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Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach

Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulati...

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Autores principales: Seo, Dong-Woo, Yi, Hahn, Bae, Hyun-Jin, Kim, Youn-Jung, Sohn, Chang-Hwan, Ahn, Shin, Lim, Kyoung-Soo, Kim, Namkug, Kim, Won-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961400/
https://www.ncbi.nlm.nih.gov/pubmed/33807882
http://dx.doi.org/10.3390/jcm10051089
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author Seo, Dong-Woo
Yi, Hahn
Bae, Hyun-Jin
Kim, Youn-Jung
Sohn, Chang-Hwan
Ahn, Shin
Lim, Kyoung-Soo
Kim, Namkug
Kim, Won-Young
author_facet Seo, Dong-Woo
Yi, Hahn
Bae, Hyun-Jin
Kim, Youn-Jung
Sohn, Chang-Hwan
Ahn, Shin
Lim, Kyoung-Soo
Kim, Namkug
Kim, Won-Young
author_sort Seo, Dong-Woo
collection PubMed
description Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulation (ROSC). We developed and validated a machine learning-based system to predict good outcome in OHCA patients before ROSC. This prospective, multicenter, registry-based study analyzed non-traumatic OHCA data collected between October 2015 and June 2017. We used information available before ROSC as predictor variables, and the primary outcome was neurologically intact survival at discharge, defined as cerebral performance category 1 or 2. The developed models’ robustness were evaluated and compared with various score metrics to confirm their performance. The model using a voting classifier had the best performance in predicting good neurological outcome (area under the curve = 0.926). We confirmed that the six top-weighted variables predicting neurological outcomes, such as several duration variables after the instant of OHCA and several electrocardiogram variables in the voting classifier model, showed significant differences between the two neurological outcome groups. These findings demonstrate the potential utility of a machine learning model to predict good neurological outcome of OHCA patients before ROSC.
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spelling pubmed-79614002021-03-17 Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach Seo, Dong-Woo Yi, Hahn Bae, Hyun-Jin Kim, Youn-Jung Sohn, Chang-Hwan Ahn, Shin Lim, Kyoung-Soo Kim, Namkug Kim, Won-Young J Clin Med Article Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulation (ROSC). We developed and validated a machine learning-based system to predict good outcome in OHCA patients before ROSC. This prospective, multicenter, registry-based study analyzed non-traumatic OHCA data collected between October 2015 and June 2017. We used information available before ROSC as predictor variables, and the primary outcome was neurologically intact survival at discharge, defined as cerebral performance category 1 or 2. The developed models’ robustness were evaluated and compared with various score metrics to confirm their performance. The model using a voting classifier had the best performance in predicting good neurological outcome (area under the curve = 0.926). We confirmed that the six top-weighted variables predicting neurological outcomes, such as several duration variables after the instant of OHCA and several electrocardiogram variables in the voting classifier model, showed significant differences between the two neurological outcome groups. These findings demonstrate the potential utility of a machine learning model to predict good neurological outcome of OHCA patients before ROSC. MDPI 2021-03-05 /pmc/articles/PMC7961400/ /pubmed/33807882 http://dx.doi.org/10.3390/jcm10051089 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Seo, Dong-Woo
Yi, Hahn
Bae, Hyun-Jin
Kim, Youn-Jung
Sohn, Chang-Hwan
Ahn, Shin
Lim, Kyoung-Soo
Kim, Namkug
Kim, Won-Young
Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach
title Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach
title_full Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach
title_fullStr Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach
title_full_unstemmed Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach
title_short Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach
title_sort prediction of neurologically intact survival in cardiac arrest patients without pre-hospital return of spontaneous circulation: machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961400/
https://www.ncbi.nlm.nih.gov/pubmed/33807882
http://dx.doi.org/10.3390/jcm10051089
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