontiers in medicine Ročník 9; s. 957437 |
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| Hlavní autori: | , , , , , , , , , , , , , , |
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| Jazyk: | English |
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Lausanne
Frontiers Media SA
14.07.2022
Frontiers Media S.A |
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| ISSN: | 2296-858X, 2296-858X |
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| Abstract | BackgroundIn 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evaluates the impact of the 2009 and 2021 creatinine-based eGFR equations on the prevalence of CKD in multiple Asian cohorts.MethodsEight population-based studies from China, India, Russia (Asian), Singapore and South Korea provided individual-level data ( n = 67,233). GFR was estimated using both the 2009 CKD-EPI equation developed using creatinine, age, sex, and race (eGFRcr [2009, ASR]) and the 2021 CKD-EPI equation developed without race (eGFRcr [2021, AS]). CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 (G3-G5). Prevalence of eGFR categories was compared within each study and within subgroups of age, sex, body mass index (BMI), diabetes, and hypertension status. The extent of reclassification was examined using net reclassification improvement (NRI).FindingsOf 67,233 adults, CKD prevalence was 8.6% ( n = 5800/67,233) using eGFRcr (2009, ASR) and 6.4% ( n = 4307/67,233) using eGFRcr (2021, AS). With the latter, CKD prevalence was reduced across all eight studies, ranging from −7.0% (95% CI −8.5% to −5.4%) to −0.4% (−1.3% to 0.5%), and across all subgroups except those in the BMI < 18.5% subgroup. Net reclassification index (NRI) was significant at −2.33% ( p < 0.001). No individuals were reclassified as a higher (more severe) eGFR category, while 1.7%−4.2% of individuals with CKD were reclassified as one eGFR category lower when eGFRcr (2021, AS) rather than eGFRcr (2009, ASR) was used.InterpretationeGFRcr (2021, AS) consistently provided reduced CKD prevalence and higher estimation of GFR among Asian cohorts than eGFRcr (2009, ASR). Based on current risk-stratified approaches to CKD management, more patients reclassified to lower-risk GFR categories could help reduce inappropriate care and its associated adverse effects among Asian renal patients. Comparison of both equations to predict progression to renal failure or adverse outcomes using prospective studies are warranted.FundingNational Medical Research Council, Singapore. |
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| AbstractList | In 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evaluates the impact of the 2009 and 2021 creatinine-based eGFR equations on the prevalence of CKD in multiple Asian cohorts.BackgroundIn 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evaluates the impact of the 2009 and 2021 creatinine-based eGFR equations on the prevalence of CKD in multiple Asian cohorts.Eight population-based studies from China, India, Russia (Asian), Singapore and South Korea provided individual-level data (n = 67,233). GFR was estimated using both the 2009 CKD-EPI equation developed using creatinine, age, sex, and race (eGFRcr [2009, ASR]) and the 2021 CKD-EPI equation developed without race (eGFRcr [2021, AS]). CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 (G3-G5). Prevalence of eGFR categories was compared within each study and within subgroups of age, sex, body mass index (BMI), diabetes, and hypertension status. The extent of reclassification was examined using net reclassification improvement (NRI).MethodsEight population-based studies from China, India, Russia (Asian), Singapore and South Korea provided individual-level data (n = 67,233). GFR was estimated using both the 2009 CKD-EPI equation developed using creatinine, age, sex, and race (eGFRcr [2009, ASR]) and the 2021 CKD-EPI equation developed without race (eGFRcr [2021, AS]). CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 (G3-G5). Prevalence of eGFR categories was compared within each study and within subgroups of age, sex, body mass index (BMI), diabetes, and hypertension status. The extent of reclassification was examined using net reclassification improvement (NRI).Of 67,233 adults, CKD prevalence was 8.6% (n = 5800/67,233) using eGFRcr (2009, ASR) and 6.4% (n = 4307/67,233) using eGFRcr (2021, AS). With the latter, CKD prevalence was reduced across all eight studies, ranging from -7.0% (95% CI -8.5% to -5.4%) to -0.4% (-1.3% to 0.5%), and across all subgroups except those in the BMI < 18.5% subgroup. Net reclassification index (NRI) was significant at -2.33% (p < 0.001). No individuals were reclassified as a higher (more severe) eGFR category, while 1.7%-4.2% of individuals with CKD were reclassified as one eGFR category lower when eGFRcr (2021, AS) rather than eGFRcr (2009, ASR) was used.FindingsOf 67,233 adults, CKD prevalence was 8.6% (n = 5800/67,233) using eGFRcr (2009, ASR) and 6.4% (n = 4307/67,233) using eGFRcr (2021, AS). With the latter, CKD prevalence was reduced across all eight studies, ranging from -7.0% (95% CI -8.5% to -5.4%) to -0.4% (-1.3% to 0.5%), and across all subgroups except those in the BMI < 18.5% subgroup. Net reclassification index (NRI) was significant at -2.33% (p < 0.001). No individuals were reclassified as a higher (more severe) eGFR category, while 1.7%-4.2% of individuals with CKD were reclassified as one eGFR category lower when eGFRcr (2021, AS) rather than eGFRcr (2009, ASR) was used.eGFRcr (2021, AS) consistently provided reduced CKD prevalence and higher estimation of GFR among Asian cohorts than eGFRcr (2009, ASR). Based on current risk-stratified approaches to CKD management, more patients reclassified to lower-risk GFR categories could help reduce inappropriate care and its associated adverse effects among Asian renal patients. Comparison of both equations to predict progression to renal failure or adverse outcomes using prospective studies are warranted.InterpretationeGFRcr (2021, AS) consistently provided reduced CKD prevalence and higher estimation of GFR among Asian cohorts than eGFRcr (2009, ASR). Based on current risk-stratified approaches to CKD management, more patients reclassified to lower-risk GFR categories could help reduce inappropriate care and its associated adverse effects among Asian renal patients. Comparison of both equations to predict progression to renal failure or adverse outcomes using prospective studies are warranted.National Medical Research Council, Singapore.FundingNational Medical Research Council, Singapore. BackgroundIn 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However, this new equation is not ethnic-specific, and prevalence of CKD in Asians is known to differ from other ethnicities. This study evaluates the impact of the 2009 and 2021 creatinine-based eGFR equations on the prevalence of CKD in multiple Asian cohorts.MethodsEight population-based studies from China, India, Russia (Asian), Singapore and South Korea provided individual-level data (n = 67,233). GFR was estimated using both the 2009 CKD-EPI equation developed using creatinine, age, sex, and race (eGFRcr [2009, ASR]) and the 2021 CKD-EPI equation developed without race (eGFRcr [2021, AS]). CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 (G3-G5). Prevalence of eGFR categories was compared within each study and within subgroups of age, sex, body mass index (BMI), diabetes, and hypertension status. The extent of reclassification was examined using net reclassification improvement (NRI).FindingsOf 67,233 adults, CKD prevalence was 8.6% (n = 5800/67,233) using eGFRcr (2009, ASR) and 6.4% (n = 4307/67,233) using eGFRcr (2021, AS). With the latter, CKD prevalence was reduced across all eight studies, ranging from −7.0% (95% CI −8.5% to −5.4%) to −0.4% (−1.3% to 0.5%), and across all subgroups except those in the BMI < 18.5% subgroup. Net reclassification index (NRI) was significant at −2.33% (p < 0.001). No individuals were reclassified as a higher (more severe) eGFR category, while 1.7%−4.2% of individuals with CKD were reclassified as one eGFR category lower when eGFRcr (2021, AS) rather than eGFRcr (2009, ASR) was used.InterpretationeGFRcr (2021, AS) consistently provided reduced CKD prevalence and higher estimation of GFR among Asian cohorts than eGFRcr (2009, ASR). Based on current risk-stratified approaches to CKD management, more patients reclassified to lower-risk GFR categories could help reduce inappropriate care and its associated adverse effects among Asian renal patients. Comparison of both equations to predict progression to renal failure or adverse outcomes using prospective studies are warranted.FundingNational Medical Research Council, Singapore. |
| Author | Lim, Cynthia Ciwei Tai, E. Shyong Tham, Yih Chung Wang, Ya Xing Cheng, Ching-Yu Jonas, Jost B. Park, Kyu Hyung Bikbov, Mukharram M. He, Feng Rim, Tyler Hyungtaek Nangia, Vinay Kang, Se Woong Sultana, Rehena Sabanayagam, Charumathi Betzler, Bjorn Kaijun |
| AuthorAffiliation | 2 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore , Singapore 8 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore 11 Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University , Mannheim , Germany 6 Beijing Institute of Ophthalmology, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University , Beijing , China 4 Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School , Singapore , Singapore 5 Department of Renal Medicine, Singapore General Hospital , Singapore , Singapore 1 Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore 10 Ufa Eye Research Institute , Ufa , Russia 14 Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine , Seoul , South Korea 3 Centre for Quantitative Medicine, |
| AuthorAffiliation_xml | – name: 5 Department of Renal Medicine, Singapore General Hospital , Singapore , Singapore – name: 10 Ufa Eye Research Institute , Ufa , Russia – name: 13 Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul , South Korea – name: 6 Beijing Institute of Ophthalmology, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University , Beijing , China – name: 12 Institute of Molecular and Clinical Ophthalmology , Basel , Switzerland – name: 4 Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School , Singapore , Singapore – name: 9 Department of Ophthalmology, Yonsei University College of Medicine , Seoul , South Korea – name: 11 Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University , Mannheim , Germany – name: 7 Suraj Eye Institute , Nagpur , India – name: 8 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore – name: 2 Singapore Eye Research Institute, Singapore National Eye Centre , Singapore , Singapore – name: 3 Centre for Quantitative Medicine, Duke-NUS Medical School , Singapore , Singapore – name: 1 Yong Loo Lin School of Medicine, National University of Singapore , Singapore , Singapore – name: 14 Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine , Seoul , South Korea |
| Author_xml | – sequence: 1 givenname: Bjorn Kaijun surname: Betzler fullname: Betzler, Bjorn Kaijun – sequence: 2 givenname: Rehena surname: Sultana fullname: Sultana, Rehena – sequence: 3 givenname: Feng surname: He fullname: He, Feng – sequence: 4 givenname: Yih Chung surname: Tham fullname: Tham, Yih Chung – sequence: 5 givenname: Cynthia Ciwei surname: Lim fullname: Lim, Cynthia Ciwei – sequence: 6 givenname: Ya Xing surname: Wang fullname: Wang, Ya Xing – sequence: 7 givenname: Vinay surname: Nangia fullname: Nangia, Vinay – sequence: 8 givenname: E. Shyong surname: Tai fullname: Tai, E. Shyong – sequence: 9 givenname: Tyler Hyungtaek surname: Rim fullname: Rim, Tyler Hyungtaek – sequence: 10 givenname: Mukharram M. surname: Bikbov fullname: Bikbov, Mukharram M. – sequence: 11 givenname: Jost B. surname: Jonas fullname: Jonas, Jost B. – sequence: 12 givenname: Se Woong surname: Kang fullname: Kang, Se Woong – sequence: 13 givenname: Kyu Hyung surname: Park fullname: Park, Kyu Hyung – sequence: 14 givenname: Ching-Yu surname: Cheng fullname: Cheng, Ching-Yu – sequence: 15 givenname: Charumathi surname: Sabanayagam fullname: Sabanayagam, Charumathi |
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| Copyright | 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Betzler, Sultana, He, Tham, Lim, Wang, Nangia, Tai, Rim, Bikbov, Jonas, Kang, Park, Cheng and Sabanayagam. Copyright © 2022 Betzler, Sultana, He, Tham, Lim, Wang, Nangia, Tai, Rim, Bikbov, Jonas, Kang, Park, Cheng and Sabanayagam. 2022 Betzler, Sultana, He, Tham, Lim, Wang, Nangia, Tai, Rim, Bikbov, Jonas, Kang, Park, Cheng and Sabanayagam |
| Copyright_xml | – notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Copyright © 2022 Betzler, Sultana, He, Tham, Lim, Wang, Nangia, Tai, Rim, Bikbov, Jonas, Kang, Park, Cheng and Sabanayagam. – notice: Copyright © 2022 Betzler, Sultana, He, Tham, Lim, Wang, Nangia, Tai, Rim, Bikbov, Jonas, Kang, Park, Cheng and Sabanayagam. 2022 Betzler, Sultana, He, Tham, Lim, Wang, Nangia, Tai, Rim, Bikbov, Jonas, Kang, Park, Cheng and Sabanayagam |
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| Snippet | BackgroundIn 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR).... In 2021, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) validated a new equation for estimated glomerular filtration rate (eGFR). However,... |
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| SubjectTerms | Age Asian Asian people Blood pressure Body mass index chronic kidney disease Collaboration Creatinine Diabetes Epidemiology Ethnicity glomerular filtration rate Hypertension Kidney diseases Medical referrals Medicine Minority & ethnic groups Population-based studies prevalence Risk factors |
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| Title | Impact of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) GFR Estimating Equations on CKD Prevalence and Classification Among Asians |
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