A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité – Universitätsmedizin Berlin, Germany, and colleagues. Healthcare systems around the world are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19. In the new study, researchers studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centers in Germany and Austria. A machine learning approach was used to find associations between the measured proteins and patient survival.

Levels of 14 proteins in the blood of critically ill COVID-19 patients are associated with survival. A single blood sample from a critically ill COVID-19 patient can be analyzed by a machine learning model which uses blood plasma proteins to predict survival, weeks before the outcome, according to a new study published this week in the open-access journal PLOS Digital Health by Florian Kurth and Markus Ralser of the Charité – Universitätsmedizin Berlin, Germany, and colleagues. Healthcare systems around the world are struggling to accommodate high numbers of severely ill COVID-19 patients who need special medical attention, especially if they are identified as being at high risk. Clinically established risk assessments in intensive care medicine, such as the SOFA or APACHE II, show only limited reliability in predicting future disease outcomes for COVID-19. In the new study, researchers studied the levels of 321 proteins in blood samples taken at 349 timepoints from 50 critically ill COVID-19 patients being treated in two independent health care centers in Germany and Austria.

Critical Care Suite 2.0 helps bedside staff and radiologists assess Endotracheal Tube (ETT) placement for intubated patients, including critical COVID-19 patients GE Healthcare announced a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients. The AI solution is one of five included in GE Healthcare's Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization and quality control. Research shows that up to 25 percent of patients intubated outside of the operating room have misplaced ETTs on chest x-rays, which can lead to severe complications for patients, including hyperinflation, pneumothorax, cardiac arrest and death. Moreover, as COVID-19 cases climb, with more than 50 million confirmed worldwide, anywhere from 5-15 percent require intensive care surveillance and intubation for ventilatory support. "Today, clinicians are overwhelmed, experiencing mounting pressure as a result of an ever-increasing number of patients," said Jan Makela, President and CEO, Imaging at GE Healthcare.