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Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study

Background: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making...

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Autores principales: Braun, Julia, Sahli, Sebastian D., Spahn, Donat R., Röder, Daniel, Neb, Holger, Lotz, Gösta, Aser, Raed, Wilhelm, Markus J., Kaserer, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10573956/
https://www.ncbi.nlm.nih.gov/pubmed/37834887
http://dx.doi.org/10.3390/jcm12196243
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author Braun, Julia
Sahli, Sebastian D.
Spahn, Donat R.
Röder, Daniel
Neb, Holger
Lotz, Gösta
Aser, Raed
Wilhelm, Markus J.
Kaserer, Alexander
author_facet Braun, Julia
Sahli, Sebastian D.
Spahn, Donat R.
Röder, Daniel
Neb, Holger
Lotz, Gösta
Aser, Raed
Wilhelm, Markus J.
Kaserer, Alexander
author_sort Braun, Julia
collection PubMed
description Background: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion. Methods: This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019. Results: 837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets. Conclusions: Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables.
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spelling pubmed-105739562023-10-14 Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study Braun, Julia Sahli, Sebastian D. Spahn, Donat R. Röder, Daniel Neb, Holger Lotz, Gösta Aser, Raed Wilhelm, Markus J. Kaserer, Alexander J Clin Med Article Background: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion. Methods: This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019. Results: 837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets. Conclusions: Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables. MDPI 2023-09-28 /pmc/articles/PMC10573956/ /pubmed/37834887 http://dx.doi.org/10.3390/jcm12196243 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Braun, Julia
Sahli, Sebastian D.
Spahn, Donat R.
Röder, Daniel
Neb, Holger
Lotz, Gösta
Aser, Raed
Wilhelm, Markus J.
Kaserer, Alexander
Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study
title Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study
title_full Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study
title_fullStr Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study
title_full_unstemmed Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study
title_short Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees—A Multicenter Study
title_sort predicting survival for veno-arterial ecmo using conditional inference trees—a multicenter study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10573956/
https://www.ncbi.nlm.nih.gov/pubmed/37834887
http://dx.doi.org/10.3390/jcm12196243
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