Utilizes a lightweight algorithm and machine learning collected for 8 variables during the first 48 hours of hospitalization to predict the risk of 6-month mortality. Researchers at the University of Minnesota have developed a lightweight algorithm using machine learning for predicting the risk of 6-month mortality at the time of hospital admission. Using just 8 different variables collected during the first 48 hours of hospitalization, this algorithm predicted death within 6-months with an AUC of 0.92. The discriminative ability of this algorithm has been shown to be significantly better than historical estimates of clinician performance. This algorithm can be a critical tool in supporting clinical decision-making at admission and in evaluating suitable options such as transfer to tertiary referral center, serious illness care-conversations in high-risk patients, patient/family counseling, and palliative care utilization.
Due to the rapidly growing use of opioids and its analogs (and the increase of fatal drug overdoses), Narcan is rapidly becoming the new Kleenex. Vaccines offer a promising treatment alternative to counteract opioid use disorders (OUD) and fatal overdoses. Vaccines generate opioid-specific antibodies that bind the target opioid, reducing the drug's impact on the brain and preventing the drug-induced effects. Futura is developing vaccines, antibody-based strategies, and small molecules to counteract opioid use disorders and overdose. These approaches can improve the lives of people living with substance use disorders or who are at risk of fatal overdoses. The team is testing the first in-human vaccine against the euphoric and toxic effects of oxycodone and have initiated Phase I Clinical Trials. Futura and UMN is seeking a leader co-found the venture with Dr. Pravetoni.