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Machine-learning system could aid critical decisions in sepsis care

 

Researchers at MIT and MGH, in a new paper first-authored by HST student Varesh Prasad and co-authored by Professor Thomas Heldt, unveil a model for predicting whether ER sepsis patients need life-saving, but potentially harmful, vasopressors. Read more in the MIT News story below.

By Rob Matheson, MIT News Office

Researchers from MIT and Massachusetts General Hospital (MGH) have developed a predictive model that could guide clinicians in deciding when to give potentially life-saving drugs to patients being treated for sepsis in the emergency room.

Sepsis is one of the most frequent causes of admission, and one of the most common causes of death, in the intensive care unit. But the vast majority of these patients first come in through the ER. Treatment usually begins with antibiotics and intravenous fluids, a couple liters at a time. If patients don’t respond well, they may go into septic shock, where their blood pressure drops dangerously low and organs fail. Then it’s often off to the ICU, where clinicians may reduce or stop the fluids and begin vasopressor medications such as norepinephrine and dopamine, to raise and maintain the patient’s blood pressure.

Date: 
Wednesday, November 7, 2018