John F Dillon1, Michael H Miller1, Emma M Robinson2, Adrian Hapca3, Mohsen Rezaeihemami4, Christopher Weatherburn5, Paul G McIntyre6, Bill Bartlett7, Peter T Donnan3, Kathleen A Boyd4, Ellie Dow7. 1. Division of Clinical and Molecular Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK. 2. Division of Clinical and Molecular Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK. Electronic address: e.robinson7@nhs.net. 3. Dundee Epidemiology and Biostatistics Unit, University of Dundee, Dundee, UK. 4. Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK. 5. Dundee Health and Social Care Partnership, Dundee, UK. 6. Department of Microbiology and Virology, Ninewells Hospital and Medical School, Dundee, UK. 7. Department of Clinical Sciences, Ninewells Hospital and Medical School, Dundee, UK.
Abstract
BACKGROUND & AIMS: 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 are 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, intelligent liver function testing (iLFT), with the aim of increasing the early diagnosis of liver disease in a cost-effective manner. METHODS: We developed an automated system that further investigated abnormal LFTs on initial testing samples to generate a probable diagnosis and management plan. We integrated this 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. RESULTS: Of eligible patients with abnormal LFTs, 490 were recruited to the control group and 64 were recruited to the intervention group. The primary diagnostic outcome was based on the general practitioner diagnosis, which agreed with the iLFT diagnosis in 67% of cases. In the iLFT group, the diagnosis of liver disease was increased by 43%. Additionally, there were significant increases in the rates of GP visits after diagnosis and the number of referrals to secondary care in the iLFT group. iLFT was cost-effective with a low initial incremental cost-effectiveness ratio of £284 per correct diagnosis, and a saving to the NHS of £3,216 per patient lifetime. CONCLUSIONS:iLFT increases liver disease diagnoses, 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. LAY SUMMARY: There is a growing epidemic of advanced liver disease, this could be offset by early detection and management. Checking liver blood tests (LFTs) should be an opportunity to diagnose liver problems, but abnormal results are often incompletely investigated. In this study we were able to substantially increase the diagnostic yield of the abnormal LFTs using the automated intelligent LFT system. With the addition of referral recommendations and management plans, this strategy provides optimum investigation and management of LFTs and is cost saving to the NHS.
RCT Entities:
BACKGROUND & AIMS: 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 are 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, intelligent liver function testing (iLFT), with the aim of increasing the early diagnosis of liver disease in a cost-effective manner. METHODS: We developed an automated system that further investigated abnormal LFTs on initial testing samples to generate a probable diagnosis and management plan. We integrated this 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. RESULTS: Of eligible patients with abnormal LFTs, 490 were recruited to the control group and 64 were recruited to the intervention group. The primary diagnostic outcome was based on the general practitioner diagnosis, which agreed with the iLFT diagnosis in 67% of cases. In the iLFT group, the diagnosis of liver disease was increased by 43%. Additionally, there were significant increases in the rates of GP visits after diagnosis and the number of referrals to secondary care in the iLFT group. iLFT was cost-effective with a low initial incremental cost-effectiveness ratio of £284 per correct diagnosis, and a saving to the NHS of £3,216 per patient lifetime. CONCLUSIONS: iLFT increases liver disease diagnoses, 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. LAY SUMMARY: There is a growing epidemic of advanced liver disease, this could be offset by early detection and management. Checking liver blood tests (LFTs) should be an opportunity to diagnose liver problems, but abnormal results are often incompletely investigated. In this study we were able to substantially increase the diagnostic yield of the abnormal LFTs using the automated intelligent LFT system. With the addition of referral recommendations and management plans, this strategy provides optimum investigation and management of LFTs and is cost saving to the NHS.
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