scholarly journals Airway Management in Critical Settings

2020 ◽  
Author(s):  
ELSayed Elkarta ◽  
Magdy Eldegwy
Keyword(s):  
Gas Exchange ◽  
Airway Management ◽  
Blood Sample ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Oxygen Therapy ◽  
Ventilatory Support ◽  
Arterial Blood Sample ◽  
Arterial Blood ◽  
Critical Patients

Airway management continues to be a challenging task for healthcare practitioners and when it comes to critical settings; it carries more challenges even for the skilled persons. Critical settings could be in fact of suits; where intervention takes place, equipment or practitioners taking care of airway management. Critically ill patients with multiple comorbidities, increasing oxygen demand and high respiratory work; that may require elective airway securing. Various protocols, guidelines and recommendations advocated for this task with the prospects of less hemodynamic alteration and prevention of pulmonary aspiration. In the former, starting oxygen therapy for all critical patients on admission was a routine following the concept; if some is good, more must be better. Nowadays excess oxygen may be unfavorable in some acute critical conditions e.g. ischemic strokes, post-acute myocardial infraction and those with hypercapnic respiratory failure. However, still high flow inspired oxygen concentration is the protocol until they are stable then its reduction to reach the targeted arterial oxygen saturation. Oxygen devices used for oxygen delivery are plenty and its selection depends on the many factors; airway patency, patient’s conscious level and compliance, and assessment of gas exchange based on arterial blood sample which is recommended for all critically ill patients. Early prompt evaluation of the airway and assessment of gas exchange using arterial blood sample analysis is curial in all critically ill patients to guide for subsequent oxygen supply and whether the patient needs ventilatory support or not. This chapter will focus on airway management, oxygen therapy and types of ventilatory support required for adult critically ill patients, while other situations’ airway management’s tools and skills will be discussed in another ones.

High flow oxygen therapy in adult critically ill patients

2017 ◽  
Vol 83 (4) ◽  
Author(s):  
Lorenzo Del Sorbo ◽  
Alice Vendramin ◽  
Sangeeta Mehta
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Oxygen Therapy ◽  
High Flow ◽  
High Flow Oxygen

Interpretation of Noninvasive and Invasive Information

2017 ◽  
Author(s):  
Borzoo Farhang ◽  
Erik P Anderson ◽  
Mark P Hamlin
Keyword(s):  
Critically Ill ◽  
Hemodynamic Monitoring ◽  
Critically Ill Patients ◽  
Pulse Contour Analysis ◽  
Pulse Contour ◽  
Response To Therapy ◽  
Contour Analysis ◽  
Central Venous ◽  
Arterial Blood ◽  
Pulmonary Arterial

Traditional, static measures of resuscitation, such as vital signs, central venous pressure, and pulmonary arterial pressure, provide momentary glimpses evolving hemodynamic states. In patients with shock, these measures of resuscitation are poor indicators of response to therapy. As a result, dynamic assessments of cardiovascular status are now used in critically ill patients to facilitate resuscitation. Some of these approaches focus on fluid responsiveness. These assessments allow care to be tailored to each patient’s response to interventions. An evolving aspect of hemodynamic monitoring is evaluation of the adequacy of tissue perfusion and oxygen delivery. In this review, we consider the use of arterial, central venous, and pulmonary arterial blood pressure monitoring; echocardiography; transesophageal Doppler technology; pulse contour analysis; bioimpedance and bioreactance; and partial rebreathing monitoring modalities to assess hemodynamic status in critically ill patients.  This review contains 22 figures, 5 tables, and 38 references. Key words: echocardiography, esophageal Doppler technology, invasive and noninvasive hemodynamic monitoring, pulse contour analysis, shock 

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 Dangerous hyperkalaemia after analysis of arterial blood sample.

1988 ◽  
Vol 41 (12) ◽  
pp. 1339-1339
Author(s):  
W D Neithercut ◽  
J M Orrell
Keyword(s):  
Blood Sample ◽  
Arterial Blood Sample ◽  
Arterial Blood
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