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Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011
Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effec...
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Published in: | Journal of diabetes and its complications 2015-07, Vol.29 (5), p.637-643 |
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creator | Schroeder, Emily B Powers, J. David O’Connor, Patrick J Nichols, Gregory A Xu, Stanley Desai, Jay R Karter, Andrew J Morales, Leo S Newton, Katherine M Pathak, Ram D Vazquez-Benitez, Gabriela Raebel, Marsha A Butler, Melissa G Lafata, Jennifer Elston Reynolds, Kristi Thomas, Abraham Waitzfelder, Beth E Steiner, John F |
description | Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR < 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio > 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P < 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P < 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence. |
doi_str_mv | 10.1016/j.jdiacomp.2015.04.007 |
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fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1827920375</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1056872715001646</els_id><sourcerecordid>1827920375</sourcerecordid><originalsourceid>FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</originalsourceid><addsrcrecordid>eNqFksFu1DAQhiMEoqXwCpUlLhxIGNuxk1wQqGwBqRUrSiVulmNPWGezyWIni_bWd-ANeRIcbQtSLz3Z0nz_2P_8kySnFDIKVL5ps9Y6bYbNNmNARQZ5BlA8So5pWfA0l_D9cbyDkGlZsOIoeRZCCwBSCPo0OWKi4rIS_DhZLz3udIe9QTI0xKz80DtD1s72uCfWBdQBid4M_Q_ieut2zk66C-SXG1exrGscMcQKGVdIrq6XXxeXi_TDJVn6oUUzviYMQPy5-R0_SZ8nT5qoxRe350lyfb74dvYpvfjy8fPZ-4vUCM7GVOdWNKbIWS2NhaqAXEa1bSw3jOdgdG3AikJSrpuCl7VmdY2cRUI2NdMlP0leHfpu_fBzwjCqjQsGu073OExB0ZIVFQNeiIdRWcqcioqyiL68h7bD5PtoZKZEHG6Vy0jJA2X8EILHRm2922i_VxTUnJxq1V1yak5OQa5iclF4ett-qjdo_8nuoorAuwOAcXQ7h14F4-bcrPNx0soO7uE33t5rYToX49bdGvcY_vtRgSlQV_P-zOtDozsqo7u_WozAhA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1685000946</pqid></control><display><type>article</type><title>Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011</title><source>ScienceDirect Freedom Collection</source><creator>Schroeder, Emily B ; Powers, J. David ; O’Connor, Patrick J ; Nichols, Gregory A ; Xu, Stanley ; Desai, Jay R ; Karter, Andrew J ; Morales, Leo S ; Newton, Katherine M ; Pathak, Ram D ; Vazquez-Benitez, Gabriela ; Raebel, Marsha A ; Butler, Melissa G ; Lafata, Jennifer Elston ; Reynolds, Kristi ; Thomas, Abraham ; Waitzfelder, Beth E ; Steiner, John F</creator><creatorcontrib>Schroeder, Emily B ; Powers, J. David ; O’Connor, Patrick J ; Nichols, Gregory A ; Xu, Stanley ; Desai, Jay R ; Karter, Andrew J ; Morales, Leo S ; Newton, Katherine M ; Pathak, Ram D ; Vazquez-Benitez, Gabriela ; Raebel, Marsha A ; Butler, Melissa G ; Lafata, Jennifer Elston ; Reynolds, Kristi ; Thomas, Abraham ; Waitzfelder, Beth E ; Steiner, John F ; SUPREME-DM Study Group</creatorcontrib><description>Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR < 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio > 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P < 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P < 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.</description><identifier>ISSN: 1056-8727</identifier><identifier>EISSN: 1873-460X</identifier><identifier>DOI: 10.1016/j.jdiacomp.2015.04.007</identifier><identifier>PMID: 25936953</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Age ; Aged ; Aged, 80 and over ; Albuminuria - etiology ; Cardiovascular disease ; Chronic renal insufficiency ; Codes ; Cohort Studies ; Delivery of Health Care, Integrated ; Diabetes ; Diabetes mellitus ; Diabetic Nephropathies - epidemiology ; Diabetic Nephropathies - physiopathology ; Diabetic Nephropathies - urine ; Disease control ; Disease prevention ; Electronic Health Records ; Endocrinology & Metabolism ; Epidemiological Monitoring ; Epidemiology ; Female ; Follow-Up Studies ; Glomerular Filtration Rate ; Humans ; Information Storage and Retrieval ; International Classification of Diseases ; Kidney diseases ; Laboratories ; Male ; Medicare ; Methods ; Middle Aged ; Mortality ; Population ; Prevalence ; Registries ; Renal Insufficiency, Chronic - complications ; Renal Insufficiency, Chronic - epidemiology ; Renal Insufficiency, Chronic - physiopathology ; Renal Insufficiency, Chronic - urine ; Surveillance ; United States - epidemiology ; Young Adult</subject><ispartof>Journal of diabetes and its complications, 2015-07, Vol.29 (5), p.637-643</ispartof><rights>Elsevier Inc.</rights><rights>2015 Elsevier Inc.</rights><rights>Copyright © 2015 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Jul 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</citedby><cites>FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25936953$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schroeder, Emily B</creatorcontrib><creatorcontrib>Powers, J. David</creatorcontrib><creatorcontrib>O’Connor, Patrick J</creatorcontrib><creatorcontrib>Nichols, Gregory A</creatorcontrib><creatorcontrib>Xu, Stanley</creatorcontrib><creatorcontrib>Desai, Jay R</creatorcontrib><creatorcontrib>Karter, Andrew J</creatorcontrib><creatorcontrib>Morales, Leo S</creatorcontrib><creatorcontrib>Newton, Katherine M</creatorcontrib><creatorcontrib>Pathak, Ram D</creatorcontrib><creatorcontrib>Vazquez-Benitez, Gabriela</creatorcontrib><creatorcontrib>Raebel, Marsha A</creatorcontrib><creatorcontrib>Butler, Melissa G</creatorcontrib><creatorcontrib>Lafata, Jennifer Elston</creatorcontrib><creatorcontrib>Reynolds, Kristi</creatorcontrib><creatorcontrib>Thomas, Abraham</creatorcontrib><creatorcontrib>Waitzfelder, Beth E</creatorcontrib><creatorcontrib>Steiner, John F</creatorcontrib><creatorcontrib>SUPREME-DM Study Group</creatorcontrib><title>Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011</title><title>Journal of diabetes and its complications</title><addtitle>J Diabetes Complications</addtitle><description>Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR < 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio > 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P < 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P < 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.</description><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Albuminuria - etiology</subject><subject>Cardiovascular disease</subject><subject>Chronic renal insufficiency</subject><subject>Codes</subject><subject>Cohort Studies</subject><subject>Delivery of Health Care, Integrated</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetic Nephropathies - epidemiology</subject><subject>Diabetic Nephropathies - physiopathology</subject><subject>Diabetic Nephropathies - urine</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Electronic Health Records</subject><subject>Endocrinology & Metabolism</subject><subject>Epidemiological Monitoring</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Glomerular Filtration Rate</subject><subject>Humans</subject><subject>Information Storage and Retrieval</subject><subject>International Classification of Diseases</subject><subject>Kidney diseases</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medicare</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Population</subject><subject>Prevalence</subject><subject>Registries</subject><subject>Renal Insufficiency, Chronic - complications</subject><subject>Renal Insufficiency, Chronic - epidemiology</subject><subject>Renal Insufficiency, Chronic - physiopathology</subject><subject>Renal Insufficiency, Chronic - urine</subject><subject>Surveillance</subject><subject>United States - epidemiology</subject><subject>Young Adult</subject><issn>1056-8727</issn><issn>1873-460X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFksFu1DAQhiMEoqXwCpUlLhxIGNuxk1wQqGwBqRUrSiVulmNPWGezyWIni_bWd-ANeRIcbQtSLz3Z0nz_2P_8kySnFDIKVL5ps9Y6bYbNNmNARQZ5BlA8So5pWfA0l_D9cbyDkGlZsOIoeRZCCwBSCPo0OWKi4rIS_DhZLz3udIe9QTI0xKz80DtD1s72uCfWBdQBid4M_Q_ieut2zk66C-SXG1exrGscMcQKGVdIrq6XXxeXi_TDJVn6oUUzviYMQPy5-R0_SZ8nT5qoxRe350lyfb74dvYpvfjy8fPZ-4vUCM7GVOdWNKbIWS2NhaqAXEa1bSw3jOdgdG3AikJSrpuCl7VmdY2cRUI2NdMlP0leHfpu_fBzwjCqjQsGu073OExB0ZIVFQNeiIdRWcqcioqyiL68h7bD5PtoZKZEHG6Vy0jJA2X8EILHRm2922i_VxTUnJxq1V1yak5OQa5iclF4ett-qjdo_8nuoorAuwOAcXQ7h14F4-bcrPNx0soO7uE33t5rYToX49bdGvcY_vtRgSlQV_P-zOtDozsqo7u_WozAhA</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Schroeder, Emily B</creator><creator>Powers, J. David</creator><creator>O’Connor, Patrick J</creator><creator>Nichols, Gregory A</creator><creator>Xu, Stanley</creator><creator>Desai, Jay R</creator><creator>Karter, Andrew J</creator><creator>Morales, Leo S</creator><creator>Newton, Katherine M</creator><creator>Pathak, Ram D</creator><creator>Vazquez-Benitez, Gabriela</creator><creator>Raebel, Marsha A</creator><creator>Butler, Melissa G</creator><creator>Lafata, Jennifer Elston</creator><creator>Reynolds, Kristi</creator><creator>Thomas, Abraham</creator><creator>Waitzfelder, Beth E</creator><creator>Steiner, John F</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>ASE</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FPQ</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K6X</scope><scope>K9-</scope><scope>K9.</scope><scope>KB0</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>7T5</scope><scope>H94</scope></search><sort><creationdate>20150701</creationdate><title>Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011</title><author>Schroeder, Emily B ; Powers, J. David ; O’Connor, Patrick J ; Nichols, Gregory A ; Xu, Stanley ; Desai, Jay R ; Karter, Andrew J ; Morales, Leo S ; Newton, Katherine M ; Pathak, Ram D ; Vazquez-Benitez, Gabriela ; Raebel, Marsha A ; Butler, Melissa G ; Lafata, Jennifer Elston ; Reynolds, Kristi ; Thomas, Abraham ; Waitzfelder, Beth E ; Steiner, John F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c532t-a4d5fc742b6cd097046201dfd3c2340cabc0d57613af738ba2bbe321df6fb2a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adult</topic><topic>Age</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Albuminuria - etiology</topic><topic>Cardiovascular disease</topic><topic>Chronic renal insufficiency</topic><topic>Codes</topic><topic>Cohort Studies</topic><topic>Delivery of Health Care, Integrated</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetic Nephropathies - epidemiology</topic><topic>Diabetic Nephropathies - physiopathology</topic><topic>Diabetic Nephropathies - urine</topic><topic>Disease control</topic><topic>Disease prevention</topic><topic>Electronic Health Records</topic><topic>Endocrinology & Metabolism</topic><topic>Epidemiological Monitoring</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Glomerular Filtration Rate</topic><topic>Humans</topic><topic>Information Storage and Retrieval</topic><topic>International Classification of Diseases</topic><topic>Kidney diseases</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medicare</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Mortality</topic><topic>Population</topic><topic>Prevalence</topic><topic>Registries</topic><topic>Renal Insufficiency, Chronic - complications</topic><topic>Renal Insufficiency, Chronic - epidemiology</topic><topic>Renal Insufficiency, Chronic - physiopathology</topic><topic>Renal Insufficiency, Chronic - urine</topic><topic>Surveillance</topic><topic>United States - epidemiology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schroeder, Emily B</creatorcontrib><creatorcontrib>Powers, J. David</creatorcontrib><creatorcontrib>O’Connor, Patrick J</creatorcontrib><creatorcontrib>Nichols, Gregory A</creatorcontrib><creatorcontrib>Xu, Stanley</creatorcontrib><creatorcontrib>Desai, Jay R</creatorcontrib><creatorcontrib>Karter, Andrew J</creatorcontrib><creatorcontrib>Morales, Leo S</creatorcontrib><creatorcontrib>Newton, Katherine M</creatorcontrib><creatorcontrib>Pathak, Ram D</creatorcontrib><creatorcontrib>Vazquez-Benitez, Gabriela</creatorcontrib><creatorcontrib>Raebel, Marsha A</creatorcontrib><creatorcontrib>Butler, Melissa G</creatorcontrib><creatorcontrib>Lafata, Jennifer Elston</creatorcontrib><creatorcontrib>Reynolds, Kristi</creatorcontrib><creatorcontrib>Thomas, Abraham</creatorcontrib><creatorcontrib>Waitzfelder, Beth E</creatorcontrib><creatorcontrib>Steiner, John F</creatorcontrib><creatorcontrib>SUPREME-DM Study Group</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>British Nursing Database</collection><collection>British Nursing Index</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>British Nursing Index (BNI) (1985 to Present)</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>British Nursing Index</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><jtitle>Journal of diabetes and its complications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schroeder, Emily B</au><au>Powers, J. David</au><au>O’Connor, Patrick J</au><au>Nichols, Gregory A</au><au>Xu, Stanley</au><au>Desai, Jay R</au><au>Karter, Andrew J</au><au>Morales, Leo S</au><au>Newton, Katherine M</au><au>Pathak, Ram D</au><au>Vazquez-Benitez, Gabriela</au><au>Raebel, Marsha A</au><au>Butler, Melissa G</au><au>Lafata, Jennifer Elston</au><au>Reynolds, Kristi</au><au>Thomas, Abraham</au><au>Waitzfelder, Beth E</au><au>Steiner, John F</au><aucorp>SUPREME-DM Study Group</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011</atitle><jtitle>Journal of diabetes and its complications</jtitle><addtitle>J Diabetes Complications</addtitle><date>2015-07-01</date><risdate>2015</risdate><volume>29</volume><issue>5</issue><spage>637</spage><epage>643</epage><pages>637-643</pages><issn>1056-8727</issn><eissn>1873-460X</eissn><abstract>Abstract Aims Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. Methods We used the SUPREME-DM DataLink (n = 879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR < 60 mL/min/1.73 m2 ), albuminuria alone (spot urine albumin creatinine ratio > 30 mg/g or equivalent), and combinations of these approaches. Results CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 ( P < 0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 ( P < 0.001). Conclusions Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>25936953</pmid><doi>10.1016/j.jdiacomp.2015.04.007</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Age Aged Aged, 80 and over Albuminuria - etiology Cardiovascular disease Chronic renal insufficiency Codes Cohort Studies Delivery of Health Care, Integrated Diabetes Diabetes mellitus Diabetic Nephropathies - epidemiology Diabetic Nephropathies - physiopathology Diabetic Nephropathies - urine Disease control Disease prevention Electronic Health Records Endocrinology & Metabolism Epidemiological Monitoring Epidemiology Female Follow-Up Studies Glomerular Filtration Rate Humans Information Storage and Retrieval International Classification of Diseases Kidney diseases Laboratories Male Medicare Methods Middle Aged Mortality Population Prevalence Registries Renal Insufficiency, Chronic - complications Renal Insufficiency, Chronic - epidemiology Renal Insufficiency, Chronic - physiopathology Renal Insufficiency, Chronic - urine Surveillance United States - epidemiology Young Adult |
title | Prevalence of chronic kidney disease among individuals with diabetes in the SUPREME-DM Project, 2005–2011 |
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