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Using neighborhood and neighborhood data in combination with existing data sources creates a more precise prediction on a patient’s recovery prospects after an out-of-hospital cardiac arrest (OHCA), according to preliminary study to be presented in the American Heart Association’s Resuscitation Science Symposium 2020. The 2020 meeting will be held virtually, November 14-16, and will feature the most recent advances related to handling cardiopulmonary arrest and life-threatening traumatic injury.
This is exciting. We were able to provide a machine learning model with information from publicly available, real-world sources that helped us find patterns that might be otherwise unseen, therefore, yielding better results. This strategy has the potential to be helpful in more accurately predicting other clinical outcomes in future studies.”-Samuel Harford, M.S., study’s lead author, a Ph.D. candidate in the department of mechanical and industrial engineering at the University of Illinois at Chicago
The machine learning algorithms were developed and tested on nearly 10,000 cases of OHCA that happened in Chicago’s 77 neighborhoods between 2014 and 2019. Researchers used OHCA data from the existing Cardiac Arrest Registry to Enhance Survival (CARES) database to identify incidents that happened outside of a health care system or a nursing home facility around the Chicago region. Info about individual communities from the Chicago Health Atlas (CHA), including crime rates, accessibility to healthcare and education, was subsequently added.
The addition of this CHA information raised the average recall of OHCA survival predictions from 84.5 to nearly 87%.
The study had limitations dependent on the standard of information, and more information that could impact the results like traffic, weather, EMS paths and socioeconomic status still have to be analyzed.
American Heart Association
