Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data.

ABSTRACT:

Rationale

Duration of mechanical ventilation is associated with adverse outcomes in critically ill patients and increased use of resources. The increasing complexity of medication regimens has been associated with increased mortality, length of stay, and fluid overload but has never been studied specifically in the setting of mechanical ventilation.

Objective

The purpose of this analysis was to develop prediction models for mechanical ventilation duration to test the hypothesis that incorporating medication data may improve model performance.

Methods

This was a retrospective cohort study of adults admitted to the ICU and undergoing mechanical ventilation for longer than 24 hours from October 2015 to October 2020. Patients were excluded if it was not their index ICU ad

SUBMITTER: Sikora A 

PROVIDER: S-EPMC10543219 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature