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The rise and fall of the model for end-stage liver disease score and the need for an optimized machine learning approach for liver allocation

Organization: Wolters Kluwer
Journey Stage: (G) Survival on the Waitlist
Organization Type: Society or Professional Organization
Use Case(s): Allocation, Patient Access, Patient Experience
Country: USA

Pulled from PubMed

Parsia A Vagefi 1, Dimitris Bertsimas 2, Ryutaro Hirose 3, Nikolaos Trichakis 2

Purpose of review: The Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates since 2002, and at the time bringing much needed objectivity to the liver allocation process. However, and despite numerous revisions to the MELD score, current liver allocation still does not allow for equitable access to all waitlisted liver candidates.

Recent findings: An optimized prediction of mortality (OPOM) was developed utilizing novel machine-learning optimal classification tree models trained to predict a liver candidate’s 3-month waitlist mortality or removal. When compared to MELD and MELD-Na, OPOM more accurately and objectively prioritized candidates for liver transplantation based on disease severity. In simulation analysis, OPOM allowed for more equitable allocation of livers with a resultant significant number of additional lives saved every year when compared with MELD-based allocation.

Summary: Machine learning technology holds the potential to help guide transplant clinical practice, and thus potentially guide national organ allocation policy.

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Key Contact:

Parsia A
Vagefi •
Executive Clinical Director, Solid Organ Transplant; Executive Vice Chair of Strategy and Finance (Surgery), Professor and Interim Chair of Surgery

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