Evidence map›Paper›PMID 36397658›Full record

ArticleAlimentary pharmacology & therapeutics2023

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.

Ashwin Dhanda, Keith Bodger, Steve Hood, Clive Henn, Michael Allison, Chioma Amasiatu, Robyn Burton, Matthew Cramp, Ewan Forrest, Meetal Khetani and 8 more

Abstract read
In one paragraph

Article in Alimentary pharmacology & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Ashwin DhandaUniversity of Plymouth, Plymouth, UK.ORCID 0000-0002-0523-0193
Keith BodgerUniversity of Liverpool, Liverpool, UK.ORCID 0000-0002-1825-3239
Steve HoodLiverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
Clive HennAddiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
Michael AllisonCambridge Liver Unit, Cambridge NIHR Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Chioma AmasiatuAddiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
Robyn BurtonAddiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
Matthew CrampUniversity of Plymouth, Plymouth, UK.
Ewan ForrestGlasgow Royal Infirmary, Glasgow, UK.ORCID 0000-0002-7293-2574
Meetal KhetaniAddiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
Alastair MacGilchristLiver Unit, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
Steven MassonLiver Unit, Newcastle Hospitals NHS Trust, Newcastle, UK.ORCID 0000-0003-1041-9844
Richard ParkerLeeds Liver Unit, Leeds Teaching Hospitals NHS Trust, Leeds, UK.ORCID 0000-0003-4888-8670
Nick SheronAddiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
Ken SimpsonLiver Unit, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
Nikhil VergisImperial College London, London, UK.
Martin WhiteAddiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
BASL ARLD SIG National Service Evaluation Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmergency 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.

aimTo deploy the Liverpool ArLD Algorithm (LAA), which accounts for alternative coding patterns (e.g., ArLD secondary diagnosis with alcohol/liver-related primary diagnosis), to national and local datasets in the context of studying trends in ArLD admissions before and during the COVID-19 pandemic.

methodsWe 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.

resultsNationally, LAA identified approximately 100% more monthly emergency admissions from 2013 to 2021 than the standard method. The annual number of ArLD-specific admissions increased by 30.4%. Of 39,667 admissions in 2020/21, only 19,949 were identified with standard approach, an estimated admission cost of £70 million in under-recorded cases. Within 28 local hospital datasets, 233 admissions were identified using the standard approach and a further 250 locally verified cases using the LAA (107% uplift). There was an 18% absolute increase in ArLD admissions in the seven-day evaluation period in 2020 versus 2019. There were no differences in disease severity or mortality, or in the proportion of admissions with decompensation of cirrhosis or alcoholic hepatitis.

conclusionsThe LAA can be applied successfully to local and national datasets. It consistently identifies approximately 100% more cases than the standard coding approach. The algorithm has revealed the true extent of ArLD admissions. The pandemic has compounded a long-term rise in ArLD admissions and mortality.

Indexed as

COVID-19Liver DiseasesAlgorithmsEnglandHospitalizationHospitalsHumansPandemics

Identifiers

PMID36397658
PMCPMC10099257

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.