Predicting optimal endotracheal tube size and depth in pediatric patients using demographic data and machine learning techniques.

IF 4.2 4区 医学 Q1 ANESTHESIOLOGY
Korean Journal of Anesthesiology Pub Date : 2023-12-01 Epub Date: 2023-09-26 DOI:10.4097/kja.23501
Hyeonsik Kim, Hyun-Kyu Yoon, Hyeonhoon Lee, Chul-Woo Jung, Hyung-Chul Lee
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引用次数: 0

Abstract

Background: Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications.

Methods: Data from 37,057 patients younger than 12 years who underwent general anesthesia with endotracheal intubation were retrospectively analyzed. Gradient boosted regression tree (GBRT) model was developed and compared with traditional age-based formulae.

Results: The GBRT model demonstrated the highest macro-averaged F1 scores of 0.502 (95% CI 0.486, 0.568) and 0.669 (95% CI 0.640, 0.694) for predicting the uncuffed and cuffed ETT size (internal diameter [ID]), outperforming the age-based formulae that yielded 0.163 (95% CI 0.140, 0.196, P < 0.001) and 0.392 (95% CI 0.378, 0.406, P < 0.001), respectively. In predicting the ETT depth (distance from tip to lip corner), the GBRT model showed the lowest mean absolute error (MAE) of 0.71 cm (95% CI 0.69, 0.72) and 0.72 cm (95% CI 0.70, 0.74) compared to the age-based formulae that showed an error of 1.18 cm (95% CI 1.16, 1.20, P < 0.001) and 1.34 cm (95% CI 1.31, 1.38, P < 0.001) for uncuffed and cuffed ETT, respectively.

Conclusions: The GBRT model using only demographic data accurately predicted the ETT size and depth. If these results are validated, the model may be practical for predicting optimal ETT size and depth for pediatric patients.

使用人口统计数据和机器学习技术预测儿科患者的最佳气管插管尺寸和深度。
背景:在儿科患者中,使用合适尺寸和深度的气管插管(ETT)有助于最大限度地减少与插管相关的并发症。现有的用于选择最佳ETT大小的基于年龄的公式存在一些不准确之处。我们开发了一个机器学习模型,该模型使用人口统计数据预测儿科患者ETT的最佳大小和深度,从而实现临床应用。方法:回顾性分析37057例12岁以下全麻气管插管患者的临床资料。建立了梯度增强回归树(GBRT)模型,并与传统的基于年龄的公式进行了比较。结果:GBRT模型在预测未翻边和翻边ETT尺寸(内径[ID])方面表现出最高的宏观平均F1得分,分别为0.502(95%CI 0.486-0.568)和0.669(95%CI 0.640-0.694),优于基于年龄的公式,该公式分别得出0.163(95%CI 0.140-0.196,P<0.001)和0.392(95%CI 0.378-0.406,<0.001)。在预测ETT深度(从尖端到唇角的距离)时,GBRT模型显示出最低的平均绝对误差(MAE),分别为0.71厘米(95%CI 0.69-0.72)和0.72厘米(95%CI 0.70-0.74),而基于年龄的公式显示,未翻边和翻边的ETT的误差分别为1.18厘米(95%置信区间1.16-1.20,P<0.001)和1.34厘米(95%可信区间1.31-1.38,P=0.001)。结论:仅使用人口统计学数据的GBRT模型准确预测了ETT的大小和深度。如果这些结果得到验证,该模型可能适用于预测儿科患者的最佳ETT大小和深度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
6.90%
发文量
84
审稿时长
16 weeks
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