Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/7/2728 |
| _version_ | 1797607568797859840 |
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| author | Priscilla Yu Michael Skinner Ivie Esangbedo Javier J. Lasa Xilong Li Sriraam Natarajan Lakshmi Raman |
| author_facet | Priscilla Yu Michael Skinner Ivie Esangbedo Javier J. Lasa Xilong Li Sriraam Natarajan Lakshmi Raman |
| author_sort | Priscilla Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:32:47Z |
| format | Article |
| id | doaj.art-65e52a84b0b045cc83c22b9581892bb0 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-11T05:32:47Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-65e52a84b0b045cc83c22b9581892bb02023-11-17T17:01:08ZengMDPI AGJournal of Clinical Medicine2077-03832023-04-01127272810.3390/jcm12072728Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning AlgorithmPriscilla Yu0Michael Skinner1Ivie Esangbedo2Javier J. Lasa3Xilong Li4Sriraam Natarajan5Lakshmi Raman6Division of Critical Care, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75235, USADepartment of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USASection of Cardiac Critical Care, Division of Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, WA 98195, USADivision of Critical Care, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75235, USAPeter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75235, USADepartment of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USADivision of Critical Care, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75235, USABackground: 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.https://www.mdpi.com/2077-0383/12/7/2728machine learningcardiac arrestpredictionpediatricsheart disease |
| spellingShingle | Priscilla Yu Michael Skinner Ivie Esangbedo Javier J. Lasa Xilong Li Sriraam Natarajan Lakshmi Raman Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm Journal of Clinical Medicine machine learning cardiac arrest prediction pediatrics heart disease |
| title | Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm |
| title_full | Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm |
| title_fullStr | Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm |
| title_full_unstemmed | Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm |
| title_short | Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm |
| title_sort | predicting cardiac arrest in children with heart disease a novel machine learning algorithm |
| topic | machine learning cardiac arrest prediction pediatrics heart disease |
| url | https://www.mdpi.com/2077-0383/12/7/2728 |
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