Deployable machine learning-based decision support system for tracheostomy in acute burn patients.

ABSTRACT:

Background

Airway obstruction is a common emergency in acute burns with high mortality. Tracheostomy is the most effective method to keep patency of airway and start mechanical ventilation. However, the indication of tracheostomy is challenging and controversial. We aimed to develop and validate a deployable machine learning (ML)-based decision support system to predict the necessity of tracheostomy for acute burn patients.

Methods

We enrolled 1011 burn patients from Southwest Hospital (2018-20) for model development and feature selection. The final model was validated on an independent internal cross-temporal cohort (2021, n = 274) and an external cross-institutional cohort (Second Affiliated Hospital of Zhejiang University School of Medicine 2020-21, n = 376)

SUBMITTER: Li H 

PROVIDER: S-EPMC12070481 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature