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Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models
OBJECTIVE: We aimed to construct and validate machine learning models for endotracheal tube (ETT) size prediction in pediatric patients. METHODS: Data of 990 pediatric patients underwent endotracheal intubation were retrospectively collected between November 2019 and October 2021, and separated into...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631215/ https://www.ncbi.nlm.nih.gov/pubmed/36340734 http://dx.doi.org/10.3389/fped.2022.970646 |
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author | Zhou, Miao Xu, Wen.Y. Xu, Sheng Zang, Qing L. Li, Qi Tan, Li Hu, Yong C. Ma, Ning Xia, Jian H. Liu, Kun Ye, Min Pu, Fei Y. Chen, Liang Song, Li J. Liu, Yang Jiang, Lai Gu, Lin Zou, Zui |
author_facet | Zhou, Miao Xu, Wen.Y. Xu, Sheng Zang, Qing L. Li, Qi Tan, Li Hu, Yong C. Ma, Ning Xia, Jian H. Liu, Kun Ye, Min Pu, Fei Y. Chen, Liang Song, Li J. Liu, Yang Jiang, Lai Gu, Lin Zou, Zui |
author_sort | Zhou, Miao |
collection | PubMed |
description | OBJECTIVE: We aimed to construct and validate machine learning models for endotracheal tube (ETT) size prediction in pediatric patients. METHODS: Data of 990 pediatric patients underwent endotracheal intubation were retrospectively collected between November 2019 and October 2021, and separated into cuffed and uncuffed endotracheal tube subgroups. Six machine learning algorithms, including support vector regression (SVR), logistic regression (LR), random forest (RF), gradient boosting tree (GBR), decision tree (DTR) and extreme gradient boosting tree (XGBR), were selected to construct and validate models using ten-fold cross validation in training set. The optimal models were selected, and the performance were compared with traditional predictive formulas and clinicians. Furthermore, additional data of 71 pediatric patients were collected to perform external validation. RESULTS: The optimal 7 uncuffed and 5 cuffed variables were screened out by feature selecting. The RF models had the best performance with minimizing prediction error for both uncuffed ETT size (MAE = 0.275 mm and RMSE = 0.349 mm) and cuffed ETT size (MAE = 0.243 mm and RMSE = 0.310 mm). The RF models were also superior in predicting power than formulas in both uncuffed and cuffed ETT size prediction. In addition, the RF models performed slightly better than senior clinicians, while they significantly outperformed junior clinicians. Based on SVR models, we proposed 3 novel linear formulas for uncuffed and cuffed ETT size respectively. CONCLUSION: We have developed machine learning models with excellent performance in predicting optimal ETT size in both cuffed and uncuffed endotracheal intubation in pediatric patients, which provides powerful decision support for clinicians to select proper ETT size. Novel formulas proposed based on machine learning models also have relatively better predictive performance. These models and formulas can serve as important clinical references for clinicians, especially for performers with rare experience or in remote areas. |
format | Online Article Text |
id | pubmed-9631215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96312152022-11-04 Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models Zhou, Miao Xu, Wen.Y. Xu, Sheng Zang, Qing L. Li, Qi Tan, Li Hu, Yong C. Ma, Ning Xia, Jian H. Liu, Kun Ye, Min Pu, Fei Y. Chen, Liang Song, Li J. Liu, Yang Jiang, Lai Gu, Lin Zou, Zui Front Pediatr Pediatrics OBJECTIVE: We aimed to construct and validate machine learning models for endotracheal tube (ETT) size prediction in pediatric patients. METHODS: Data of 990 pediatric patients underwent endotracheal intubation were retrospectively collected between November 2019 and October 2021, and separated into cuffed and uncuffed endotracheal tube subgroups. Six machine learning algorithms, including support vector regression (SVR), logistic regression (LR), random forest (RF), gradient boosting tree (GBR), decision tree (DTR) and extreme gradient boosting tree (XGBR), were selected to construct and validate models using ten-fold cross validation in training set. The optimal models were selected, and the performance were compared with traditional predictive formulas and clinicians. Furthermore, additional data of 71 pediatric patients were collected to perform external validation. RESULTS: The optimal 7 uncuffed and 5 cuffed variables were screened out by feature selecting. The RF models had the best performance with minimizing prediction error for both uncuffed ETT size (MAE = 0.275 mm and RMSE = 0.349 mm) and cuffed ETT size (MAE = 0.243 mm and RMSE = 0.310 mm). The RF models were also superior in predicting power than formulas in both uncuffed and cuffed ETT size prediction. In addition, the RF models performed slightly better than senior clinicians, while they significantly outperformed junior clinicians. Based on SVR models, we proposed 3 novel linear formulas for uncuffed and cuffed ETT size respectively. CONCLUSION: We have developed machine learning models with excellent performance in predicting optimal ETT size in both cuffed and uncuffed endotracheal intubation in pediatric patients, which provides powerful decision support for clinicians to select proper ETT size. Novel formulas proposed based on machine learning models also have relatively better predictive performance. These models and formulas can serve as important clinical references for clinicians, especially for performers with rare experience or in remote areas. Frontiers Media S.A. 2022-10-20 /pmc/articles/PMC9631215/ /pubmed/36340734 http://dx.doi.org/10.3389/fped.2022.970646 Text en © 2022 Zhou, Xu, Xu, Zang, Li, Tan, Hu, Ma, Xia, Liu, Ye, Pu, Chen, Song, Liu, Jiang, Gu and Zou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pediatrics Zhou, Miao Xu, Wen.Y. Xu, Sheng Zang, Qing L. Li, Qi Tan, Li Hu, Yong C. Ma, Ning Xia, Jian H. Liu, Kun Ye, Min Pu, Fei Y. Chen, Liang Song, Li J. Liu, Yang Jiang, Lai Gu, Lin Zou, Zui Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models |
title | Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models |
title_full | Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models |
title_fullStr | Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models |
title_full_unstemmed | Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models |
title_short | Prediction of endotracheal tube size in pediatric patients: Development and validation of machine learning models |
title_sort | prediction of endotracheal tube size in pediatric patients: development and validation of machine learning models |
topic | Pediatrics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9631215/ https://www.ncbi.nlm.nih.gov/pubmed/36340734 http://dx.doi.org/10.3389/fped.2022.970646 |
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