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http://hdl.handle.net/1893/37018
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DC Field | Value | Language |
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dc.contributor.author | Dhanda, Ashwin | en_UK |
dc.contributor.author | Bodger, Keith | en_UK |
dc.contributor.author | Hood, Steve | en_UK |
dc.contributor.author | Henn, Clive | en_UK |
dc.contributor.author | Allison, Michael | en_UK |
dc.contributor.author | Amasiatu, Chioma | en_UK |
dc.contributor.author | Burton, Robyn | en_UK |
dc.contributor.author | Cramp, Matthew | en_UK |
dc.contributor.author | Forrest, Ewan | en_UK |
dc.contributor.author | Khetani, Meetal | en_UK |
dc.contributor.author | MacGilchrist, Alastair | en_UK |
dc.contributor.author | Masson, Steven | en_UK |
dc.contributor.author | Parker, Richard | en_UK |
dc.contributor.author | Sheron, Nick | en_UK |
dc.contributor.author | Simpson, Ken | en_UK |
dc.date.accessioned | 2025-04-30T00:00:48Z | - |
dc.date.available | 2025-04-30T00:00:48Z | - |
dc.date.issued | 2023-02 | en_UK |
dc.identifier.uri | http://hdl.handle.net/1893/37018 | - |
dc.description.abstract | Background: 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 cases | en_UK |
dc.language.iso | en | en_UK |
dc.publisher | Wiley | en_UK |
dc.relation | Dhanda 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.17307 | en_UK |
dc.rights | This 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.uri | http://creativecommons.org/licenses/by-nc/4.0/ | en_UK |
dc.title | 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 | en_UK |
dc.type | Journal Article | en_UK |
dc.identifier.doi | 10.1111/apt.17307 | en_UK |
dc.identifier.pmid | 36397658 | en_UK |
dc.citation.jtitle | Alimentary Pharmacology and Therapeutics | en_UK |
dc.citation.issn | 1365-2036 | en_UK |
dc.citation.issn | 0269-2813 | en_UK |
dc.citation.volume | 57 | en_UK |
dc.citation.issue | 4 | en_UK |
dc.citation.spage | 368 | en_UK |
dc.citation.epage | 377 | en_UK |
dc.citation.publicationstatus | Published | en_UK |
dc.citation.peerreviewed | Refereed | en_UK |
dc.type.status | VoR - Version of Record | en_UK |
dc.contributor.funder | Department of Health | en_UK |
dc.author.email | robyn.burton@stir.ac.uk | en_UK |
dc.citation.date | 17/11/2022 | en_UK |
dc.citation.isbn | 1365-2036 | en_UK |
dc.contributor.affiliation | University of Plymouth | en_UK |
dc.contributor.affiliation | University of Liverpool | en_UK |
dc.contributor.affiliation | University of Liverpool | en_UK |
dc.contributor.affiliation | Department of Health and Social Care | en_UK |
dc.contributor.affiliation | Cambridge University Hospitals NHS | en_UK |
dc.contributor.affiliation | Department of Health and Social Care | en_UK |
dc.contributor.affiliation | Institute for Social Marketing | en_UK |
dc.contributor.affiliation | University of Plymouth | en_UK |
dc.contributor.affiliation | Glasgow Royal Infirmary | en_UK |
dc.contributor.affiliation | Department of Health and Social Care | en_UK |
dc.contributor.affiliation | Royal Infirmary of Edinburgh | en_UK |
dc.contributor.affiliation | Newcastle upon Tyne Hospitals NHS Foundation Trust | en_UK |
dc.contributor.affiliation | Leeds Teaching Hospitals NHS Trust | en_UK |
dc.contributor.affiliation | Department of Health and Social Care | en_UK |
dc.contributor.affiliation | Newcastle upon Tyne Hospitals NHS Foundation Trust | en_UK |
dc.identifier.isi | www.webofscience.com/wos/woscc/summary/23083d7b-7d1d-45da-8cb3-acf33d9a302e-015e3501aa/relevance/1 | en_UK |
dc.identifier.scopusid | 2-s2.0-85143534498&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=DOI%2810.1111%2Fapt.17307%29&sessionSearchId=12e9cdca58082c9124edb02cb6b0be04 | en_UK |
dc.identifier.wtid | 2025825 | en_UK |
dc.contributor.orcid | 0000-0002-0523-0193 | en_UK |
dc.contributor.orcid | 0000-0002-1825-3239 | en_UK |
dc.contributor.orcid | 0000-0003-1684-5238 | en_UK |
dc.contributor.orcid | 0000-0002-7293-2574 | en_UK |
dc.contributor.orcid | 0000-0003-1041-9844 | en_UK |
dc.contributor.orcid | 0000-0003-4888-8670 | en_UK |
dc.date.accepted | 2022-11-03 | en_UK |
dcterms.dateAccepted | 2022-11-03 | en_UK |
dc.date.filedepositdate | 2025-04-25 | en_UK |
dc.subject.tag | COVID-19 | en_UK |
rioxxterms.version | VoR | en_UK |
local.rioxx.author | Dhanda, Ashwin|0000-0002-0523-0193 | en_UK |
local.rioxx.author | Bodger, Keith|0000-0002-1825-3239 | en_UK |
local.rioxx.author | Hood, Steve| | en_UK |
local.rioxx.author | Henn, Clive| | en_UK |
local.rioxx.author | Allison, Michael| | en_UK |
local.rioxx.author | Amasiatu, Chioma| | en_UK |
local.rioxx.author | Burton, Robyn|0000-0003-1684-5238 | en_UK |
local.rioxx.author | Cramp, Matthew| | en_UK |
local.rioxx.author | Forrest, Ewan|0000-0002-7293-2574 | en_UK |
local.rioxx.author | Khetani, Meetal| | en_UK |
local.rioxx.author | MacGilchrist, Alastair| | en_UK |
local.rioxx.author | Masson, Steven|0000-0003-1041-9844 | en_UK |
local.rioxx.author | Parker, Richard|0000-0003-4888-8670 | en_UK |
local.rioxx.author | Sheron, Nick| | en_UK |
local.rioxx.author | Simpson, Ken| | en_UK |
local.rioxx.project | Project ID unknown|Department of Health| | en_UK |
local.rioxx.freetoreaddate | 2025-04-27 | en_UK |
local.rioxx.licence | http://creativecommons.org/licenses/by-nc/4.0/|2025-04-27| | en_UK |
local.rioxx.filename | Aliment Pharmacol Ther - 2022 - Dhanda - The Liverpool alcohol___related liver disease algorithm identifies twice as many.pdf | en_UK |
local.rioxx.filecount | 1 | en_UK |
local.rioxx.source | 1365-2036 | en_UK |
Appears in Collections: | Faculty of Health Sciences and Sport Journal Articles |
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Aliment Pharmacol Ther - 2022 - Dhanda - The Liverpool alcohol___related liver disease algorithm identifies twice as many.pdf | Fulltext - Published Version | 963.3 kB | Adobe PDF | View/Open |
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