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

Abstract

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.

Effect of different head-high lateral extubation on adverse reactions in the peri-extubation period of pediatric OSAS surgery under general anesthesia

“…3 to 5 min to be perfected by muscle relaxation and then tracheal intubation was performed under visual laryngoscope. (catheter diameter (with sleeve) = age/4 + 4; catheter insertion depth through the mouth = age/2 + 12 cm) [ 13 ]. After auscultation of clear and symmetrical respiratory sounds in both lungs, the cannula was properly fixed and connected to the anesthesia machine to control respiration, ventilation mode: volume-controlled ventilation, inspired oxygen concentration 50%, tidal volume 8-10 ml/kg, minute ventilation volume 100-200 ml/kg, respiratory rate 14-18breaths/min, inspiration-expiration ratio 1:2, peak inspiratory pressure 12-20cmH 2 O (the upper-pressure limit should not exceed the maximum 28cmH 2 O), end-tidal carbon dioxide (ETCO 2 ) is maintained at 35-45 mmHg, and respiratory rate and tidal volume are adjusted according to ETCO 2 [ 14 ].…”

Advances in pediatric perioperative care using artificial intelligence

“…Similarly, machine learning techniques (artificial neural network, random forest, elastic net and support vector machine) have been used to predict optimal depth of tracheal tube tip using data such as age, weight, height and gender [29]. This can guide physicians when planning for appropriate airway management, and may ultimately reduce the frequency of unintentional endobronchial intubations.…”

Predicting the risk of inappropriate depth of endotracheal intubation in pediatric patients using machine learning approaches

“…An endotracheal intubation training program using virtual reality (VR) has been developed in airway management by applying an ML technique through user feedback 21 . In addition, our previous study demonstrated that ML could predict the appropriate intubation depth more accurately than the conventional formula-based method in pediatric patients 22 . However, the sample size used in the study was very small.…”