Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study.

ABSTRACT:

Background

Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care.

Objective

This study aimed to develop and validate prediction models for UE in ICU patients using machine learning.

Methods

This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and

SUBMITTER: Hur S 

PROVIDER: S-EPMC8387891 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature