Department of Anesthesiology and Pain Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemoonan-Ro, Jongro-Gu, Seoul, 03181, Republic of Korea.
Department of Anesthesiology and Pain Medicine, Chung-Ang University Hospital, Chung-Ang University School of Medicine, Seoul, Republic of Korea.
Sci Rep. 2023 Mar 29;13(1):5156. doi: 10.1038/s41598-023-32122-5.
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.
气管内导管（ET）位置不当在儿科患者中很常见，可导致严重并发症。如果有一种易于使用的工具可以根据每位患者的特点预测最佳 ET 深度，将有所帮助。因此，我们计划开发一种新的机器学习（ML）模型来预测儿科患者的合适 ET 深度。
这项研究回顾性地收集了 1436 名年龄<7 岁的接受气管插管状态下胸部 X 线检查的儿科患者的数据。从电子病历和胸部 X 线中收集了患者数据，包括年龄、性别、身高、体重、ET 内径（ID）和 ET 深度。其中，1436 项数据分为训练（70%，n=1007）和测试（30%，n=429）数据集。训练数据集用于构建合适的 ET 深度估计模型，而测试数据集用于将模型性能与基于公式的方法（如年龄法、身高法和管-ID 法）进行比较。
与基于公式的方法（35.7%、62.2%和 46.6%）相比，我们的 ML 模型（17.9%）中不合适的 ET 位置的发生率明显更低。与 ML 模型相比，年龄、身高和管 ID 方法中不合适 ET 位置的相对风险[95%置信区间，CI]分别为 1.99[1.56-2.52]、3.47[2.80-4.30]和 2.60[2.07-3.26]。此外，与 ML 模型相比，年龄法中浅插管的相对风险较高，而身高法和管 ID 法中深插管或支气管内插管的风险较高。
仅使用基本的患者信息，我们的 ML 模型就能预测儿科患者的最佳 ET 深度，降低不合适的 ET 放置风险。这将有助于不熟悉小儿气管插管的临床医生确定合适的 ET 深度。
