A machine learning prediction model for waiting time to kidney transplant

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorTransplant Unit–Santa Casa Juiz de Fora-
Autor(es): dc.contributorKidney-Pancreas Transplantation Service of Leforte and Oswaldo Cruz Hospitals-
Autor(es): dc.contributorTransplant Unit Division-
Autor(es): dc.contributorLiberdade-
Autor(es): dc.creatorSapiertein Silva, Juliana Feiman [UNESP]-
Autor(es): dc.creatorFerreira, Gustavo Fernandes-
Autor(es): dc.creatorPerosa, Marcelo-
Autor(es): dc.creatorNga, Hong Si [UNESP]-
Autor(es): dc.creatorde Andrade, Luis Gustavo Modelli [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:46:51Z-
Data de disponibilização: dc.date.available2022-02-22T00:46:51Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1371/journal.pone.0252069-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/206356-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/206356-
Descrição: dc.descriptionBackground Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach. Methods A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations. Results Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70). Conclusion The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.-
Descrição: dc.descriptionDepartment of Internal Medicine UNESP Univ Estadual Paulista-
Descrição: dc.descriptionTransplant Unit–Santa Casa Juiz de Fora-
Descrição: dc.descriptionKidney-Pancreas Transplantation Service of Leforte and Oswaldo Cruz Hospitals-
Descrição: dc.descriptionTransplant Unit Division-
Descrição: dc.descriptionTransplant Unit Division Liberdade-
Descrição: dc.descriptionDepartment of Internal Medicine UNESP Univ Estadual Paulista-
Idioma: dc.languageen-
Relação: dc.relationPLoS ONE-
???dc.source???: dc.sourceScopus-
Título: dc.titleA machine learning prediction model for waiting time to kidney transplant-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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