Liver disease causes more and more deaths in the UK. There are several reasons why the disease can develop including viral infections, heavy alcohol use, being very overweight, or some reactions to medicines. Receiving a new liver through a transplant can help treat serious disease but a new liver can be found only for less than 5% of seriously ill patients and most of these patients will never see a liver transplant doctor. Liver disease can be prevented and blood tests can help doctors identify patients at risk. There are, however, many high-risk patients who cannot be identified based on these tests.
We wish to understand which patients with abnormal liver blood tests develop severe liver disease. We also wish to understand how to identify those high-risk patients who are not picked up using liver blood tests. The results of this study may help to find people with early liver disease before they develop severe disease. This will leave more time for preventive treatments such as medicines or lifestyle changes to work and result in less people developing severe liver disease.
Cox proportional hazard regression will be used to identify predictors associated with liver disease outcomes in a cohort of patients with abnormal liver function blood tests (LFTs). By calculating the risk of developing liver disease outcomes according to a range of risk factors, we plan to build a predictive model to identify people at risk. Within the cohort, we will use self-controlled case series design to determine whether, after an episode of a common infectious illness (e.g. urinary tract infection), patients are temporarily at an increased risk of liver-related hospitalisation. Using conditional Poisson regression, we will estimate the incidence ratios (risk) of hospitalisations during each such risk period compared to hospitalisations during other time periods.
Using a case-control design, we aim to understand how to identify patients who, regardless of normal LFTs, develop cirrhosis. Cases will be those who develop cirrhosis despite prior normal LFT results. Controls will be patients without cirrhosis or abnormal LFTS (selected using Incidence Density Sampling). Multiple logistic regression will be used to identify predictors of cirrhosis in patients with normal LFTs. Finally, in a further case control analysis, we will identify risk factors for cirrhosis in those who have no LFT results.
Cirrhosis of the liver
- Hepatic encephalopathy
- Variceal banding
- Portal hypertension
- Alcoholic Hepatitis
- Endoscopic sclerotherapy
- Oesophageal/Gastric varices
- Variceal bleeding
- Ascites
- Jaundice
- Acute on Chronic Liver Failure
- Spontaneous bacterial peritonitis
- Bacteraemia/sepsis
- Primary liver cancer
- Transjugular intrahepatic portosystemic shunt
- Liver transplant
- Hospitalisation for liver disease
- Liver disease-related death
Andrew Hayward - Chief Investigator - University College London ( UCL )
Suvi Harmala - Corresponding Applicant - University College London ( UCL )
Alastair O'Brien - Collaborator - University College London ( UCL )
Laura Shallcross - Collaborator - University College London ( UCL )
Spiros Denaxas - Collaborator - University College London ( UCL )
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation