Conference Paper/Proceeding/Abstract 23003 views 137 downloads
Defining Acute Kidney Injury Episodes
International Journal of Population Data Science, Volume: 4, Issue: 3
Swansea University Authors: Gareth Davies , Ashley Akbari , Ronan Lyons
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DOI (Published version): 10.23889/ijpds.v4i3.1251
Abstract
BackgroundAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.Main AimTo describe four different potential definitions of...
Published in: | International Journal of Population Data Science |
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ISSN: | 2399-4908 |
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Swansea University
2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa53659 |
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<?xml version="1.0"?><rfc1807><datestamp>2022-03-21T13:12:26.6229532</datestamp><bib-version>v2</bib-version><id>53659</id><entry>2019-11-19</entry><title>Defining Acute Kidney Injury Episodes</title><swanseaauthors><author><sid>98490239b86cc892a382416d048cdb3c</sid><ORCID>0000-0001-9005-1618</ORCID><firstname>Gareth</firstname><surname>Davies</surname><name>Gareth Davies</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>aa1b025ec0243f708bb5eb0a93d6fb52</sid><ORCID>0000-0003-0814-0801</ORCID><firstname>Ashley</firstname><surname>Akbari</surname><name>Ashley Akbari</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>83efcf2a9dfcf8b55586999d3d152ac6</sid><ORCID>0000-0001-5225-000X</ORCID><firstname>Ronan</firstname><surname>Lyons</surname><name>Ronan Lyons</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2019-11-19</date><deptcode>MEDS</deptcode><abstract>BackgroundAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.Main AimTo describe four different potential definitions of an AKI episode.MethodWe identified AKI using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person’s index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine.ResultsThere were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,505, 33,759, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively.ConclusionAKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis.</abstract><type>Conference Paper/Proceeding/Abstract</type><journal>International Journal of Population Data Science</journal><volume>4</volume><journalNumber>3</journalNumber><paginationStart/><paginationEnd/><publisher>Swansea University</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2399-4908</issnElectronic><keywords/><publishedDay>19</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2019</publishedYear><publishedDate>2019-11-19</publishedDate><doi>10.23889/ijpds.v4i3.1251</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2022-03-21T13:12:26.6229532</lastEdited><Created>2019-11-19T00:00:00.0000000</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Gareth</firstname><surname>Davies</surname><orcid>0000-0001-9005-1618</orcid><order>1</order></author><author><firstname>Timothy</firstname><surname>Scale</surname><order>2</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>3</order></author><author><firstname>James</firstname><surname>Chess</surname><order>4</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>5</order></author></authors><documents><document><filename>53659__16947__dee47d543151495aa3b08603cc564648.pdf</filename><originalFilename>53659.pdf</originalFilename><uploaded>2020-03-27T10:15:17.7651541</uploaded><type>Output</type><contentLength>211369</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Released under the terms of a Creative Commons Attribution 4.0 International License (CC-BY).</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
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2022-03-21T13:12:26.6229532 v2 53659 2019-11-19 Defining Acute Kidney Injury Episodes 98490239b86cc892a382416d048cdb3c 0000-0001-9005-1618 Gareth Davies Gareth Davies true false aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2019-11-19 MEDS BackgroundAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.Main AimTo describe four different potential definitions of an AKI episode.MethodWe identified AKI using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person’s index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine.ResultsThere were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,505, 33,759, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively.ConclusionAKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis. Conference Paper/Proceeding/Abstract International Journal of Population Data Science 4 3 Swansea University 2399-4908 19 11 2019 2019-11-19 10.23889/ijpds.v4i3.1251 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University 2022-03-21T13:12:26.6229532 2019-11-19T00:00:00.0000000 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Gareth Davies 0000-0001-9005-1618 1 Timothy Scale 2 Ashley Akbari 0000-0003-0814-0801 3 James Chess 4 Ronan Lyons 0000-0001-5225-000X 5 53659__16947__dee47d543151495aa3b08603cc564648.pdf 53659.pdf 2020-03-27T10:15:17.7651541 Output 211369 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution 4.0 International License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/ |
title |
Defining Acute Kidney Injury Episodes |
spellingShingle |
Defining Acute Kidney Injury Episodes Gareth Davies Ashley Akbari Ronan Lyons |
title_short |
Defining Acute Kidney Injury Episodes |
title_full |
Defining Acute Kidney Injury Episodes |
title_fullStr |
Defining Acute Kidney Injury Episodes |
title_full_unstemmed |
Defining Acute Kidney Injury Episodes |
title_sort |
Defining Acute Kidney Injury Episodes |
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98490239b86cc892a382416d048cdb3c_***_Gareth Davies aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari 83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons |
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Gareth Davies Ashley Akbari Ronan Lyons |
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Gareth Davies Timothy Scale Ashley Akbari James Chess Ronan Lyons |
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International Journal of Population Data Science |
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BackgroundAcute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode.Main AimTo describe four different potential definitions of an AKI episode.MethodWe identified AKI using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person’s index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine.ResultsThere were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,505, 33,759, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively.ConclusionAKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis. |
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2019-11-19T20:00:23Z |
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