The Cost-Effectiveness of a Novel SIAscopic Diagnostic Aid for the Management of Pigmented Skin Lesions in Primary Care- A Decision-Analytic Model

Abstract

Objectives

Pigmented skin lesions are commonly presented in primary care. Appropriate diagnosis and management is challenging because the vast majority are benign. The MoleMate system is a handheld SIAscopy scanner integrated with a primary care diagnostic algorithm aimed at improving the management of pigmented skin lesions in primary care.

Methods

This decision-model–based economic evaluation draws on the results of a randomized controlled trial of the MoleMate system versus best practice (ISRCTN79932379) to estimate the expected long-term cost and health gain of diagnosis with the MoleMate system versus best practice in an English primary care setting. The model combines trial results with data from the wider literature to inform long-term prognosis, health state utilities, and cost.

Results

Results are reported as mean and incremental cost and quality-adjusted life-years (QALYs) gained, incremental cost-effectiveness ratio with probabilistic sensitivity analysis, and value of information analysis. Over a lifetime horizon, the MoleMate system is expected to cost an extra £18 over best practice alone, and yield an extra 0.01 QALYs per patient examined. The incremental cost-effectiveness ratio is £1,896 per QALY gained, with a 66.1% probability of being below £30,000 per QALY gained. The expected value of perfect information is £43.1 million.

Conclusions

Given typical thresholds in the United Kingdom (£20,000–£30,000 per QALY), the MoleMate system may be cost-effective compared with best practice diagnosis alone in a primary care setting. However, there is considerable decision uncertainty, driven particularly by the sensitivity and specificity of MoleMate versus best practice, and the risk of disease progression in undiagnosed melanoma; future research should focus on reducing uncertainty in these parameters.

Authors

Edward C.F. Wilson Jon D. Emery Ann Louise Kinmonth A. Toby Prevost Helen C. Morris Elka Humphrys Per N. Hall Nigel Burrows Lucy Bradshaw Joe Walls Paul Norris Margaret Johnson Fiona M. Walter

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