The FDA has granted de novo authorization to Fifth Eye Inc. for its Analytic for Hemodynamic Instability (AHI), a machine learning (ML)-based, real-time indicator of patient deterioration. Commercialization of the software device, which continuously monitors patients with an electrocardiogram (ECG) for signs of deterioration, got underway on March 1.
As COVID-19 testing remains elusive in the U.S., much of the nation’s focus has started to shift to how to treat the presumed millions of patients who are already or soon to be infected with the novel coronavirus.
Swiss researchers set out to identify and analyze vital sign data that could offer a window into predicting circulatory failure, which could enable more effective prevention of catastrophic events in the ICU.
Deciding which patients should go into the intensive care unit (ICU) after surgery is a difficult call and typically made entirely at the surgeon's discretion. The result is that surgeons typically err on the side of caution by putting more post-operative patients in the ICU than necessary. To aid in better ICU decision-making, physicians at New York University Langone Hospital System (NYU Langone) developed a machine learning algorithm that combs through a patient's electronic medical record to identify relevant factors to determine if they needed the ICU after surgery.