Development and validation of diagnostic triage criteria for liver disease from a minimum data set enabling the 'intelligent LFT' pathway for the automated assessment of deranged liver enzymes. Issue 3 (7th February 2018) - Miller, Michael Hugh; Fraser, Andrew; Leggett, Gillian; MacGilchrist, Alastair; Gibson, George; Orr, James; Forrest, Ewan H; Dow, Ellie; Bartlett, William; Weatherburn, Chirstopher; Laurell, Axel; Grant, Kirsty; et al.
Abstract : Background: Liver function tests (LFTs) are commonly abnormal; most patients with 'incidental' abnormal LFTs are not investigated appropriately and for those who are, current care pathways are geared to find an explanation for the abnormality by a lengthy process of investigation and exclusion, with costs to the patient and to the health service. Objective: To validate an intelligent automatable analysis tool (iLFT) for abnormal liver enzymes, which diagnoses common liver conditions, provides fibrosis stage and recommends management Design: A retrospective case note review from three tertiary referral liver centres, with application of the iLFT algorithm and comparison with the clinician's final opinion as gold standard. Results: The iLFT algorithm in 91.3% of cases would have correctly recommended referral or management in primary care. In the majority of the rest of the cases, iLFT failed safe and recommended referral even when the final clinical diagnosis could have been managed in primary care. Diagnostic accuracy was achieved in 82.4% of cases, consistent with the fail-safe design of the algorithm. Two cases would have remained in primary care as per the algorithm outcome, however on clinical review had features of advanced fibrosis. Conclusion: iLFT analysis of abnormal liver enzymes offers a safe and robust method of risk stratifying patients to the most appropriate care pathway as well as providing reliable diagnostic information based on a single blood draw, without repeated contacts with health services. Offers the possibility of high quality investigation and diagnosis to all patients rather than a tiny minority.