Oxygen exposure as quantified by time-weighted area under curve for arterial oxygen content is associated with mortality in mechanically ventilated critically ill patients

2020 ◽  
Vol 7 (2) ◽  
pp. 197
Author(s):  
JamesP Harvey ◽  
DayalG Jayawardena ◽  
Mahesh Ramanan
Keyword(s):  
Critically Ill ◽  
Oxygen Content ◽  
Critically Ill Patients ◽  
Area Under Curve ◽  
Arterial Oxygen ◽  
Mechanically Ventilated ◽  
Arterial Oxygen Content ◽  
Oxygen Exposure

scholarly journals Non-cardio respiratory monitoring of mechanically ventilated critically ill patients

2019 ◽  
pp. S150-S159
Author(s):  
Chinmaya Kumar Panda ◽  
Habib Mohammad Reazaul Karim ◽  
Subrata Kumar Singha
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Well Being ◽  
Multiple Organ ◽  
Respiratory Monitoring ◽  
Arterial Oxygen ◽  
Mechanically Ventilated

Critically ill patients often require multiple organ supports; respiratory support in terms of mechanical ventilation (MV) is one of the commonest. But, only providing an organ support contributes less to the complete well being of the patients. Moreover, MV itself can affect various physiological systems, metabolic response, and cause side effects. A very close temporal relationship exists between patients, monitoring and management decision too, and therefore, appropriate information from monitoring can lead to better outcomes. The present review is intended to briefly highlight the current opinions and strategies for non cardio-respiratory monitoring in such critically ill patients.Abbreviations: AKI-Acute Kidney Injury; APACHE-Acute Physiology and Chronic Health Evaluation; BPS-Behavioral Pain Scale; CAM-ICU-Confusion Assessment Method for the Intensive Care Unit; CPOT–Critical Care Pain Observation Tool; EVLWI-Extra vascular lung water index; FDA-Food and Drug Administration; ISO-International Organization for Standardization; ICU-Intensive Care Unit; LOS-Length of stay; MODS-Multiple Organ Dysfunction Score; MV-Mechanical Ventilation; PaO2-Partial pressure of arterial oxygen; FiO2-Fraction of inspired oxygen; SAPS-Simplified Acute Physiologic Score; RASS-Richmond Agitation Sedation Scale; SOFA-Sequential Organ Failure Assessment; SAS-Sedation Agitation Scale; UO-Urine outputCitation: Panda CK, Karim HMR, Singha SK. Non-cardio respiratory monitoring of mechanically ventilated critically ill patients. Anaesth Pain & Intensive Care 2018;22 Suppl 1:S150-S159Received: 9 Jul 2018 Reviewed: 1 Oct 2018 Corrected & Accepted: 9 Oct 2018

scholarly journals Etiology, incidence, and outcomes of patient–ventilator asynchrony in critically-ill patients undergoing invasive mechanical ventilation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yongfang Zhou ◽  
Steven R. Holets ◽  
Man Li ◽  
Gustavo A. Cortes-Puentes ◽  
Todd J. Meyer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mechanical Ventilation ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Invasive Mechanical Ventilation ◽  
Surgical Intensive Care ◽  
Mechanically Ventilated ◽  
History Of ◽  
Training Background ◽  
The Relationship

AbstractPatient–ventilator asynchrony (PVA) is commonly encountered during mechanical ventilation of critically ill patients. Estimates of PVA incidence vary widely. Type, risk factors, and consequences of PVA remain unclear. We aimed to measure the incidence and identify types of PVA, characterize risk factors for development, and explore the relationship between PVA and outcome among critically ill, mechanically ventilated adult patients admitted to medical, surgical, and medical-surgical intensive care units in a large academic institution staffed with varying provider training background. A single center, retrospective cohort study of all adult critically ill patients undergoing invasive mechanical ventilation for ≥ 12 h. A total of 676 patients who underwent 696 episodes of mechanical ventilation were included. Overall PVA occurred in 170 (24%) episodes. Double triggering 92(13%) was most common, followed by flow starvation 73(10%). A history of smoking, and pneumonia, sepsis, or ARDS were risk factors for overall PVA and double triggering (all P < 0.05). Compared with volume targeted ventilation, pressure targeted ventilation decreased the occurrence of events (all P < 0.01). During volume controlled synchronized intermittent mandatory ventilation and pressure targeted ventilation, ventilator settings were associated with the incidence of overall PVA. The number of overall PVA, as well as double triggering and flow starvation specifically, were associated with worse outcomes and fewer hospital-free days (all P < 0.01). Double triggering and flow starvation are the most common PVA among critically ill, mechanically ventilated patients. Overall incidence as well as double triggering and flow starvation PVA specifically, portend worse outcome.

scholarly journals Selection strategy for sedation depth in critically ill patients on mechanical ventilation

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Fengxiang Chang ◽  
Bo Tang ◽  
Lin Han ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Dimensionality Reduction ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Latent Profile Analysis ◽  
Profile Analysis ◽  
Mechanically Ventilated ◽  
Mechanically Ventilated Patients ◽  
Sedation And Analgesia ◽  
Ventilated Patients

Abstract Background Analgesia and sedation therapy are commonly used for critically ill patients, especially mechanically ventilated patients. From the initial nonsedation programs to deep sedation and then to on-demand sedation, the understanding of sedation therapy continues to deepen. However, according to different patient’s condition, understanding the individual patient’s depth of sedation needs remains unclear. Methods The public open source critical illness database Medical Information Mart for Intensive Care III was used in this study. Latent profile analysis was used as a clustering method to classify mechanically ventilated patients based on 36 variables. Principal component analysis dimensionality reduction was used to select the most influential variables. The ROC curve was used to evaluate the classification accuracy of the model. Results Based on 36 characteristic variables, we divided patients undergoing mechanical ventilation and sedation and analgesia into two categories with different mortality rates, then further reduced the dimensionality of the data and obtained the 9 variables that had the greatest impact on classification, most of which were ventilator parameters. According to the Richmond-ASS scores, the two phenotypes of patients had different degrees of sedation and analgesia, and the corresponding ventilator parameters were also significantly different. We divided the validation cohort into three different levels of sedation, revealing that patients with high ventilator conditions needed a deeper level of sedation, while patients with low ventilator conditions required reduction in the depth of sedation as soon as possible to promote recovery and avoid reinjury. Conclusion Through latent profile analysis and dimensionality reduction, we divided patients treated with mechanical ventilation and sedation and analgesia into two categories with different mortalities and obtained 9 variables that had the greatest impact on classification, which revealed that the depth of sedation was limited by the condition of the respiratory system.

Blood flow to fetal organs as a function of arterial oxygen content

1979 ◽  
Vol 135 (5) ◽  
pp. 637-646 ◽  
Author(s):  
Louis L.H. Peeters ◽  
Roger E. Sheldon ◽  
M. Douglas Jones ◽  
Edgar L. Makowski ◽  
Giacomo Meschia
Keyword(s):  
Blood Flow ◽  
Oxygen Content ◽  
Arterial Oxygen ◽  
Arterial Oxygen Content ◽  
Fetal Organs
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