Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/37018
Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
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
Author(s): Dhanda, Ashwin
Bodger, Keith
Hood, Steve
Henn, Clive
Allison, Michael
Amasiatu, Chioma
Burton, Robyn
Cramp, Matthew
Forrest, Ewan
Khetani, Meetal
MacGilchrist, Alastair
Masson, Steven
Parker, Richard
Sheron, Nick
Simpson, Ken
Contact Email: robyn.burton@stir.ac.uk
Issue Date: Feb-2023
Date Deposited: 25-Apr-2025
Citation: 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
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
DOI Link: 10.1111/apt.17307
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.
Licence URL(s): http://creativecommons.org/licenses/by-nc/4.0/

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