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
2000 ◽  
Vol 15 (2) ◽  
pp. 63-89
Author(s):  
Michael A. Jantz ◽  
Steven A. Sahn

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.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  

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.


Curationis ◽  
1982 ◽  
Vol 5 (3) ◽  
Author(s):  
G. Dannenfeldt

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.


2020 ◽  
Vol 70 (4) ◽  
pp. 1851
Author(s):  
K. PAVLIDOU ◽  
I. SAVVAS

In the last decade, attempts to improve the quality of the services provided to the critically ill patients in the Intensive Care Unit (ICU) are of great interest in human medicine. The aim of the majority of the clinical studies is the correlation of the survival rate of a critically ill patient with specific prognostic factors at the time of admission. The detailed assessment of a patient at admission in the ICU and during hospitalization seems to affect the management and the outcome. The main aim of this study was to evaluate if the trans-diaphragmatic pressure measurement can be a prognostic factor of the outcome in the ICU in dogs. Thirty-one dogs, 21 male and 10 female was included in this prospective, cohort study. Age, breed, sex, body weight and clinical diagnosis were recorded. The type of admission, the mentation status, physiological and biochemical parameters were measured at the admission of the dog in the ICU. All the variables were assessed over the first 24 hours following ICU admission. The animals were allocated into sixgroups: peritonitis/intra-abdominal surgery, intra-thoracic surgery, respiratory disease, neurologic disease, neoplasia, and systematic disease. The trans-diaphragmatic pressure (Pdi) was measured under the same anesthetic level in all animals with two oesophageal balloon catheters. The most frequent problem for admission in ICU was peritonitis (5/31). Seventeen out of 31 were admitted in acute status while 14/31 had a chronic problem. Mean±standard deviation of Pdi was 10.7±5.6 mmHg and of lactate concentration 2.3±1.2 mmol/L. Both, they can predict outcome (p=0.071 and p=0.076, respectively). Seven out of 31 dogs died, 2 were euthanized and 22 were discharged from the ICU after hospitalization. The technique of Pdi measurement with balloon catheters can be successfully applied in dogs in the ICU. Pdi measurement, as well as lactate concentration may be used as prognostic indicators for the outcome, in dogs in the ICU. However, a bigger sample size is need to support these findings.


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