scholarly journals Adverse events experienced while transferring the critically ill patient from the emergency department to the intensive care unit

2006 ◽  
Vol 23 (11) ◽  
pp. 858-861 ◽  
Author(s):  
L Gillman ◽  
G Leslie ◽  
T Williams ◽  
K Fawcett ◽  
R Bell ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Adverse Events ◽  
Critically Ill ◽  
Critically Ill Patient

Bedside Thoracic Ultrasonography for the Critically Ill Patient: From the Emergency Department to the Intensive Care Unit

2020 ◽  
Vol 39 (3) ◽  
pp. 215-228
Author(s):  
María C. Arango-Granados ◽  
Luis A. Bustamante Cristancho ◽  
Virginia Zarama Córdoba
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patient

Pleural Disease in the Intensive Care Unit

2000 ◽  
Vol 15 (2) ◽  
pp. 63-89
Author(s):  
Michael A. Jantz ◽  
Steven A. Sahn
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pleural Effusion ◽  
Critically Ill ◽  
Bronchopleural Fistula ◽  
Tension Pneumothorax ◽  
Respiratory Physiology ◽  
Critically Ill Patient ◽  
Pleural Disease ◽  
The Impact

Pleural disease itself is an unusual cause for admission to the intensive care unit (ICU). Pleural complications of diseases and procedures in the ICU are common, however, and the impact on respiratory physiology is additive to that of the underlying cardiopulmonary disease. Pleural effusion and pneumothorax may be overlooked in the critically ill patient due to alterations in radiologic appearance in the supine patient. The development of a pneumothorax in a patient in the ICU represents a potentially life-threatening situation. This article reviews the etiologies, pathophysiology, and management of pleural effusion, pneumothorax, tension pneumothorax, and bronchopleural fistula in the critically ill patient. In addition, we review the potential complications of thoracentesis and chest tube thoracostomy, including re-expansion pulmonary edema.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

scholarly journals The psychological aspects of intensive care units

Curationis ◽  
1982 ◽  
Vol 5 (3) ◽  
Author(s):  
G. Dannenfeldt
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Critically Ill ◽  
Nursing Care ◽  
Psychological Problems ◽  
Psychological Aspects ◽  
Critically Ill Patient ◽  
Physical Care

The technical and physical care of the critically ill patient has been perfected, but the psychological aspects of intensive nursing care have to a greater or lesser extent been neglected. The objective of this article is to highlight the causes of psychological problems in an intensive care unit, how to recognise these problems and above all how to prevent or correct them.

Sign in / Sign up

Export Citation Format

Share Document