Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.

ABSTRACT:

Background

Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).

Methods

Patients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimiza

SUBMITTER: Wang H 

PROVIDER: S-EPMC9358918 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature