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

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), 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 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.

Keywords

• airway management
• demography
• general anesthesia
• intratracheal intubation
• machine learning
• pediatrics