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
| Autor: | Dimitris Bertsimas, Nikolaos Trichakis, Ryutaro Hirose, Parsia A. Vagefi |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Tissue and Organ Procurement Waiting Lists medicine.medical_treatment 030230 surgery Liver transplantation Machine learning computer.software_genre End Stage Liver Disease Machine Learning 03 medical and health sciences Liver disease 0302 clinical medicine Model for End-Stage Liver Disease Disease severity Humans Immunology and Allergy Medicine Transplantation business.industry Decision tree learning medicine.disease Liver Transplantation body regions Clinical Practice Female 030211 gastroenterology & hepatology Artificial intelligence Waitlist mortality business computer |
| Zdroj: | Current Opinion in Organ Transplantation. 25:122-125 |
| ISSN: | 1531-7013 1087-2418 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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