ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | https://tede.unioeste.br/handle/tede/7111 |
Resumo: | Introduction: The multifactorial origin of acute kidney injury (AKI) after liver transplantation (LT) makes it complex to predict which candidate for the procedure presents an increased risk of this complication, but the significant impact of AKI on the prognosis of these patients highlights the need of the construction of a effective prediction model applicable in clinical practice for the occurrence of this complication. Objective: To develop a new risk score for the onset of acute AKI after LT by application of an artificial neural network model. Methodology: Data were collected from one 145 patients submitted to decesead donor LT, including demographic data and comorbidities of the recipient, clinical characteristics of the donor and graft, intraoperative information and laboratory tests. The primary outcome was postoperative AKI according to the International Club of Ascites criteria. By logistic regression, the predictors were identified and inputed in the artificial neural network algorithm, then accuracy of the artificial neural network and logistic regression models were tested. A scoring system based on the β coefficient values of the predictors was conducted, and a final prognostic score was determined and categorized into risk groups in the artificial neural network model. Results: The incidence of AKI was 60.6% (n = 88 / 145) and the following predictors of AKI onset were identified by logistic regression: MELD score ≥ 25, previous kidney dysfunction, grafts from extended donors criteria, intraoperative arterial hypotension , massive blood transfusion and levels of serum lactate ≥ 2 mmol/l at the end of surgery. These six independent variables were incorporated in the artificial neural network model, and the AUROC was best for artificial neural network (0.81) than for logistic regression model (0.71). There was satisfactory agreement in the artificial neural network model between predictions and actual AKI onset events (HLχ2 of 5.57, p = 0.612). The six predictors received weighted points for the risk score construction, and according to the artificial neural network model the cutoff values for AKI risk stratification were: 0 to 6 (low), 7 to 15 (moderate) and 16 to 22 (high), with significant difference of AKI incidence between the risk groups. Conclusions: The present new score by artificial neural network application is a new instrument for identification of recipients at risk of post-LT AKI, and this score is readily available at the end of the surgery, and would be a decision tool for prophylactic or early therapeutic procedures for postoperative AKI. |
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Peres, Luis Alberto BatistaPeres, Luis Alberto BatistaBonfleur, Maria LúciaGrassiolli, SabrinaNascimento, Marcelo Mazza doAzevedo, Valderilio Feijohttp://lattes.cnpq.br/5471823932486181Bredt, Luis César2024-03-27T14:05:58Z2023-10-27Bredt, Luis César. ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL. 2023. 196 f. Tese( Doutorado em Biociências e Saúde) - Universidade Estadual do Oeste do Paraná, Cascavel.https://tede.unioeste.br/handle/tede/7111Introduction: The multifactorial origin of acute kidney injury (AKI) after liver transplantation (LT) makes it complex to predict which candidate for the procedure presents an increased risk of this complication, but the significant impact of AKI on the prognosis of these patients highlights the need of the construction of a effective prediction model applicable in clinical practice for the occurrence of this complication. Objective: To develop a new risk score for the onset of acute AKI after LT by application of an artificial neural network model. Methodology: Data were collected from one 145 patients submitted to decesead donor LT, including demographic data and comorbidities of the recipient, clinical characteristics of the donor and graft, intraoperative information and laboratory tests. The primary outcome was postoperative AKI according to the International Club of Ascites criteria. By logistic regression, the predictors were identified and inputed in the artificial neural network algorithm, then accuracy of the artificial neural network and logistic regression models were tested. A scoring system based on the β coefficient values of the predictors was conducted, and a final prognostic score was determined and categorized into risk groups in the artificial neural network model. Results: The incidence of AKI was 60.6% (n = 88 / 145) and the following predictors of AKI onset were identified by logistic regression: MELD score ≥ 25, previous kidney dysfunction, grafts from extended donors criteria, intraoperative arterial hypotension , massive blood transfusion and levels of serum lactate ≥ 2 mmol/l at the end of surgery. These six independent variables were incorporated in the artificial neural network model, and the AUROC was best for artificial neural network (0.81) than for logistic regression model (0.71). There was satisfactory agreement in the artificial neural network model between predictions and actual AKI onset events (HLχ2 of 5.57, p = 0.612). The six predictors received weighted points for the risk score construction, and according to the artificial neural network model the cutoff values for AKI risk stratification were: 0 to 6 (low), 7 to 15 (moderate) and 16 to 22 (high), with significant difference of AKI incidence between the risk groups. Conclusions: The present new score by artificial neural network application is a new instrument for identification of recipients at risk of post-LT AKI, and this score is readily available at the end of the surgery, and would be a decision tool for prophylactic or early therapeutic procedures for postoperative AKI.Introdução: A etiologia multifatorial da injúria renal aguda (IRA) após transplante hepático (TH) torna complexa a previsão de qual candidato ao procedimento apresenta risco aumentado dessa complicação, mas o impacto significativo da IRA no prognóstico desses pacientes transplantados destaca a necessidade da construção de um modelo preditivo validado e aplicável na prática clínica, para a ocorrência dessa complicação. Objetivo: Desenvolver um novo escore de risco de ocorrência de injúria renal aguda (IRA) após transplante hepático por aplicação de um modelo de rede neural artificial. Metodologia: Foram coletadas as informações de cento e quarenta e cinco pacientes submetidos a transplante hepático com doador falecido, abrangendo dados demográficos e comorbidades do receptor, características clínicas do doador e do enxerto, informacões intraoperatórias e exames laboratoriais. O desfecho primário foi IRA pós-operatória de acordo com os critérios do International Club of Ascites. Por regressão logística, identificaram-se os preditores, os quais foram incorporados ao algoritmo de rede neural artificial; assim, o desempenho preditivo dos modelos de rede neural e regressão logística foram testados. Foi desenvolvido um sistema de pontuação baseado nos valores do coeficiente β das variáveis preditoras; dessa forma, um escore prognóstico final foi determinado e categorizado em grupos de risco no modelo de rede neural artificial. Resultados: A incidência de IRA foi de 60,6% (n = 88 / 145) e foram identificados por regressão logística os seguintes preditores de IRA: escore MELD ≥ 25, disfunção renal prévia, enxertos de doadores com critérios expandidos, hipotensão arterial intraoperatória, transfusão maciça de hemoderivados e lactato sérico ao fim do transplante ≥ 2 mmol/l. Essas seis variáveis independentes foram incorporadas ao modelo de rede neural artificial, que apresentou melhor desempenho preditivo que o modelo de regressão logística tradicional (área sob a curva ROC = 0,81 e 0,71, respectivamente). Houve concordância satisfatória no modelo de rede neural entre as previsões e as observações reais dos eventos de IRA (HLχ2 de 5,57, p = 0,612). As seis variáveis preditoras receberam pontos ponderados para a construção do escore de risco; assim, de acordo com o modelo de rede neural artificial, os valores de corte para estratificação do risco de IRA foram: 0 a 6 (baixo), 7 a 15 (moderado) e 16 a 22 (alto), com diferença significativa entre as incidências e graus de IRA entre os grupos de risco. Conclusões: O presente escore desenvolvido por aplicação de rede neural artificial é um novo instrumento para identificar receptores em risco de IRA pós-transplante hepático, sendo que essa pontuação está prontamente disponível ao final do procedimento, a qual efetiva-se como uma ferramenta de decisão para medidas profiláticas ou terapêuticas precoces de IRA pós-operatória.Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2024-03-27T14:05:58Z No. of bitstreams: 1 Luis Cesar Bredt.pdf: 11565625 bytes, checksum: 0ca0e8ad5f19098caf6fbcca036d1d15 (MD5)Made available in DSpace on 2024-03-27T14:05:58Z (GMT). No. of bitstreams: 1 Luis Cesar Bredt.pdf: 11565625 bytes, checksum: 0ca0e8ad5f19098caf6fbcca036d1d15 (MD5) Previous issue date: 2023-10-27application/pdfpor6588633818200016417500Universidade Estadual do Oeste do ParanáCascavelPrograma de Pós-Graduação em Biociências e SaúdeUNIOESTEBrasilCentro de Ciências Biológicas e da Saúdehttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessInjúria renal agudaEscore de riscoRede neural artificialAcute kidney injuryRisk scoreArtificial neural networkBIOLOGIA, PROCESSO SAÚDE-DOENÇA E POLITICAS DE SAÚDEESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIALRISK SCORE FOR ACUTE KIDNEY INJURY AFTER LIVER TRANSPLANTATION BY APPLICATION OF ARTIFICIAL NEURAL NETWORKinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-82512614700830132786006001458059979463924370reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALLuis Cesar Bredt.pdfLuis Cesar Bredt.pdfapplication/pdf11565625http://tede.unioeste.br:8080/tede/bitstream/tede/7111/2/Luis+Cesar+Bredt.pdf0ca0e8ad5f19098caf6fbcca036d1d15MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://tede.unioeste.br:8080/tede/bitstream/tede/7111/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede/71112024-04-08 10:28:44.068oai:tede.unioeste.br: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Biblioteca Digital de Teses e Dissertaçõeshttp://tede.unioeste.br/PUBhttp://tede.unioeste.br/oai/requestbiblioteca.repositorio@unioeste.bropendoar:2024-04-08T13:28:44Biblioteca Digital de Teses e Dissertações do UNIOESTE - Universidade Estadual do Oeste do Paraná (UNIOESTE)false |
dc.title.por.fl_str_mv |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
dc.title.alternative.eng.fl_str_mv |
RISK SCORE FOR ACUTE KIDNEY INJURY AFTER LIVER TRANSPLANTATION BY APPLICATION OF ARTIFICIAL NEURAL NETWORK |
title |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
spellingShingle |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL Bredt, Luis César Injúria renal aguda Escore de risco Rede neural artificial Acute kidney injury Risk score Artificial neural network BIOLOGIA, PROCESSO SAÚDE-DOENÇA E POLITICAS DE SAÚDE |
title_short |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
title_full |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
title_fullStr |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
title_full_unstemmed |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
title_sort |
ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL |
author |
Bredt, Luis César |
author_facet |
Bredt, Luis César |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Peres, Luis Alberto Batista |
dc.contributor.referee1.fl_str_mv |
Peres, Luis Alberto Batista |
dc.contributor.referee2.fl_str_mv |
Bonfleur, Maria Lúcia |
dc.contributor.referee3.fl_str_mv |
Grassiolli, Sabrina |
dc.contributor.referee4.fl_str_mv |
Nascimento, Marcelo Mazza do |
dc.contributor.referee5.fl_str_mv |
Azevedo, Valderilio Feijo |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/5471823932486181 |
dc.contributor.author.fl_str_mv |
Bredt, Luis César |
contributor_str_mv |
Peres, Luis Alberto Batista Peres, Luis Alberto Batista Bonfleur, Maria Lúcia Grassiolli, Sabrina Nascimento, Marcelo Mazza do Azevedo, Valderilio Feijo |
dc.subject.por.fl_str_mv |
Injúria renal aguda Escore de risco Rede neural artificial |
topic |
Injúria renal aguda Escore de risco Rede neural artificial Acute kidney injury Risk score Artificial neural network BIOLOGIA, PROCESSO SAÚDE-DOENÇA E POLITICAS DE SAÚDE |
dc.subject.eng.fl_str_mv |
Acute kidney injury Risk score Artificial neural network |
dc.subject.cnpq.fl_str_mv |
BIOLOGIA, PROCESSO SAÚDE-DOENÇA E POLITICAS DE SAÚDE |
description |
Introduction: The multifactorial origin of acute kidney injury (AKI) after liver transplantation (LT) makes it complex to predict which candidate for the procedure presents an increased risk of this complication, but the significant impact of AKI on the prognosis of these patients highlights the need of the construction of a effective prediction model applicable in clinical practice for the occurrence of this complication. Objective: To develop a new risk score for the onset of acute AKI after LT by application of an artificial neural network model. Methodology: Data were collected from one 145 patients submitted to decesead donor LT, including demographic data and comorbidities of the recipient, clinical characteristics of the donor and graft, intraoperative information and laboratory tests. The primary outcome was postoperative AKI according to the International Club of Ascites criteria. By logistic regression, the predictors were identified and inputed in the artificial neural network algorithm, then accuracy of the artificial neural network and logistic regression models were tested. A scoring system based on the β coefficient values of the predictors was conducted, and a final prognostic score was determined and categorized into risk groups in the artificial neural network model. Results: The incidence of AKI was 60.6% (n = 88 / 145) and the following predictors of AKI onset were identified by logistic regression: MELD score ≥ 25, previous kidney dysfunction, grafts from extended donors criteria, intraoperative arterial hypotension , massive blood transfusion and levels of serum lactate ≥ 2 mmol/l at the end of surgery. These six independent variables were incorporated in the artificial neural network model, and the AUROC was best for artificial neural network (0.81) than for logistic regression model (0.71). There was satisfactory agreement in the artificial neural network model between predictions and actual AKI onset events (HLχ2 of 5.57, p = 0.612). The six predictors received weighted points for the risk score construction, and according to the artificial neural network model the cutoff values for AKI risk stratification were: 0 to 6 (low), 7 to 15 (moderate) and 16 to 22 (high), with significant difference of AKI incidence between the risk groups. Conclusions: The present new score by artificial neural network application is a new instrument for identification of recipients at risk of post-LT AKI, and this score is readily available at the end of the surgery, and would be a decision tool for prophylactic or early therapeutic procedures for postoperative AKI. |
publishDate |
2023 |
dc.date.issued.fl_str_mv |
2023-10-27 |
dc.date.accessioned.fl_str_mv |
2024-03-27T14:05:58Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Bredt, Luis César. ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL. 2023. 196 f. Tese( Doutorado em Biociências e Saúde) - Universidade Estadual do Oeste do Paraná, Cascavel. |
dc.identifier.uri.fl_str_mv |
https://tede.unioeste.br/handle/tede/7111 |
identifier_str_mv |
Bredt, Luis César. ESCORE DE RISCO DE INJÚRIA RENAL AGUDA APÓS TRANSPLANTE HEPÁTICO POR APLICAÇÃO DE REDE NEURAL ARTIFICIAL. 2023. 196 f. Tese( Doutorado em Biociências e Saúde) - Universidade Estadual do Oeste do Paraná, Cascavel. |
url |
https://tede.unioeste.br/handle/tede/7111 |
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por |
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por |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Universidade Estadual do Oeste do Paraná Cascavel |
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Programa de Pós-Graduação em Biociências e Saúde |
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UNIOESTE |
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Brasil |
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Centro de Ciências Biológicas e da Saúde |
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Universidade Estadual do Oeste do Paraná Cascavel |
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