Predictors for extubation failure in COVID-19 patients using a machine learning approach.

ABSTRACT:

Introduction

Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19.

Methods

We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primar

SUBMITTER: Fleuren LM 

PROVIDER: S-EPMC8711075 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature