Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warrant...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0313600 |
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| _version_ | 1841533147264581632 |
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| author | Lathan Liou Elizabeth Mostofsky Laura Lehman Soziema Salia Francisco J Barrera Ying Wei Amal Cheema Anuradha Lala Andrew Beam Murray A Mittleman |
| author_facet | Lathan Liou Elizabeth Mostofsky Laura Lehman Soziema Salia Francisco J Barrera Ying Wei Amal Cheema Anuradha Lala Andrew Beam Murray A Mittleman |
| author_sort | Lathan Liou |
| collection | DOAJ |
| description | Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr. Model performance was also compared in a seasonally-matched pre-policy cohort. In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. The relative importance of some predictive variables differed between the pre-policy and post-policy cohorts, such as the absence of ECMO in the post-policy cohort. Survival machine learning models provide reasonable prediction of 1-year posttransplant mortality outcomes and continual updating of prediction models is warranted in the contemporary era. |
| format | Article |
| id | doaj-art-2e857634300d4ff79d46c8392f656b3c |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2e857634300d4ff79d46c8392f656b3c2025-01-17T05:31:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031360010.1371/journal.pone.0313600Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.Lathan LiouElizabeth MostofskyLaura LehmanSoziema SaliaFrancisco J BarreraYing WeiAmal CheemaAnuradha LalaAndrew BeamMurray A MittlemanAlthough prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr. Model performance was also compared in a seasonally-matched pre-policy cohort. In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. The relative importance of some predictive variables differed between the pre-policy and post-policy cohorts, such as the absence of ECMO in the post-policy cohort. Survival machine learning models provide reasonable prediction of 1-year posttransplant mortality outcomes and continual updating of prediction models is warranted in the contemporary era.https://doi.org/10.1371/journal.pone.0313600 |
| spellingShingle | Lathan Liou Elizabeth Mostofsky Laura Lehman Soziema Salia Francisco J Barrera Ying Wei Amal Cheema Anuradha Lala Andrew Beam Murray A Mittleman Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. PLoS ONE |
| title | Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. |
| title_full | Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. |
| title_fullStr | Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. |
| title_full_unstemmed | Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. |
| title_short | Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era. |
| title_sort | survival machine learning methods for mortality prediction after heart transplantation in the contemporary era |
| url | https://doi.org/10.1371/journal.pone.0313600 |
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