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

• Published in: PloS one Vol. 16; no. 9; p. e0257069
• Main Authors: Shim, Jae-Geum, Ryu, Kyoung-Ho, Lee, Sung Hyun, Cho, Eun-Ah, Lee, Sungho, Ahn, Jin Hee
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 02.09.2021; Public Library of Science (PLoS)
• Subjects: Age; Anesthesiology; Artificial intelligence; Artificial neural networks; Biology and Life Sciences; Children; Cohort analysis; Computer and Information Sciences; Confidence intervals; Decision trees; Diseases; Electronic health records; Health aspects; Learning algorithms; Machine learning; Medical records; Medicine; Medicine and Health Sciences; Nervous system; Neural networks; Pain; Patients; Pediatrics; Physical Sciences; Prediction models; Research and Analysis Methods; Support vector machines; Thorax; Trachea; Variables; Ventilation; Ventilators; Vertebrae; Virtual reality; X-rays; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0257069

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.