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

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.