Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Department of Emergency Medicine, Seoul National University Hospital, Seoul, Korea.
Emerg Med J. 2018 May;35(5):303-308. doi: 10.1136/emermed-2017-206795. Epub 2018 Feb 6.
The purpose of this study was to construct a prediction model for endotracheal tube depth using neck CT images.
A retrospective image review was conducted that included patients who had undergone neck CT. Using sagittal neck CT images, we calculated the length between upper incisor and mid-trachea and then derived the model via regression analysis. The model was validated externally using chest radiographs of patients who had undergone endotracheal intubation. We compared performance of our model with that of other methods (Broselow tape and APLS formula) via Bland-Altman analysis and the percentage of estimations within 10% of the measured values.
A total of 1111 children were included in this study. The tube depth obtained from CT images was linearly related to body weight (tube depth (cm)=5.5+0.5×body wt (kg)) in children younger than 1 year and to height (tube depth (cm)=3+0.1×height (cm)) in children older than 1 year. External validation demonstrated that our new model showed better agreement with the desired tube depth than Broselow tape and APLS formula. The mean differences in children younger than 1 year were 0.61 cm and -1.24 cm for our formula and Broselow tape, respectively. The mean differences in children older than 1 year were -0.43 cm, -1.98 and -1.64 cm for our formula, Broselow tape and APLS formula, respectively. The percentages of estimates within 10% of the measured values were 52.7% and 35.8% for our formula and Broselow tape in children younger than 1 year, respectively, and 54.3%, 33.8% and 37.2% for our formula, Broselow tape and APLS formula in children older than 1 year, respectively (P<0.01).
Our new formula is useful and more accurate than the currently available methods.
本研究旨在构建一种基于颈部 CT 图像预测气管导管深度的模型。
本研究回顾性分析了接受颈部 CT 检查的患者的图像。我们通过计算上切牙到气管中段的矢状位颈部 CT 图像长度,然后通过回归分析得出模型。该模型通过对已行气管插管的患者的胸部 X 线片进行外部验证。我们通过 Bland-Altman 分析和估计值与实测值相差 10%以内的百分比比较了我们的模型与其他方法(Broselow 色码带和 APLS 公式)的性能。
本研究共纳入 1111 例患儿。1 岁以下患儿的 CT 图像上导管深度与体重呈线性关系(导管深度(cm)=5.5+0.5×体重(kg)),1 岁以上患儿与身高呈线性关系(导管深度(cm)=3+0.1×身高(cm))。外部验证表明,与 Broselow 色码带和 APLS 公式相比,我们的新模型与所需导管深度的一致性更好。1 岁以下患儿的平均差值分别为 0.61cm 和-1.24cm,分别为我们的公式和 Broselow 色码带。1 岁以上患儿的平均差值分别为-0.43cm、-1.98cm 和-1.64cm,分别为我们的公式、Broselow 色码带和 APLS 公式。1 岁以下患儿的估计值与实测值相差 10%以内的百分比分别为 52.7%和 35.8%,分别为我们的公式和 Broselow 色码带。1 岁以上患儿的百分比分别为 54.3%、33.8%和 37.2%,分别为我们的公式、Broselow 色码带和 APLS 公式(P<0.01)。
我们的新公式比现有的方法更有用、更准确。