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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7565544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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