Maximizing utility of nondirected living liver donor grafts using machine learning.
Front Immunol
; 14: 1194338, 2023.
Article
em En
| MEDLINE
| ID: mdl-37457719
ABSTRACT
Objective:
There is an unmet need for optimizing hepatic allograft allocation from nondirected living liver donors (ND-LLD). Materials andmethod:
Using 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.Results:
Donor-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<0.005).Conclusion:
When applied in clinical context, our models assist with the identification and stratification of potential recipients for hepatic grafts from ND-LLD based on predicted graft survivals, while accounting for complex donor-recipient interactions. These analyses highlight the unmet need for granular data collection and machine learning modeling to identify potential recipients who have the best predicted transplant outcomes with ND-LLD grafts.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transplante de Fígado
/
Falência Hepática
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article