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Abstract
Purpose To determine the cost-effectiveness of annual renal imaging surveillance (RIS) in hereditary leiomyomatosis and renal cell cancer (HLRCC). HLRCC is associated with a 21% risk to age 70 years of RCC. Presentations with advanced renal cell cancer (RCC) are associated with poor outcomes whereas RIS detects early-stage RCC; however, evidence for the cost-effectiveness of RIS is lacking.
Methods We developed a decision-analytic model to compare, at different age starting points (11 years, 18 years, 40 years, 60 years), the costs and benefits of lifetime contrast-enhanced renal MRI surveillance (CERMRIS) vs no surveillance in HLRCC. Benefits were measured in life-years gained (LYG), quality-adjusted life years (QALYs) and costs in British Pounds Sterling (GBP). Net monetary benefit (NMB) was calculated using a cost-effectiveness threshold of £20 000/QALY. One-way sensitivity and probabilistic analyses were also performed.
Results In the base-case 11-year age cohort, surveillance was cost-effective (Incremental_NMB=£3522 (95% CI −£2747 to £7652); Incremental_LYG=1.25 (95% CI 0.30 to 1.86); Incremental_QALYs=0.29 (95% CI 0.07 to 0.43)] at an additional mean discounted cost of £2185/patient (95% CI £430 to £4144). Surveillance was also cost-effective in other age cohorts and dominated a no surveillance strategy in the 40 year cohort [Incremental_NMB=£12 655 (95% CIs −£709 to £21 134); Incremental_LYG=1.52 (95% CI 0.30 to 2.26); Incremental_QALYs=0.58 (95% CI 0.12 to 0.87) with a cost saving of £965/patient (95% CI −£4202 to £2652).
Conclusion Annual CERMRI in HLRCC is cost-effective across age groups modelled.
- health care economics and organizations
- genetics
- surgical oncology
- medical oncology
- genetic predisposition to disease
Data availability statement
Data are available on reasonable request. The data analysed and example code for the decision model used in this study are available from the corresponding author on request.
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- health care economics and organizations
- genetics
- surgical oncology
- medical oncology
- genetic predisposition to disease
Data availability statement
Data are available on reasonable request. The data analysed and example code for the decision model used in this study are available from the corresponding author on request.
Footnotes
Twitter @ER_Woodward
Collaborators N/A.
Contributors AJT, DGE, ERM and ERW conceptualised the project and ERW is its guarantor. AJT and ERW curated the data. AJT undertook the formal data analysis with assistance from YMA, VACR and ERW. All authors contributed to the interpretation of the results. AJT and ERW drafted the manuscript. All authors critically revised the manuscript.
Funding This work was funded by the NIHR Manchester Biomedical Research Centre (IS-BRC-1215-20007).
Disclaimer The views expressed are those of the authors and not necessarily those of the NHS or Department of Health.
Competing interests AJT declares conflicts with Perspectum. DGE declares consultancies with AstraZeneca, SpringWorks and Recursion and declares no conflict of interest specifically related to this study. The other authors declare no conflicts of interest.
Provenance and peer review Not commissioned; externally peer reviewed.
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