Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/37018
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dc.contributor.authorDhanda, Ashwinen_UK
dc.contributor.authorBodger, Keithen_UK
dc.contributor.authorHood, Steveen_UK
dc.contributor.authorHenn, Cliveen_UK
dc.contributor.authorAllison, Michaelen_UK
dc.contributor.authorAmasiatu, Chiomaen_UK
dc.contributor.authorBurton, Robynen_UK
dc.contributor.authorCramp, Matthewen_UK
dc.contributor.authorForrest, Ewanen_UK
dc.contributor.authorKhetani, Meetalen_UK
dc.contributor.authorMacGilchrist, Alastairen_UK
dc.contributor.authorMasson, Stevenen_UK
dc.contributor.authorParker, Richarden_UK
dc.contributor.authorSheron, Nicken_UK
dc.contributor.authorSimpson, Kenen_UK
dc.date.accessioned2025-04-30T00:00:48Z-
dc.date.available2025-04-30T00:00:48Z-
dc.date.issued2023-02en_UK
dc.identifier.urihttp://hdl.handle.net/1893/37018-
dc.description.abstractBackground: Emergency admissions in England for alcohol-related liver disease(ArLD) have increased steadily for decades. Statistics based on administrative datatypically focus on the ArLD-specific code as the primary diagnosis and are thereforeat risk of excluding ArLD admissions defined by other coding combinations.Aim: To deploy the Liverpool ArLD Algorithm (LAA), which accounts for alternativecoding patterns (e.g., ArLD secondary diagnosis with alcohol/liver-related primarydiagnosis), to national and local datasets in the context of studying trends in ArLDadmissions before and during the COVID-19 pandemic.Methods: We applied the standard approach and LAA to Hospital Episode Statisticsfor England (2013–21). The algorithm was also deployed at 28 hospitals to dischargecoding for emergency admissions during a common 7-day period in 2019 and 2020,in which eligible patient records were reviewed manually to verify the diagnosis andextract data.Results: Nationally, LAA identified approximately 100% more monthly emergencyadmissions from 2013 to 2021 than the standard method. The annual number ofArLD-specific admissions increased by 30.4%. Of 39,667 admissions in 2020/21, only19,949 were identified with standard approach, an estimated admission cost of £70million in under-recorded cases. Within 28 local hospital datasets, 233 admissions were identified using the standard approach and a further 250 locally verified cases Background: Emergency admissions in England for alcohol-related liver disease(ArLD) have increased steadily for decades. Statistics based on administrative data typically focus on the ArLD-specific code as the primary diagnosis and are therefore at risk of excluding ArLD admissions defined by other coding combinations.Aim: To deploy the Liverpool ArLD Algorithm (LAA), which accounts for alternativecoding patterns (e.g., ArLD secondary diagnosis with alcohol/liver-related primary diagnosis), to national and local datasets in the context of studying trends in ArLDadmissions before and during the COVID-19 pandemic.Methods: We applied the standard approach and LAA to Hospital Episode Statistics for England (2013–21). The algorithm was also deployed at 28 hospitals to discharge coding for emergency admissions during a common 7-day period in 2019 and 2020,in which eligible patient records were reviewed manually to verify the diagnosis and extract data.Results: Nationally, LAA identified approximately 100% more monthly emergency admissions from 2013 to 2021 than the standard method. The annual number f ArLD-specific admissions increased by 30.4%. Of 39,667 admissions in 2020/21, only19,949 were identified with standard approach, an estimated admission cost of £70million in under-recorded cases. Within 28 local hospital datasets, 233 admissions were identified using the standard approach and a further 250 locally verified casesen_UK
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.relationDhanda A, Bodger K, Hood S, Henn C, Allison M, Amasiatu C, Burton R, Cramp M, Forrest E, Khetani M, MacGilchrist A, Masson S, Parker R, Sheron N & Simpson K (2023) The Liverpool alcohol-related liver disease algorithm identifies twice as many emergency admissions compared to standard methods when applied to Hospital Episode Statistics for England. <i>Alimentary Pharmacology & Therapeutics</i>, 57 (4), pp. 368-377. https://doi.org/10.1111/apt.17307en_UK
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproductionin any medium, provided the original work is properly cited and is not used for commercial purposes.© 2022 The Authors. Alimentary Pharmacology & Therapeutics published by John Wiley & Sons Ltd.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en_UK
dc.titleThe Liverpool alcohol-related liver disease algorithm identifies twice as many emergency admissions compared to standard methods when applied to Hospital Episode Statistics for Englanden_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1111/apt.17307en_UK
dc.identifier.pmid36397658en_UK
dc.citation.jtitleAlimentary Pharmacology and Therapeuticsen_UK
dc.citation.issn1365-2036en_UK
dc.citation.issn0269-2813en_UK
dc.citation.volume57en_UK
dc.citation.issue4en_UK
dc.citation.spage368en_UK
dc.citation.epage377en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderDepartment of Healthen_UK
dc.author.emailrobyn.burton@stir.ac.uken_UK
dc.citation.date17/11/2022en_UK
dc.citation.isbn1365-2036en_UK
dc.contributor.affiliationUniversity of Plymouthen_UK
dc.contributor.affiliationUniversity of Liverpoolen_UK
dc.contributor.affiliationUniversity of Liverpoolen_UK
dc.contributor.affiliationDepartment of Health and Social Careen_UK
dc.contributor.affiliationCambridge University Hospitals NHSen_UK
dc.contributor.affiliationDepartment of Health and Social Careen_UK
dc.contributor.affiliationInstitute for Social Marketingen_UK
dc.contributor.affiliationUniversity of Plymouthen_UK
dc.contributor.affiliationGlasgow Royal Infirmaryen_UK
dc.contributor.affiliationDepartment of Health and Social Careen_UK
dc.contributor.affiliationRoyal Infirmary of Edinburghen_UK
dc.contributor.affiliationNewcastle upon Tyne Hospitals NHS Foundation Trusten_UK
dc.contributor.affiliationLeeds Teaching Hospitals NHS Trusten_UK
dc.contributor.affiliationDepartment of Health and Social Careen_UK
dc.contributor.affiliationNewcastle upon Tyne Hospitals NHS Foundation Trusten_UK
dc.identifier.isiwww.webofscience.com/wos/woscc/summary/23083d7b-7d1d-45da-8cb3-acf33d9a302e-015e3501aa/relevance/1en_UK
dc.identifier.scopusid2-s2.0-85143534498&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=DOI%2810.1111%2Fapt.17307%29&sessionSearchId=12e9cdca58082c9124edb02cb6b0be04en_UK
dc.identifier.wtid2025825en_UK
dc.contributor.orcid0000-0002-0523-0193en_UK
dc.contributor.orcid0000-0002-1825-3239en_UK
dc.contributor.orcid0000-0003-1684-5238en_UK
dc.contributor.orcid0000-0002-7293-2574en_UK
dc.contributor.orcid0000-0003-1041-9844en_UK
dc.contributor.orcid0000-0003-4888-8670en_UK
dc.date.accepted2022-11-03en_UK
dcterms.dateAccepted2022-11-03en_UK
dc.date.filedepositdate2025-04-25en_UK
dc.subject.tagCOVID-19en_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorDhanda, Ashwin|0000-0002-0523-0193en_UK
local.rioxx.authorBodger, Keith|0000-0002-1825-3239en_UK
local.rioxx.authorHood, Steve|en_UK
local.rioxx.authorHenn, Clive|en_UK
local.rioxx.authorAllison, Michael|en_UK
local.rioxx.authorAmasiatu, Chioma|en_UK
local.rioxx.authorBurton, Robyn|0000-0003-1684-5238en_UK
local.rioxx.authorCramp, Matthew|en_UK
local.rioxx.authorForrest, Ewan|0000-0002-7293-2574en_UK
local.rioxx.authorKhetani, Meetal|en_UK
local.rioxx.authorMacGilchrist, Alastair|en_UK
local.rioxx.authorMasson, Steven|0000-0003-1041-9844en_UK
local.rioxx.authorParker, Richard|0000-0003-4888-8670en_UK
local.rioxx.authorSheron, Nick|en_UK
local.rioxx.authorSimpson, Ken|en_UK
local.rioxx.projectProject ID unknown|Department of Health|en_UK
local.rioxx.freetoreaddate2025-04-27en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by-nc/4.0/|2025-04-27|en_UK
local.rioxx.filenameAliment Pharmacol Ther - 2022 - Dhanda - The Liverpool alcohol___related liver disease algorithm identifies twice as many.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1365-2036en_UK
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