Adverse Outcomes Prediction for Congenital Heart Surgery

Detalhes bibliográficos
Autor(a) principal: Bertsimas, Dimitris
Data de Publicação: 2021
Outros Autores: Zhuo, Daisy, Dunn, Jack, Levine, Jordan, Zuccarelli, Eugenio, Smyrnakis, Nikos, Tobota, Zdzislaw, Maruszewski, Bohdan, Fragata, Jose, Sarris, George E.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150568
Resumo: Funding Information: The authors wish to gratefully acknowledge the contributions of all ECDB participating surgeons and centers, as the contributions of the data concerning their patients who had surgery with CHD over the years have made this study possible. The author(s) received no financial support for the research, authorship, and/or publication of this article. Publisher Copyright: © The Author(s) 2021.
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spelling Adverse Outcomes Prediction for Congenital Heart SurgeryA Machine Learning Approachartificial intelligencecongenital heart surgeryoutcomesstatistics-risk analysis/modelingstatistics-survival analysisSurgeryPediatrics, Perinatology, and Child HealthCardiology and Cardiovascular MedicineFunding Information: The authors wish to gratefully acknowledge the contributions of all ECDB participating surgeons and centers, as the contributions of the data concerning their patients who had surgery with CHD over the years have made this study possible. The author(s) received no financial support for the research, authorship, and/or publication of this article. Publisher Copyright: © The Author(s) 2021.Objective: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS. Methods: We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. Results: Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors. Conclusions: The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNBertsimas, DimitrisZhuo, DaisyDunn, JackLevine, JordanZuccarelli, EugenioSmyrnakis, NikosTobota, ZdzislawMaruszewski, BohdanFragata, JoseSarris, George E.2023-03-14T22:44:07Z2021-072021-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article8application/pdfhttp://hdl.handle.net/10362/150568eng2150-1351PURE: 55671050https://doi.org/10.1177/21501351211007106info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:32:35Zoai:run.unl.pt:10362/150568Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:11.401834Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Adverse Outcomes Prediction for Congenital Heart Surgery
A Machine Learning Approach
title Adverse Outcomes Prediction for Congenital Heart Surgery
spellingShingle Adverse Outcomes Prediction for Congenital Heart Surgery
Bertsimas, Dimitris
artificial intelligence
congenital heart surgery
outcomes
statistics-risk analysis/modeling
statistics-survival analysis
Surgery
Pediatrics, Perinatology, and Child Health
Cardiology and Cardiovascular Medicine
title_short Adverse Outcomes Prediction for Congenital Heart Surgery
title_full Adverse Outcomes Prediction for Congenital Heart Surgery
title_fullStr Adverse Outcomes Prediction for Congenital Heart Surgery
title_full_unstemmed Adverse Outcomes Prediction for Congenital Heart Surgery
title_sort Adverse Outcomes Prediction for Congenital Heart Surgery
author Bertsimas, Dimitris
author_facet Bertsimas, Dimitris
Zhuo, Daisy
Dunn, Jack
Levine, Jordan
Zuccarelli, Eugenio
Smyrnakis, Nikos
Tobota, Zdzislaw
Maruszewski, Bohdan
Fragata, Jose
Sarris, George E.
author_role author
author2 Zhuo, Daisy
Dunn, Jack
Levine, Jordan
Zuccarelli, Eugenio
Smyrnakis, Nikos
Tobota, Zdzislaw
Maruszewski, Bohdan
Fragata, Jose
Sarris, George E.
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
RUN
dc.contributor.author.fl_str_mv Bertsimas, Dimitris
Zhuo, Daisy
Dunn, Jack
Levine, Jordan
Zuccarelli, Eugenio
Smyrnakis, Nikos
Tobota, Zdzislaw
Maruszewski, Bohdan
Fragata, Jose
Sarris, George E.
dc.subject.por.fl_str_mv artificial intelligence
congenital heart surgery
outcomes
statistics-risk analysis/modeling
statistics-survival analysis
Surgery
Pediatrics, Perinatology, and Child Health
Cardiology and Cardiovascular Medicine
topic artificial intelligence
congenital heart surgery
outcomes
statistics-risk analysis/modeling
statistics-survival analysis
Surgery
Pediatrics, Perinatology, and Child Health
Cardiology and Cardiovascular Medicine
description Funding Information: The authors wish to gratefully acknowledge the contributions of all ECDB participating surgeons and centers, as the contributions of the data concerning their patients who had surgery with CHD over the years have made this study possible. The author(s) received no financial support for the research, authorship, and/or publication of this article. Publisher Copyright: © The Author(s) 2021.
publishDate 2021
dc.date.none.fl_str_mv 2021-07
2021-07-01T00:00:00Z
2023-03-14T22:44:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/150568
url http://hdl.handle.net/10362/150568
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2150-1351
PURE: 55671050
https://doi.org/10.1177/21501351211007106
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 8
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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