A simple prediction score for kidney disease in the Korean population
Aim: Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed. Methods: Using simple, non‐invasive questions, we developed a prediction mo...
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| Veröffentlicht in: | Nephrology (Carlton, Vic.) Vic.), 2012-03, Vol.17 (3), p.278-284 |
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| creator | KWON, KEUN-SANG BANG, HEEJUNG BOMBACK, ANDREW S KOH, DAI-HA YUM, JUNG-HO LEE, JU-HYUNG LEE, SIK PARK, SUNG K YOO, KEUN-YOUNG PARK, SUE K CHANG, SOUNG-HOON LIM, HYUN-SUL CHOI, JOONG MYUNG KSHIRSAGAR, ABHIJIT V |
| description | Aim: Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed.
Methods: Using simple, non‐invasive questions, we developed a prediction model for chronic kidney disease from national population samples in Korea. The Korean National Health and Nutrition Examination Survey (n = 6565) was used for model development while validation was performed in two independent population samples, internal (n = 2921) and external datasets (n = 8166). Chronic kidney disease was defined as glomerular filtration rate |
| doi_str_mv | 10.1111/j.1440-1797.2011.01552.x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_963491737</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>963491737</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4062-f80f9ed5406a8889cb45b3527c3bb6da565b5d6b004c999f4629f0e69ea3363</originalsourceid><addsrcrecordid>eNqNkMtOwzAQRS0Eorx-AXnHKsGP2IkXLBAt5aWCAAmJjeUkE-GSJsFuRfv3OLR0jTcea-6ZsQ5CmJKYhnM-jWmSkIimKo0ZoTQmVAgWL3fQwbaxG2rOSCS4yAbo0PspITRlku6jAWM0pYqzAzS6xN7Ouhpw56C0xdy2DfZF6wBXrcOftmxghUvrwXjAtsHzD8D3oW0a3LXdojY9cYz2KlN7ONncR-jlevR6dRM9PI5vry4foiIhkkVVRioFpQgPk2WZKvJE5FywtOB5LksjpMhFKXNCkkIpVSWSqYqAVGA4l_wIna2ndq79WoCf65n1BdS1aaBdeK0kTxRNeRqS2TpZuNZ7B5XunJ0Zt9KU6N6gnupelO5F6d6g_jWolwE93SxZ5DMot-CfshC4WAe-bQ2rfw_Wk9FTXwU-WvPWz2G55Y371DL8Xei3yVgPh3cpnzy_6yH_AeDcjXo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>963491737</pqid></control><display><type>article</type><title>A simple prediction score for kidney disease in the Korean population</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><creator>KWON, KEUN-SANG ; BANG, HEEJUNG ; BOMBACK, ANDREW S ; KOH, DAI-HA ; YUM, JUNG-HO ; LEE, JU-HYUNG ; LEE, SIK ; PARK, SUNG K ; YOO, KEUN-YOUNG ; PARK, SUE K ; CHANG, SOUNG-HOON ; LIM, HYUN-SUL ; CHOI, JOONG MYUNG ; KSHIRSAGAR, ABHIJIT V</creator><creatorcontrib>KWON, KEUN-SANG ; BANG, HEEJUNG ; BOMBACK, ANDREW S ; KOH, DAI-HA ; YUM, JUNG-HO ; LEE, JU-HYUNG ; LEE, SIK ; PARK, SUNG K ; YOO, KEUN-YOUNG ; PARK, SUE K ; CHANG, SOUNG-HOON ; LIM, HYUN-SUL ; CHOI, JOONG MYUNG ; KSHIRSAGAR, ABHIJIT V</creatorcontrib><description>Aim: Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed.
Methods: Using simple, non‐invasive questions, we developed a prediction model for chronic kidney disease from national population samples in Korea. The Korean National Health and Nutrition Examination Survey (n = 6565) was used for model development while validation was performed in two independent population samples, internal (n = 2921) and external datasets (n = 8166). Chronic kidney disease was defined as glomerular filtration rate < 60 mL/min per 1.73 m2.
Results: Seven factors – age, female gender, anaemia, hypertension, diabetes mellitus, cardiovascular disease and proteinuria – were significantly associated with prevalent chronic kidney disease. Integer scores were assigned to variables based on the magnitude of associations: 2 for age 50–59 years, 3 for age 60–69 years and 4 for age 70 years or older, and 1 for female gender, anaemia, hypertension, diabetes, proteinuria and cardiovascular disease. Based on the Youden index, a value of 4 or greater defined a high risk population with sensitivity 89%, specificity 71%, and positive predictive value 19%, and negative predictive value 99%. The area under the curve was 0.83 for the development set, and 0.87 and 0.78 in the two validation datasets.
Conclusion: This prediction algorithm, weighted towards common non‐invasive variables, had good performance characteristics in an Asian population, and provides new evidence of the similarity of the algorithms for Western and Eastern populations.
Algorithms that predict the presence of CKD in specific populations facilitate patient screening particularly in primary care. These investigators have developed and validated an algorithm for predicting CKD in the Korean population.</description><identifier>ISSN: 1320-5358</identifier><identifier>EISSN: 1440-1797</identifier><identifier>DOI: 10.1111/j.1440-1797.2011.01552.x</identifier><identifier>PMID: 22171932</identifier><language>eng</language><publisher>Melbourne, Australia: Blackwell Publishing Asia</publisher><subject>Aged ; Algorithms ; Asian ; Chronic Disease ; chronic kidney disease ; epidemiology ; Female ; Humans ; Kidney Diseases - diagnosis ; Kidney Diseases - etiology ; Korea ; Logistic Models ; Male ; Middle Aged ; public health ; Risk Factors ; screening</subject><ispartof>Nephrology (Carlton, Vic.), 2012-03, Vol.17 (3), p.278-284</ispartof><rights>2011 The Authors. Nephrology © 2011 Asian Pacific Society of Nephrology</rights><rights>2011 The Authors. Nephrology © 2011 Asian Pacific Society of Nephrology.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4062-f80f9ed5406a8889cb45b3527c3bb6da565b5d6b004c999f4629f0e69ea3363</citedby><cites>FETCH-LOGICAL-c4062-f80f9ed5406a8889cb45b3527c3bb6da565b5d6b004c999f4629f0e69ea3363</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1440-1797.2011.01552.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1440-1797.2011.01552.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22171932$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>KWON, KEUN-SANG</creatorcontrib><creatorcontrib>BANG, HEEJUNG</creatorcontrib><creatorcontrib>BOMBACK, ANDREW S</creatorcontrib><creatorcontrib>KOH, DAI-HA</creatorcontrib><creatorcontrib>YUM, JUNG-HO</creatorcontrib><creatorcontrib>LEE, JU-HYUNG</creatorcontrib><creatorcontrib>LEE, SIK</creatorcontrib><creatorcontrib>PARK, SUNG K</creatorcontrib><creatorcontrib>YOO, KEUN-YOUNG</creatorcontrib><creatorcontrib>PARK, SUE K</creatorcontrib><creatorcontrib>CHANG, SOUNG-HOON</creatorcontrib><creatorcontrib>LIM, HYUN-SUL</creatorcontrib><creatorcontrib>CHOI, JOONG MYUNG</creatorcontrib><creatorcontrib>KSHIRSAGAR, ABHIJIT V</creatorcontrib><title>A simple prediction score for kidney disease in the Korean population</title><title>Nephrology (Carlton, Vic.)</title><addtitle>Nephrology (Carlton)</addtitle><description>Aim: Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed.
Methods: Using simple, non‐invasive questions, we developed a prediction model for chronic kidney disease from national population samples in Korea. The Korean National Health and Nutrition Examination Survey (n = 6565) was used for model development while validation was performed in two independent population samples, internal (n = 2921) and external datasets (n = 8166). Chronic kidney disease was defined as glomerular filtration rate < 60 mL/min per 1.73 m2.
Results: Seven factors – age, female gender, anaemia, hypertension, diabetes mellitus, cardiovascular disease and proteinuria – were significantly associated with prevalent chronic kidney disease. Integer scores were assigned to variables based on the magnitude of associations: 2 for age 50–59 years, 3 for age 60–69 years and 4 for age 70 years or older, and 1 for female gender, anaemia, hypertension, diabetes, proteinuria and cardiovascular disease. Based on the Youden index, a value of 4 or greater defined a high risk population with sensitivity 89%, specificity 71%, and positive predictive value 19%, and negative predictive value 99%. The area under the curve was 0.83 for the development set, and 0.87 and 0.78 in the two validation datasets.
Conclusion: This prediction algorithm, weighted towards common non‐invasive variables, had good performance characteristics in an Asian population, and provides new evidence of the similarity of the algorithms for Western and Eastern populations.
Algorithms that predict the presence of CKD in specific populations facilitate patient screening particularly in primary care. These investigators have developed and validated an algorithm for predicting CKD in the Korean population.</description><subject>Aged</subject><subject>Algorithms</subject><subject>Asian</subject><subject>Chronic Disease</subject><subject>chronic kidney disease</subject><subject>epidemiology</subject><subject>Female</subject><subject>Humans</subject><subject>Kidney Diseases - diagnosis</subject><subject>Kidney Diseases - etiology</subject><subject>Korea</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Middle Aged</subject><subject>public health</subject><subject>Risk Factors</subject><subject>screening</subject><issn>1320-5358</issn><issn>1440-1797</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkMtOwzAQRS0Eorx-AXnHKsGP2IkXLBAt5aWCAAmJjeUkE-GSJsFuRfv3OLR0jTcea-6ZsQ5CmJKYhnM-jWmSkIimKo0ZoTQmVAgWL3fQwbaxG2rOSCS4yAbo0PspITRlku6jAWM0pYqzAzS6xN7Ouhpw56C0xdy2DfZF6wBXrcOftmxghUvrwXjAtsHzD8D3oW0a3LXdojY9cYz2KlN7ONncR-jlevR6dRM9PI5vry4foiIhkkVVRioFpQgPk2WZKvJE5FywtOB5LksjpMhFKXNCkkIpVSWSqYqAVGA4l_wIna2ndq79WoCf65n1BdS1aaBdeK0kTxRNeRqS2TpZuNZ7B5XunJ0Zt9KU6N6gnupelO5F6d6g_jWolwE93SxZ5DMot-CfshC4WAe-bQ2rfw_Wk9FTXwU-WvPWz2G55Y371DL8Xei3yVgPh3cpnzy_6yH_AeDcjXo</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>KWON, KEUN-SANG</creator><creator>BANG, HEEJUNG</creator><creator>BOMBACK, ANDREW S</creator><creator>KOH, DAI-HA</creator><creator>YUM, JUNG-HO</creator><creator>LEE, JU-HYUNG</creator><creator>LEE, SIK</creator><creator>PARK, SUNG K</creator><creator>YOO, KEUN-YOUNG</creator><creator>PARK, SUE K</creator><creator>CHANG, SOUNG-HOON</creator><creator>LIM, HYUN-SUL</creator><creator>CHOI, JOONG MYUNG</creator><creator>KSHIRSAGAR, ABHIJIT V</creator><general>Blackwell Publishing Asia</general><scope>BSCLL</scope><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>7X8</scope></search><sort><creationdate>201203</creationdate><title>A simple prediction score for kidney disease in the Korean population</title><author>KWON, KEUN-SANG ; BANG, HEEJUNG ; BOMBACK, ANDREW S ; KOH, DAI-HA ; YUM, JUNG-HO ; LEE, JU-HYUNG ; LEE, SIK ; PARK, SUNG K ; YOO, KEUN-YOUNG ; PARK, SUE K ; CHANG, SOUNG-HOON ; LIM, HYUN-SUL ; CHOI, JOONG MYUNG ; KSHIRSAGAR, ABHIJIT V</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4062-f80f9ed5406a8889cb45b3527c3bb6da565b5d6b004c999f4629f0e69ea3363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Aged</topic><topic>Algorithms</topic><topic>Asian</topic><topic>Chronic Disease</topic><topic>chronic kidney disease</topic><topic>epidemiology</topic><topic>Female</topic><topic>Humans</topic><topic>Kidney Diseases - diagnosis</topic><topic>Kidney Diseases - etiology</topic><topic>Korea</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Middle Aged</topic><topic>public health</topic><topic>Risk Factors</topic><topic>screening</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>KWON, KEUN-SANG</creatorcontrib><creatorcontrib>BANG, HEEJUNG</creatorcontrib><creatorcontrib>BOMBACK, ANDREW S</creatorcontrib><creatorcontrib>KOH, DAI-HA</creatorcontrib><creatorcontrib>YUM, JUNG-HO</creatorcontrib><creatorcontrib>LEE, JU-HYUNG</creatorcontrib><creatorcontrib>LEE, SIK</creatorcontrib><creatorcontrib>PARK, SUNG K</creatorcontrib><creatorcontrib>YOO, KEUN-YOUNG</creatorcontrib><creatorcontrib>PARK, SUE K</creatorcontrib><creatorcontrib>CHANG, SOUNG-HOON</creatorcontrib><creatorcontrib>LIM, HYUN-SUL</creatorcontrib><creatorcontrib>CHOI, JOONG MYUNG</creatorcontrib><creatorcontrib>KSHIRSAGAR, ABHIJIT V</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Nephrology (Carlton, Vic.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>KWON, KEUN-SANG</au><au>BANG, HEEJUNG</au><au>BOMBACK, ANDREW S</au><au>KOH, DAI-HA</au><au>YUM, JUNG-HO</au><au>LEE, JU-HYUNG</au><au>LEE, SIK</au><au>PARK, SUNG K</au><au>YOO, KEUN-YOUNG</au><au>PARK, SUE K</au><au>CHANG, SOUNG-HOON</au><au>LIM, HYUN-SUL</au><au>CHOI, JOONG MYUNG</au><au>KSHIRSAGAR, ABHIJIT V</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A simple prediction score for kidney disease in the Korean population</atitle><jtitle>Nephrology (Carlton, Vic.)</jtitle><addtitle>Nephrology (Carlton)</addtitle><date>2012-03</date><risdate>2012</risdate><volume>17</volume><issue>3</issue><spage>278</spage><epage>284</epage><pages>278-284</pages><issn>1320-5358</issn><eissn>1440-1797</eissn><abstract>Aim: Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed.
Methods: Using simple, non‐invasive questions, we developed a prediction model for chronic kidney disease from national population samples in Korea. The Korean National Health and Nutrition Examination Survey (n = 6565) was used for model development while validation was performed in two independent population samples, internal (n = 2921) and external datasets (n = 8166). Chronic kidney disease was defined as glomerular filtration rate < 60 mL/min per 1.73 m2.
Results: Seven factors – age, female gender, anaemia, hypertension, diabetes mellitus, cardiovascular disease and proteinuria – were significantly associated with prevalent chronic kidney disease. Integer scores were assigned to variables based on the magnitude of associations: 2 for age 50–59 years, 3 for age 60–69 years and 4 for age 70 years or older, and 1 for female gender, anaemia, hypertension, diabetes, proteinuria and cardiovascular disease. Based on the Youden index, a value of 4 or greater defined a high risk population with sensitivity 89%, specificity 71%, and positive predictive value 19%, and negative predictive value 99%. The area under the curve was 0.83 for the development set, and 0.87 and 0.78 in the two validation datasets.
Conclusion: This prediction algorithm, weighted towards common non‐invasive variables, had good performance characteristics in an Asian population, and provides new evidence of the similarity of the algorithms for Western and Eastern populations.
Algorithms that predict the presence of CKD in specific populations facilitate patient screening particularly in primary care. These investigators have developed and validated an algorithm for predicting CKD in the Korean population.</abstract><cop>Melbourne, Australia</cop><pub>Blackwell Publishing Asia</pub><pmid>22171932</pmid><doi>10.1111/j.1440-1797.2011.01552.x</doi><tpages>7</tpages></addata></record> |
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| source | MEDLINE; Access via Wiley Online Library |
| subjects | Aged Algorithms Asian Chronic Disease chronic kidney disease epidemiology Female Humans Kidney Diseases - diagnosis Kidney Diseases - etiology Korea Logistic Models Male Middle Aged public health Risk Factors screening |
| title | A simple prediction score for kidney disease in the Korean population |
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