Maximizing utility of nondirected living liver donor grafts using machine learning

Autor: Kiran Bambha, Nicole J. Kim, Mark Sturdevant, James D. Perkins, Catherine Kling, Ramasamy Bakthavatsalam, Patrick Healey, Andre Dick, Jorge D. Reyes, Scott W. Biggins
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Frontiers in Immunology, Vol 14 (2023)
Druh dokumentu: article
ISSN: 1664-3224
DOI: 10.3389/fimmu.2023.1194338
Popis: ObjectiveThere is an unmet need for optimizing hepatic allograft allocation from nondirected living liver donors (ND-LLD).Materials and methodUsing OPTN living donor liver transplant (LDLT) data (1/1/2000-12/31/2019), we identified 6328 LDLTs (4621 right, 644 left, 1063 left-lateral grafts). Random forest survival models were constructed to predict 10-year graft survival for each of the 3 graft types.ResultsDonor-to-recipient body surface area ratio was an important predictor in all 3 models. Other predictors in all 3 models were: malignant diagnosis, medical location at LDLT (inpatient/ICU), and moderate ascites. Biliary atresia was important in left and left-lateral graft models. Re-transplant was important in right graft models. C-index for 10-year graft survival predictions for the 3 models were: 0.70 (left-lateral); 0.63 (left); 0.61 (right). Similar C-indices were found for 1-, 3-, and 5-year graft survivals. Comparison of model predictions to actual 10-year graft survivals demonstrated that the predicted upper quartile survival group in each model had significantly better actual 10-year graft survival compared to the lower quartiles (p
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