Does the endotracheal tube insertion depth predicted by formulas in children have a
            good concordance with the ideal position observed by X-ray?

Santos, Dayanna Letícia Silva; Andrade, Paulo Douglas de Oliveira; Gomes, Évelim Leal de Freitas Dantas

doi:10.5935/0103-507x.20200046

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…That height is considered the most critical factor in determining ET depth 34 . Although the trachea length and height were correlated, the rate at which the appropriate ET depth was observed was only 43% 23 , 40 . In addition, in the weight-based method proposed by Gill et al, the ET tube was located in inappropriate positions in 56% of cases, in deep positions in 52% of patients 41 .…”

Section: Resultsmentioning

confidence: 92%

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

Shim

Lee

et al. 2023

Sci Rep

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Endotracheal tube (ET) misplacement is common in pediatric patients, which can lead to the serious complication. It would be helpful if there is an easy-to-use tool to predict the optimal ET depth considering in each patient’s characteristics. Therefore, we plan to develop a novel machine learning (ML) model to predict the appropriate ET depth in pediatric patients. This study retrospectively collected data from 1436 pediatric patients aged < 7 years who underwent chest x-ray examination in an intubated state. Patient data including age, sex, height weight, the internal diameter (ID) of the ET, and ET depth were collected from electronic medical records and chest x-ray. Among these, 1436 data were divided into training (70%, n = 1007) and testing (30%, n = 429) datasets. The training dataset was used to build the appropriate ET depth estimation model, while the test dataset was used to compare the model performance with the formula-based methods such as age-based method, height-based method and tube-ID method. The rate of inappropriate ET location was significantly lower in our ML model (17.9%) compared to formula-based methods (35.7%, 62.2%, and 46.6%). The relative risk [95% confidence interval, CI] of an inappropriate ET location compared to ML model in the age-based, height-based, and tube ID-based method were 1.99 [1.56–2.52], 3.47 [2.80–4.30], and 2.60 [2.07–3.26], respectively. In addition, compared to ML model, the relative risk of shallow intubation tended to be higher in the age-based method, whereas the risk of the deep or endobronchial intubation tended to be higher in the height-based and the tube ID-based method. The use of our ML model was able to predict optimal ET depth for pediatric patients only with basic patient information and reduce the risk of inappropriate ET placement. It will be helpful to clinicians unfamiliar with pediatric tracheal intubation to determine the appropriate ET depth.

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Section: Resultsmentioning

confidence: 92%

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

Shim

Lee

et al. 2023

Sci Rep

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“…Santos and colleagues [ 26 ] evaluated the effectiveness of three different formulas using anthropometric measures to estimate the insertion depth of an endotracheal tube in children in a small cohort of children less than 2 years of age. The formulas based on height measured in cm, [(height/10) + 5], had the strongest correlation ( r = 0.88, P < 0.05, concordance correlation coefficient = 0.88) with ideal depth as determined by CXR compared to a weight-based formula or one that used the inner diameter of the ETT [ 26 ]. The correlation between the weight-based calculation, with weight measured in kg (weight + 6) and depth on CXR, was r = 0.75, P < 0.05, with a concordance correlation coefficient 0.43, whereas the correlation between endotracheal tube diameter-based calculation and depth on CXR was r = 0.80, P < 0.05, with a concordance correlation coefficient 0.78 [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…Predictive formulas based on the ETT internal diameter (3 × ETT ID mm) are limited in use to ETTs with an internal diameter larger than 3 mm and are associated with malposition rates of 15%-25% [25]. Santos and colleagues [26] evaluated the effectiveness of three different formulas using anthropometric measures to estimate the insertion depth of an endotracheal tube in children in a small cohort of children less than 2 years of age. The formulas based on height measured in cm, [(height/10) + 5], had the strongest correlation (r = 0.88, P < 0.05, concordance correlation coefficient = 0.88) with ideal depth as determined by CXR compared to a weight-based formula or one that used the inner diameter of the ETT [26].…”

Section: Development and Internal Validation Of An Ett Depth Equationmentioning

confidence: 99%

Development and internal validation of an equation using anthropometric measures to predict correct endotracheal tube insertion depth

Volsko¹,

McNinch²,

Page-Goertz³

2022

Can J Respir Ther

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Purpose: To develop, internally validate, and assess the utility of implementing a regression model for determining endotracheal tube (ETT) insertion depth. Research methods: We recorded height, weight, age, sex, ETT internal diameter (ID), lip marking, and tracheal position from the electronic record from a random subset of 2,000 intubated subjects obtained from 1 January 2009 to 5 May 2012. A multivariable linear regression model was constructed and validated by a nonparametric bootstrapping technique using unrestricted random sampling methods. A prospective pilot of subjects admitted to the pediatric intensive care unit requiring invasive mechanical ventilatory support was conducted from 7 January 2019 to 31 May 2019. Those with spinal and/or skeletal malformations, without a post-intubation chest-x-ray (CXR) order, or whose CXR quality impaired visualizing the carina and ETT tip, were excluded. The validated regression equation determined insertion depth. CXR following intubation determined ETT position. Demographic data were summarized. Two-tailed, one-sample binomial test of proportions assessed differences in the proportion of correct position by the equation. Main findings: Four hundred and seventy-seven subjects included in model construction yielded 10,000 independent samples for internal validation; 55% were female, and the mean age (SD) was 47 (63) months. Bias between bootstrap coefficients and refined model estimates were negligible (P < 0.01). Eleven subjects in the pilot were female (64%), mean age (SD) of 36.7 (38) months. Four protocol violations (36.4%) resulted in malposition. Subsequent repositioning per protocol resulted in 100% correct positioning (P = 0.01). Conclusion:The regression equation [0.8636 * (Ht. 0.6223 )] facilitated correct ETT placement. A larger, diverse sample is required for external model validation.

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“…A reliable and safe means of predicting the optimal tracheal tube depth is therefore needed [ 6 ]. Currently, effective methods to guide health professionals in real time when performing intubations are lacking [ 25 ]. Recently, a new prediction model, which analyzes computed tomography images, was proposed; however, it was only applicable to patients within a limited height range and individual differences were difficult to determine since only height or weight measures were used in the prediction equation [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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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Does the endotracheal tube insertion depth predicted by formulas in children have a good concordance with the ideal position observed by X-ray?

Cited by 8 publications

References 18 publications

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

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

Development and internal validation of an equation using anthropometric measures to predict correct endotracheal tube insertion depth

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

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