The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis.
BACKGROUND:This study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. METHODS:A total of 443 brain dead de...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0228597 |
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| author | Silvana Daher Costa Luis Gustavo Modelli de Andrade Francisco Victor Carvalho Barroso Cláudia Maria Costa de Oliveira Elizabeth De Francesco Daher Paula Frassinetti Castelo Branco Camurça Fernandes Ronaldo de Matos Esmeraldo Tainá Veras de Sandes-Freitas |
| author_facet | Silvana Daher Costa Luis Gustavo Modelli de Andrade Francisco Victor Carvalho Barroso Cláudia Maria Costa de Oliveira Elizabeth De Francesco Daher Paula Frassinetti Castelo Branco Camurça Fernandes Ronaldo de Matos Esmeraldo Tainá Veras de Sandes-Freitas |
| author_sort | Silvana Daher Costa |
| collection | DOAJ |
| description | BACKGROUND:This study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. METHODS:A total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following DMR were evaluated using predictive modeling: arterial blood gas pH, serum sodium, blood glucose, urine output, mean arterial pressure, vasopressors use, and reversed cardiac arrest. RESULTS:Most patients (95.7%) received kidneys from standard criteria donors. The incidence of DGF was 53%. In multivariable logistic regression analysis, DMR variables did not impact on DGF occurrence. In post-hoc analysis including only KT with cold ischemia time<21h (n = 220), urine output in 24h prior to recovery surgery (OR = 0.639, 95%CI 0.444-0.919) and serum sodium (OR = 1.030, 95%CI 1.052-1.379) were risk factors for DGF. Using elastic net regularized regression model and ML analysis (decision tree, neural network and support vector machine), urine output and other DMR variables emerged as DGF predictors: mean arterial pressure, ≥ 1 or high dose vasopressors and blood glucose. CONCLUSIONS:Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF. |
| first_indexed | 2024-12-20T03:02:23Z |
| format | Article |
| id | doaj.art-c8e62745ffc24382ba1c38c3f0b5642c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T03:02:23Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c8e62745ffc24382ba1c38c3f0b5642c2022-12-21T19:55:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01152e022859710.1371/journal.pone.0228597The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis.Silvana Daher CostaLuis Gustavo Modelli de AndradeFrancisco Victor Carvalho BarrosoCláudia Maria Costa de OliveiraElizabeth De Francesco DaherPaula Frassinetti Castelo Branco Camurça FernandesRonaldo de Matos EsmeraldoTainá Veras de Sandes-FreitasBACKGROUND:This study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. METHODS:A total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following DMR were evaluated using predictive modeling: arterial blood gas pH, serum sodium, blood glucose, urine output, mean arterial pressure, vasopressors use, and reversed cardiac arrest. RESULTS:Most patients (95.7%) received kidneys from standard criteria donors. The incidence of DGF was 53%. In multivariable logistic regression analysis, DMR variables did not impact on DGF occurrence. In post-hoc analysis including only KT with cold ischemia time<21h (n = 220), urine output in 24h prior to recovery surgery (OR = 0.639, 95%CI 0.444-0.919) and serum sodium (OR = 1.030, 95%CI 1.052-1.379) were risk factors for DGF. Using elastic net regularized regression model and ML analysis (decision tree, neural network and support vector machine), urine output and other DMR variables emerged as DGF predictors: mean arterial pressure, ≥ 1 or high dose vasopressors and blood glucose. CONCLUSIONS:Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.https://doi.org/10.1371/journal.pone.0228597 |
| spellingShingle | Silvana Daher Costa Luis Gustavo Modelli de Andrade Francisco Victor Carvalho Barroso Cláudia Maria Costa de Oliveira Elizabeth De Francesco Daher Paula Frassinetti Castelo Branco Camurça Fernandes Ronaldo de Matos Esmeraldo Tainá Veras de Sandes-Freitas The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. PLoS ONE |
| title | The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. |
| title_full | The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. |
| title_fullStr | The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. |
| title_full_unstemmed | The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. |
| title_short | The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. |
| title_sort | impact of deceased donor maintenance on delayed kidney allograft function a machine learning analysis |
| url | https://doi.org/10.1371/journal.pone.0228597 |
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