A functional gradient boosting machine learning algorithm predicted cardiac arrest one hour prior to the event with an AUROC of 0.85 (95% CI 0.79-0.90), similar to a logistic regression model.
Case-Control
No
Does a novel machine learning algorithm improve the prediction of cardiac arrest compared to traditional logistic regression in children admitted to a pediatric cardiac intensive care unit?
A novel machine learning algorithm using electronic health record data can predict cardiac arrest in pediatric CICU patients with high accuracy (AUROC 0.85), though it performs similarly to traditional logistic regression.
Effect estimate: AUROC 0.85 (95% CI 0.79-0.90)
BACKGROUND: Children with congenital and acquired heart disease are at a higher risk of cardiac arrest compared to those without heart disease. Although the monitoring of cardiopulmonary resuscitation quality and extracorporeal resuscitation technologies have advanced, survival after cardiac arrest in this population has not improved. Cardiac arrest prevention, using predictive algorithms with machine learning, has the potential to reduce cardiac arrest rates. However, few studies have evaluated the use of these algorithms in predicting cardiac arrest in children with heart disease. METHODS: We collected demographic, laboratory, and vital sign information from the electronic health records (EHR) of all the patients that were admitted to a single-center pediatric cardiac intensive care unit (CICU), between 2010 and 2019, who had a cardiac arrest during their CICU admission, as well as a comparator group of randomly selected non-cardiac-arrest controls. We compared traditional logistic regression modeling against a novel adaptation of a machine learning algorithm (functional gradient boosting), using time series data to predict the risk of cardiac arrest. RESULTS: A total of 160 unique cardiac arrest events were matched to non-cardiac-arrest time periods. Using 11 different variables (vital signs and laboratory values) from the EHR, our algorithm's peak performance for the prediction of cardiac arrest was at one hour prior to the cardiac arrest (AUROC of 0.85 0.79,0.90), a performance that was similar to our previously published multivariable logistic regression model. CONCLUSIONS: Our novel machine learning predictive algorithm, which was developed using retrospective data that were collected from the EHR and predicted cardiac arrest in the children that were admitted to a single-center pediatric cardiac intensive care unit, demonstrated a performance that was similar to that of a traditional logistic regression model. While these results are encouraging, future research, including prospective validations with multicenter data, is warranted prior to the implementation of this algorithm as a real-time clinical decision support tool.
Yu et al. (Thu,) conducted a case-control in Children with heart disease. Functional gradient boosting machine learning algorithm vs. Traditional multivariable logistic regression model was evaluated on Prediction of cardiac arrest at one hour prior to the event (AUROC 0.85, 95% CI 0.79-0.90). A functional gradient boosting machine learning algorithm predicted cardiac arrest one hour prior to the event with an AUROC of 0.85 (95% CI 0.79-0.90), similar to a logistic regression model.