Cargando…
Predictors for extubation failure in COVID-19 patients using a machine learning approach
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 t...
Ejemplares similares
-
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
por: Fleuren, Lucas M., et al.
Publicado: (2021) -
Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse
por: Fleuren, Lucas M., et al.
Publicado: (2021) -
Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records
por: Vagliano, Iacopo, et al.
Publicado: (2022) -
Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome
por: Dam, Tariq A., et al.
Publicado: (2021) -
Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in‐hospital mortality
por: Plečko, Drago, et al.
Publicado: (2021)