Machine-learning system could aid critical decisions in sepsis care
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. That’s where things can get tricky. Administering fluids for too long may not...