A back propagation neural network approach to estimate the glomerular filtration rate in an older population

The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Adults aged ≥ 65 years who under...

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Published inBMC geriatrics Vol. 23; no. 1; pp. 322 - 10
Main Authors Jiang, Shimin, Li, Yetong, Jiao, Yuanyuan, Zhang, Danyang, Wang, Ying, Li, Wenge
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 24.05.2023
BioMed Central
BMC
Subjects
Accuracy
Age groups
Aged
Analysis
Back propagation
Back propagation neural network
Bias
Care and treatment
Creatinine
Deep learning
Diabetes
Diagnosis
Elderly
Epidemiology
Estimation equation
Evaluation
Female
Geriatrics
Glomerular Filtration Rate
Humans
Independent variables
Kidney
Kidney diseases
Machine learning
Male
Neural networks
Neural Networks, Computer
Neurons
Older people
Pentetic Acid
Personal health
Renal Insufficiency, Chronic - diagnostic imaging
Serum creatinine
Software
Technetium
China
Back propagation neural network
Estimation equation
Elderly
Serum creatinine
Glomerular filtration rate
Online AccessGet full text
ISSN1471-2318
1471-2318
DOI10.1186/s12877-023-04027-5

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Abstract The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid ( Tc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m , which was smaller than that of LMR (4.59 ml/min/1.73 m ; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m ; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m ; p = 0.31), EKFC (-1.41 ml/min/1.73 m ; p = 0.26), BIS1 (0.64 ml/min/1.73 m ; p = 0.99), and MDRD (1.11 ml/min/1.73 m ; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m ) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m , the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m ). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation. The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.
AbstractList BackgroundThe use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group.MethodsAdults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR).ResultsThe study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m2, which was smaller than that of LMR (4.59 ml/min/1.73 m2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m2; p = 0.31), EKFC (-1.41 ml/min/1.73 m2; p = 0.26), BIS1 (0.64 ml/min/1.73 m2; p = 0.99), and MDRD (1.11 ml/min/1.73 m2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55−2.78] and 0.24 [-2.58−1.61], respectively), smaller than any other equation.ConclusionsThe novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.
Abstract Background The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Methods Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). Results The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m2, which was smaller than that of LMR (4.59 ml/min/1.73 m2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m2; p = 0.31), EKFC (-1.41 ml/min/1.73 m2; p = 0.26), BIS1 (0.64 ml/min/1.73 m2; p = 0.99), and MDRD (1.11 ml/min/1.73 m2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55−2.78] and 0.24 [-2.58−1.61], respectively), smaller than any other equation. Conclusions The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.
Background The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Methods Adults aged [greater than or equal to] 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (.sup.99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). Results The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 [+ or -] 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m.sup.2, which was smaller than that of LMR (4.59 ml/min/1.73 m.sup.2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m.sup.2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m.sup.2; p = 0.31), EKFC (-1.41 ml/min/1.73 m.sup.2; p = 0.26), BIS1 (0.64 ml/min/1.73 m.sup.2; p = 0.99), and MDRD (1.11 ml/min/1.73 m.sup.2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m.sup.2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m.sup.2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m.sup.2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation. Conclusions The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use. Keywords: Elderly, Glomerular filtration rate, Estimation equation, Back propagation neural network, Serum creatinine
The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group.BACKGROUNDThe use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group.Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR).METHODSAdults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR).The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m2, which was smaller than that of LMR (4.59 ml/min/1.73 m2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m2; p = 0.31), EKFC (-1.41 ml/min/1.73 m2; p = 0.26), BIS1 (0.64 ml/min/1.73 m2; p = 0.99), and MDRD (1.11 ml/min/1.73 m2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation.RESULTSThe study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m2, which was smaller than that of LMR (4.59 ml/min/1.73 m2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m2; p = 0.31), EKFC (-1.41 ml/min/1.73 m2; p = 0.26), BIS1 (0.64 ml/min/1.73 m2; p = 0.99), and MDRD (1.11 ml/min/1.73 m2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation.The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.CONCLUSIONSThe novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.
Accurate estimation of kidney function in older adults is an important step to management of chronic kidney disease. BPNN tool highly improved precision and accuracy, although bias remains suboptimal. BPNN tool can be recommended for clinical use routinely, especially for patients with GFR < 45 ml/min/1.73 m 2 .
The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Adults aged [greater than or equal to] 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid (.sup.99mTc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 [+ or -] 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m.sup.2, which was smaller than that of LMR (4.59 ml/min/1.73 m.sup.2; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m.sup.2; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m.sup.2; p = 0.31), EKFC (-1.41 ml/min/1.73 m.sup.2; p = 0.26), BIS1 (0.64 ml/min/1.73 m.sup.2; p = 0.99), and MDRD (1.11 ml/min/1.73 m.sup.2; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m.sup.2) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m.sup.2, the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m.sup.2). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation. The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.
The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer any performance advantages. We therefore aimed to develop an accurate GFR-estimating tool for this age group. Adults aged ≥ 65 years who underwent GFR measurement by technetium-99 m-diethylene triamine pentaacetic acid ( Tc-DTPA) renal dynamic imaging were included. Data were randomly split into a training set containing 80% of the participants and a test set containing the remaining 20% of the subjects. The Back propagation neural network (BPNN) approach was used to derive a novel GFR estimation tool; then we compared the performance of the BPNN tool with six creatinine-based equations (Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI], European Kidney Function Consortium [EKFC], Berlin Initiative Study-1 [BIS1], Lund-Malmö Revised [LMR], Asian modified CKD-EPI, and Modification of Diet in Renal Disease [MDRD]) in the test cohort. Three equation performance criteria were considered: bias (difference between measured GFR and estimated GFR), precision (interquartile range [IQR] of the median difference), and accuracy P30 (percentage of GFR estimates that are within 30% of measured GFR). The study included 1,222 older adults. The mean age of both the training cohort (n = 978) and the test cohort (n = 244) was 72 ± 6 years, with 544 (55.6%) and 129 (52.9%) males, respectively. The median bias of BPNN was 2.06 ml/min/1.73 m , which was smaller than that of LMR (4.59 ml/min/1.73 m ; p = 0.03), and higher than that of the Asian modified CKD-EPI (-1.43 ml/min/1.73 m ; p = 0.02). The median bias between BPNN and each of CKD-EPI (2.19 ml/min/1.73 m ; p = 0.31), EKFC (-1.41 ml/min/1.73 m ; p = 0.26), BIS1 (0.64 ml/min/1.73 m ; p = 0.99), and MDRD (1.11 ml/min/1.73 m ; p = 0.45) was not significant. However, the BPNN had the highest precision IQR (14.31 ml/min/1.73 m ) and the greatest accuracy P30 among all equations (78.28%). At measured GFR < 45 ml/min/1.73 m , the BPNN has highest accuracy P30 (70.69%), and highest precision IQR (12.46 ml/min/1.73 m ). The biases of BPNN and BIS1 equations were similar (0.74 [-1.55-2.78] and 0.24 [-2.58-1.61], respectively), smaller than any other equation. The novel BPNN tool is more accurate than the currently available creatinine-based GFR estimation equations in an older population and could be recommended for routine clinical use.
ArticleNumber 322
Audience Academic
Author Wang, Ying
Zhang, Danyang
Jiao, Yuanyuan
Jiang, Shimin
Li, Yetong
Li, Wenge
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37226135$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1186_s12882_025_03972_0
crossref_primary_10_1080_0886022X_2023_2294152
crossref_primary_10_1093_ndt_gfad241
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Issue 1
Keywords Back propagation neural network
Estimation equation
Elderly
Serum creatinine
Glomerular filtration rate
Language English
License 2023. The Author(s).
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Snippet The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear to offer...
Background The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear...
BackgroundThe use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does not appear...
Accurate estimation of kidney function in older adults is an important step to management of chronic kidney disease. BPNN tool highly improved precision and...
Abstract Background The use of creatinine-based glomerular filtration rate (GFR)-estimating equations to evaluate kidney function in elderly individuals does...
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StartPage 322
SubjectTerms Accuracy
Age groups
Aged
Analysis
Back propagation
Back propagation neural network
Bias
Care and treatment
Creatinine
Deep learning
Diabetes
Diagnosis
Elderly
Epidemiology
Estimation equation
Evaluation
Female
Geriatrics
Glomerular Filtration Rate
Humans
Independent variables
Kidney
Kidney diseases
Machine learning
Male
Neural networks
Neural Networks, Computer
Neurons
Older people
Pentetic Acid
Personal health
Renal Insufficiency, Chronic - diagnostic imaging
Serum creatinine
Software
Technetium
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Title A back propagation neural network approach to estimate the glomerular filtration rate in an older population
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