scholarly journals An Alternative to Ventilators to Support Critical COVID-19 Patients

2020 ◽  
Author(s):  
Gang Pan ◽  
Tao Lyu ◽  
John Hunt
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Pure Water ◽  
Bubble Formation ◽  
Saturation Pressure ◽  
Physiological Saline ◽  
Oxygen Solubility ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Gaseous Bubble

Critically ill patients with COVID-19 may develop serious respiratory difficulties, causing a significant reduction in blood oxygen saturation pressure. Physical Ventilation of the lungs is one of the main methods to help critically ill patients through the acute phase of infection. During extreme situations when dysfunctional lungs are filled with sticky sputum in the alveolus or when there are simply not enough ventilators to match the need, the mortality rate can be dramatically increased. Here, we propose an intravenous injection method that may increase and maintain the blood oxygen pressure at normal levels. The intravenous (IV) infusion contains oxygen nanobubbles in physiological saline solution (ONPS), in which the dissolved oxygen content can be 2-6 times higher than the normal oxygen solubility in pure water. This makes it possible to oxygenate blood with a small limited volume of IV fluid without the risk of gaseous bubble formation in blood vessels.

Central venous to mixed venous blood oxygen and lactate gradients are associated with outcome in critically ill patients

2008 ◽  
Vol 34 (9) ◽  
pp. 1662-1668 ◽  
Author(s):  
Guillermo Gutierrez ◽  
Pablo Comignani ◽  
Luis Huespe ◽  
F. Javier Hurtado ◽  
Arnaldo Dubin ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Venous Blood ◽  
Mixed Venous Blood ◽  
Blood Oxygen ◽  
Central Venous ◽  
Mixed Venous

scholarly journals SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO2 Waveform Prediction

2021 ◽  
Author(s):  
Akshaya V. Annapragada ◽  
Joseph L. Greenstein ◽  
Sanjukta N. Bose ◽  
Bradford D. Winters ◽  
Sridevi V. Sarma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Optimal Resource Allocation ◽  
Blood Oxygen Saturation ◽  
Optimal Resource ◽  
Deep Learning Model

AbstractHypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT provides information on both occurrence and magnitude of potential hypoxemic events 30 minutes in advance, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

scholarly journals SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

2021 ◽  
Vol 17 (12) ◽  
pp. e1009712
Author(s):  
Akshaya V. Annapragada ◽  
Joseph L. Greenstein ◽  
Sanjukta N. Bose ◽  
Bradford D. Winters ◽  
Sridevi V. Sarma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Blood Oxygen Saturation ◽  
The Future ◽  
Optimal Resource ◽  
Deep Learning Model

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

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