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Machine Learning Model Could Predict Outcomes Following Cardiac Arrest


A novel machine learning model could help predict mortality and neurological outcomes post-cardiac arrest, according to a new Johns Hopkins study. Presented at the Society of Critical Care Medicine's 49th Annual Critical Care Congress in Orlando, FL, study results indicate the new model demonstrated significantly improved prediction capabilities compared to the reference APACHE model. "The objectives of our study were to first predict the neurological outcome and mortality at discharge using data only from the first 24 hours of ICU admission and the second objective was to determine whether utilizing physiologic time series (PTS) data, specifically just features from the bedside monitoring data, are useful in terms of model performance," said lead investigator Hanbiehn Kim, MBE, of Johns Hopkins University, during his presentation. Using the Philips eICU database, which includes over 200,000 patients from 208 hospitals, Kim and colleagues from Johns Hopkins Hospital extracted data on cardiac arrest patients who were mechanically ventilated. Of note, this database includes PTS data from patient bedside bio-monitors that recorded heart rate, oxygen saturation, blood pressure, and respiratory rate at 5-minute intervals.