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Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation

Brain injury is a significant source of morbidity and mortality for pediatric patients treated with Extracorporeal Membrane Oxygenation (ECMO). Our objective was to utilize neural networks to predict radiographic evidence of brain injury in pediatric ECMO-supported patients and identify specific var...

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Autores principales: Shah, Neel, Farhat, Abdelaziz, Tweed, Jefferson, Wang, Ziheng, Lee, Jeon, McBeth, Rafe, Skinner, Michael, Tian, Fenghua, Thiagarajan, Ravi, Raman, Lakshmi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565544/
https://www.ncbi.nlm.nih.gov/pubmed/32842683
http://dx.doi.org/10.3390/jcm9092718
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author Shah, Neel
Farhat, Abdelaziz
Tweed, Jefferson
Wang, Ziheng
Lee, Jeon
McBeth, Rafe
Skinner, Michael
Tian, Fenghua
Thiagarajan, Ravi
Raman, Lakshmi
author_facet Shah, Neel
Farhat, Abdelaziz
Tweed, Jefferson
Wang, Ziheng
Lee, Jeon
McBeth, Rafe
Skinner, Michael
Tian, Fenghua
Thiagarajan, Ravi
Raman, Lakshmi
author_sort Shah, Neel
collection PubMed
description Brain injury is a significant source of morbidity and mortality for pediatric patients treated with Extracorporeal Membrane Oxygenation (ECMO). Our objective was to utilize neural networks to predict radiographic evidence of brain injury in pediatric ECMO-supported patients and identify specific variables that can be explored for future research. Data from 174 ECMO-supported patients were collected up to 24 h prior to, and for the duration of, the ECMO course. Thirty-five variables were collected, including physiological data, markers of end-organ perfusion, acid-base homeostasis, vasoactive infusions, markers of coagulation, and ECMO-machine factors. The primary outcome was the presence of radiologic evidence of moderate to severe brain injury as established by brain CT or MRI. This information was analyzed by a neural network, and results were compared to a logistic regression model as well as clinician judgement. The neural network model was able to predict brain injury with an Area Under the Curve (AUC) of 0.76, 73% sensitivity, and 80% specificity. Logistic regression had 62% sensitivity and 61% specificity. Clinician judgment had 39% sensitivity and 69% specificity. Sequential feature group masking demonstrated a relatively greater contribution of physiological data and minor contribution of coagulation factors to the model's performance. These findings lay the foundation for further areas of research directions.
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spelling pubmed-75655442020-10-26 Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation Shah, Neel Farhat, Abdelaziz Tweed, Jefferson Wang, Ziheng Lee, Jeon McBeth, Rafe Skinner, Michael Tian, Fenghua Thiagarajan, Ravi Raman, Lakshmi J Clin Med Article Brain injury is a significant source of morbidity and mortality for pediatric patients treated with Extracorporeal Membrane Oxygenation (ECMO). Our objective was to utilize neural networks to predict radiographic evidence of brain injury in pediatric ECMO-supported patients and identify specific variables that can be explored for future research. Data from 174 ECMO-supported patients were collected up to 24 h prior to, and for the duration of, the ECMO course. Thirty-five variables were collected, including physiological data, markers of end-organ perfusion, acid-base homeostasis, vasoactive infusions, markers of coagulation, and ECMO-machine factors. The primary outcome was the presence of radiologic evidence of moderate to severe brain injury as established by brain CT or MRI. This information was analyzed by a neural network, and results were compared to a logistic regression model as well as clinician judgement. The neural network model was able to predict brain injury with an Area Under the Curve (AUC) of 0.76, 73% sensitivity, and 80% specificity. Logistic regression had 62% sensitivity and 61% specificity. Clinician judgment had 39% sensitivity and 69% specificity. Sequential feature group masking demonstrated a relatively greater contribution of physiological data and minor contribution of coagulation factors to the model's performance. These findings lay the foundation for further areas of research directions. MDPI 2020-08-22 /pmc/articles/PMC7565544/ /pubmed/32842683 http://dx.doi.org/10.3390/jcm9092718 Text en © 2020 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
Shah, Neel
Farhat, Abdelaziz
Tweed, Jefferson
Wang, Ziheng
Lee, Jeon
McBeth, Rafe
Skinner, Michael
Tian, Fenghua
Thiagarajan, Ravi
Raman, Lakshmi
Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation
title Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation
title_full Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation
title_fullStr Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation
title_full_unstemmed Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation
title_short Neural Networks to Predict Radiographic Brain Injury in Pediatric Patients Treated with Extracorporeal Membrane Oxygenation
title_sort neural networks to predict radiographic brain injury in pediatric patients treated with extracorporeal membrane oxygenation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565544/
https://www.ncbi.nlm.nih.gov/pubmed/32842683
http://dx.doi.org/10.3390/jcm9092718
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