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PWE-126 An intelligent liver diagnostic tool
  1. A Dethier1,
  2. Y Zhu1,
  3. K Scott1,
  4. M Miller1,
  5. A Fraser2,
  6. E Forrest3,
  7. A Mac Gilchrist4,
  8. J Dillon1
  1. 1NHS Tayside, Dundee
  2. 2NHS Grampian, Aberdeen
  3. 3NHS Greater Glasgow and Clyde, Glasgow
  4. 4NHS Lothian, Edinburgh, UK

Abstract

Introduction Abnormal liver function tests (ALFTs) are common, up to 20% of all tests. Asymptomatic ALFTs rarely lead to serious chronic liver disease; however, liver disease presents often at an advanced stage with reduced treatment options and often, there was ALFTs prior to this. Referral of all patients with ALFTs to secondary care is not sustainable. The aim of this study was to validate a diagnostic algorithm developed by using LFTs which, if abnormal, trigger a liver screen. The algorithm can integrate into the tracked analyser systems to give automated complex results back to the GP with management advice and reduce referral to secondary care, saving time and improving diagnosis rate.

Method The algorithm was developed by an expert panel of hepatologists defining minimal diagnostic criteria for liver disease. Entry to the algorithm depended on ALFTs in the absence of obvious liver dysfunction; triggering a liver screen for biochemical, immunological and viral causes of liver disease and using age, gender, alcohol consumption, BMI and calculated fibrosis scores for specific diseases. A systematic retrospective review of the electronic notes and lab results of 100 consecutive patients was performed. They had been referred to secondary care after first discovery of ALFTs in 2012–2013 in NHS Tayside. Those with an established chronic liver disease and/or previous known abnormal GGT and AST were excluded. Patients were categorised by the completed algorithm into 3 groups depending on where they should be managed: secondary or primary care or to a category of unclear diagnosis. A comparison was made between where the patient was managed and where he/she would have been managed if the algorithm had been applied at the time.

Results In our study, 80% patients were appropriately categorised by the algorithm (54% in Primary care and 26% in Secondary care) following the criteria. The algorithm would have referred another 19% to specialists, although GP care would have been safe, and 1% under Primary care but would actually need specialist input, having established cirrhosis on liver biopsy despite a diagnosis of NAFLD, with a NAFLD fibrosis score of -1.52 (<-1.455, 90% sensitivity and 60% specificity to exclude fibrosis) on algorithm. The sensitivity of our algorithm is 96.3%, the specificity 73.9%, the positive predictive value 57.8% and negative predictive value 98.2%.

Conclusion This algorithm safely identifies the majority of patients with asymptomatic ALFTs whose liver disease can be managed in primary care and correctly determine patients who require referral to a Hepatologist. If combined with an automated laboratory analyser system, this could correctly diagnose and plan management for more than 75% on the first abnormal LFT value.

Disclosure of interest None Declared.

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