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 0.6

SUBMITTER: Kim H 

PROVIDER: S-EPMC10718635 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature