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Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study

OBJECTIVE: To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. METHODS: Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retr...

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Autores principales: Shim, Jae-Geum, Ryu, Kyoung-Ho, Lee, Sung Hyun, Cho, Eun-Ah, Lee, Sungho, Ahn, Jin Hee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8412312/
https://www.ncbi.nlm.nih.gov/pubmed/34473775
http://dx.doi.org/10.1371/journal.pone.0257069
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author Shim, Jae-Geum
Ryu, Kyoung-Ho
Lee, Sung Hyun
Cho, Eun-Ah
Lee, Sungho
Ahn, Jin Hee
author_facet Shim, Jae-Geum
Ryu, Kyoung-Ho
Lee, Sung Hyun
Cho, Eun-Ah
Lee, Sungho
Ahn, Jin Hee
author_sort Shim, Jae-Geum
collection PubMed
description OBJECTIVE: To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. METHODS: Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID). RESULTS: For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula. CONCLUSIONS: In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients.
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spelling pubmed-84123122021-09-03 Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study Shim, Jae-Geum Ryu, Kyoung-Ho Lee, Sung Hyun Cho, Eun-Ah Lee, Sungho Ahn, Jin Hee PLoS One Research Article OBJECTIVE: To construct a prediction model for optimal tracheal tube depth in pediatric patients using machine learning. METHODS: Pediatric patients aged <7 years who received post-operative ventilation after undergoing surgery between January 2015 and December 2018 were investigated in this retrospective study. The optimal location of the tracheal tube was defined as the median of the distance between the upper margin of the first thoracic(T1) vertebral body and the lower margin of the third thoracic(T3) vertebral body. We applied four machine learning models: random forest, elastic net, support vector machine, and artificial neural network and compared their prediction accuracy to three formula-based methods, which were based on age, height, and tracheal tube internal diameter(ID). RESULTS: For each method, the percentage with optimal tracheal tube depth predictions in the test set was calculated as follows: 79.0 (95% confidence interval [CI], 73.5 to 83.6) for random forest, 77.4 (95% CI, 71.8 to 82.2; P = 0.719) for elastic net, 77.0 (95% CI, 71.4 to 81.8; P = 0.486) for support vector machine, 76.6 (95% CI, 71.0 to 81.5; P = 1.0) for artificial neural network, 66.9 (95% CI, 60.9 to 72.5; P < 0.001) for the age-based formula, 58.5 (95% CI, 52.3 to 64.4; P< 0.001) for the tube ID-based formula, and 44.4 (95% CI, 38.3 to 50.6; P < 0.001) for the height-based formula. CONCLUSIONS: In this study, the machine learning models predicted the optimal tracheal tube tip location for pediatric patients more accurately than the formula-based methods. Machine learning models using biometric variables may help clinicians make decisions regarding optimal tracheal tube depth in pediatric patients. Public Library of Science 2021-09-02 /pmc/articles/PMC8412312/ /pubmed/34473775 http://dx.doi.org/10.1371/journal.pone.0257069 Text en © 2021 Shim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shim, Jae-Geum
Ryu, Kyoung-Ho
Lee, Sung Hyun
Cho, Eun-Ah
Lee, Sungho
Ahn, Jin Hee
Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
title Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
title_full Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
title_fullStr Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
title_full_unstemmed Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
title_short Machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: A retrospective cohort study
title_sort machine learning model for predicting the optimal depth of tracheal tube insertion in pediatric patients: a retrospective cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8412312/
https://www.ncbi.nlm.nih.gov/pubmed/34473775
http://dx.doi.org/10.1371/journal.pone.0257069
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