Decision analytic model of the diagnostic pathways for patients with suspected non-alcoholic fatty liver disease using non-invasive transient elastography and multiparametric magnetic resonance imaging

OBJECTIVES The mortality associated with liver disease continues to increase, despite the improvements implemented in the UK healthcare as does the prevalence of non-alcoholic fatty liver disease (NAFLD), given the escalating prevalence of obesity. The currently available methods to assess and monitor the stage of liver disease present several limitations. Recently, multiparametric MRI has been developed to address these limitations. The aim of this study is to develop a decision analytic model for patients with suspected NAFLD, to investigate the effect of adding multiparametric MRI to the diagnostic pathway. PERSPECTIVE The model takes the perspective of the UK National Health Service (NHS) as the service provider. METHODS A simple decision-tree model was developed to compare the costs associated with 3 diagnostic pathways for NAFLD that use non-invasive techniques. First, using transient elastography alone; second, using multiparametric MRI as an adjunct to transient elastography and third, multiparametric MRI alone. The model was built to capture these clinical pathways, and used to compare the expected diagnostic outcomes and costs associated with each. RESULTS The use of multiparametric MRI as an adjunct to transient elastography, while increasing screening costs, is predicted to reduce the number of liver biopsies required by about 66%. Used as the sole diagnostic scan, there remains an expected 16% reduction in the number of biopsies required. There is a small drop in the overall diagnostic accuracy, as in the current model, liver biopsy is presumed to give a definitive diagnosis. CONCLUSIONS The inclusion of multiparametric MRI, either as an adjunct to or replacement of transient elastography, in the diagnostic pathway of NAFLD may lead to cost savings for the NHS if the model presumptions hold. Further high-quality clinical evidence and cost data are required to test the model's predictions.