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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.
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; Vergis, Nikhil; White, Martin.
  • Dhanda A; University of Plymouth, Plymouth, UK.
  • Bodger K; South West Liver Unit, University Hospitals Plymouth NHS Trust, Plymouth, UK.
  • Hood S; University of Liverpool, Liverpool, UK.
  • Henn C; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
  • Allison M; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
  • Amasiatu C; Addiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
  • Burton R; Cambridge Liver Unit, Cambridge NIHR Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
  • Cramp M; Addiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
  • Forrest E; Addiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
  • Khetani M; University of Plymouth, Plymouth, UK.
  • MacGilchrist A; South West Liver Unit, University Hospitals Plymouth NHS Trust, Plymouth, UK.
  • Masson S; Glasgow Royal Infirmary, Glasgow, UK.
  • Parker R; Addiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
  • Sheron N; Liver Unit, Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK.
  • Simpson K; Liver Unit, Newcastle Hospitals NHS Trust, Newcastle, UK.
  • Vergis N; Leeds Liver Unit, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • White M; Addiction and Inclusion Directorate, Office for Health Improvement and Disparities, Department for Health and Social Care, London, UK.
Aliment Pharmacol Ther ; 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2229464
ABSTRACT

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 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.

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 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.

CONCLUSIONS:

The 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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal subject: Pharmacology / Gastroenterology / Drug Therapy Year: 2022 Document Type: Article Affiliation country: Apt.17307

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal subject: Pharmacology / Gastroenterology / Drug Therapy Year: 2022 Document Type: Article Affiliation country: Apt.17307