Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

ABSTRACT:

Background

For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources.

Methods

From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and E

SUBMITTER: Dam TA 

PROVIDER: S-EPMC9583049 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature