Over the years working in the neurocritical ICU, Dr. Soojin Park recognized a problem: She knew that 30 to 40% of her patients were at risk for stroke in the weeks following an aneurysmal subarachnoid hemorrhage, but it was still difficult to determine which patients were most likely to develop additional problems, like a delayed cerebral ischemia, and treat them accordingly. So, Dr. Park used her background in data science to develop a tool that can better predict which specific patients were at increased risk. The COSMIC score utilizes machine learning, and basic patient data such as blood pressure and heart rate, to predict likely outcomes, and improve targeted patient care in the neurocritical ICU.
Monitoring patients with aneurysmal rupture for delayed cerebral ischemia was historically a numbers game. It was difficult for doctors to predict outcomes in the weeks that followed their rupture, so at-risk patients could find themselves under observation in the ICU anywhere from 7 to 21 days. Dr. Soojin Park, Medical Director of Critical Care Data Science and AI at NewYork-Presbyterian/Columbia, knew there had to be a better way to monitor patients and predict outcomes. So, relying on her background in machine learning and leveraging vast amounts of data, Dr. Park developed the potentially game-changing Continuous Monitoring Tool for Delayed Cerebral Ischemia (or COSMIC) score. The score uses machine learning, and basic patient data that can be collected with equipment available at any hospital, to detect signals that more accurately assess risk, allowing doctors to treat each neurocritical patient with targeted care - ultimately improving outcomes and patient experience.
