lancet-header
Preprints with The Lancet is part of SSRN´s First Look, a place where journals and other research experts identify content of interest prior to publication. These preprint papers are not peer-reviewed and are posted here as part of a 12-month trial. Authors have either opted in at submission to The Lancet family of journals to post their preprints on Preprints with The Lancet, or submitted directly via SSRN. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. These papers should not be used for clinical decision making or reporting of research to a lay audience without indicating that this is preliminary research that has not been peer-reviewed. For more information see the Comment published in The Lancet, or visit The Lancet´s FAQ page, and for any feedback please contact preprints@lancet.com

Intelligent Liver Function Testing (iLFT): A Trial of Reflex Testing, Automated Diagnosis and Staging of Liver Disease

63 Pages Posted: 15 Sep 2018

See all articles by John F Dillon

John F Dillon

University of Dundee

Dr Michael H Miller

University of Dundee

Dr Emma M Robinson

University of Dundee - Ninewells Hospital and Medical school

Dr Adrian Hapca

University of Dundee

Dr Mohsen Rezaeihemami

University of Glasgow - Institute of Health and Wellbeing

Dr Paul G McIntyre

University of Dundee

Dr Bill Bartlett

University of Dundee

Peter T Donnan

University of Dundee

Dr Kathleen A Boyd

University of Glasgow - Institute of Health and Wellbeing

Dr Ellie Dow

University of Dundee

More...

Abstract

Background: Liver function tests (LFTs) are frequently requested blood tests which may indicate liver disease. LFTs are commonly abnormal, the causes of which can be complex and frequently under investigated. This can lead to missed opportunities to diagnose and treat liver disease at an early stage. We developed an automated investigation algorithm, which would maximise early diagnosis of liver related diseases. Our aim was to determine whether this new pathway of care, Intelligent Liver Function testing (iLFT) increased diagnosis of liver disease and was cost-effective.

Methods: By adjusting the current laboratory test order and communications systems we were able to produce an automated system to further analyse abnormal LFTs on initial testing samples. We integrated an automated investigation algorithm into the laboratory management system, based on minimal diagnostic criteria, liver fibrosis estimation, and reflex testing for causes of liver disease. This algorithm then generated a diagnosis and/or management plan. A stepped-wedged trial design was utilised to compare LFT outcomes in General Practices in the 6 months before and after introduction of the iLFT system. Diagnostic outcomes were collated and compared.

Findings: Using iLFT, the diagnosis of liver disease was increased by 43%. It was cost-effective with a low initial incremental cost-effectiveness ratio (ICER) of £284 per correct diagnosis, and a saving to the NHS of £3,216 per patient lifetime.

Interpretation: iLFT increases liver diagnosis, improves quality of care, and is highly cost-effective. This can be achieved with minor changes to working practices and exploitation of functionality existing within modern laboratory diagnostics systems.

Funding: Chief Scientist Office, Scottish Government.

Declaration of Interest: None declared.

Ethical Approval: The study was approved by the East of Scotland Research Ethics committee.

Suggested Citation

Dillon, John F and Miller, Michael H and Robinson, Dr Emma M and Hapca, Adrian and Rezaeihemami, Mohsen and McIntyre, Paul G and Bartlett, Bill and Donnan, Peter T and Boyd, Kathleen A and Dow, Ellie, Intelligent Liver Function Testing (iLFT): A Trial of Reflex Testing, Automated Diagnosis and Staging of Liver Disease (March 8, 2018). Available at SSRN: https://ssrn.com/abstract=3228236

John F Dillon (Contact Author)

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Michael H Miller

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Dr Emma M Robinson

University of Dundee - Ninewells Hospital and Medical school ( email )

Dundee
United Kingdom

Adrian Hapca

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Mohsen Rezaeihemami

University of Glasgow - Institute of Health and Wellbeing

Glasgow, Scotland G12 8LE
United Kingdom

Paul G McIntyre

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Bill Bartlett

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Peter T Donnan

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Kathleen A Boyd

University of Glasgow - Institute of Health and Wellbeing

Glasgow, Scotland G12 8LE
United Kingdom

Ellie Dow

University of Dundee

Dundee, Scotland DD1 4HN
United Kingdom

Click here to go to TheLancet.com

Go to TheLancet.com

Paper statistics

Abstract Views
86
Downloads
16