Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective
Objective: We aimed to determine if machine learning can predict acute brain injury and to identify modifiable risk factors for acute brain injury in patients receiving venoarterial extracorporeal membrane oxygenation. Methods: We included adults (age ≥18 years) receiving venoarterial extracorporeal...
| Published in: | JTCVS Open |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Article |
| Language: | English |
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Elsevier
2024-08-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266627362400158X |
| _version_ | 1850338301799563264 |
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| author | Andrew Kalra, BS Preetham Bachina, BS Benjamin L. Shou, BS Jaeho Hwang, MD, MPH Meylakh Barshay, BS Shreyas Kulkarni, BS Isaac Sears, BS Carsten Eickhoff, PhD Christian A. Bermudez, MD Daniel Brodie, MD Corey E. Ventetuolo, MD, MS Bo Soo Kim, MD Glenn J.R. Whitman, MD Adeel Abbasi, MD, ScM Sung-Min Cho, DO, MHS Bo Soo Kim David Hager Steven P. Keller Errol L. Bush R. Scott Stephens Shivalika Khanduja Jin Kook Kang Ifeanyi David Chinedozi Zachary Darby Hannah J. Rando Trish Brown Jiah Kim Christopher Wilcox Albert Leng Andrew Geeza Armaan F. Akbar Chengyuan Alex Feng David Zhao Marc Sussman Pedro Alejandro Mendez-Tellez Philip Sun Karlo Capili Ramon Riojas Diane Alejo Scott Stephen Harry Flaster |
| author_facet | Andrew Kalra, BS Preetham Bachina, BS Benjamin L. Shou, BS Jaeho Hwang, MD, MPH Meylakh Barshay, BS Shreyas Kulkarni, BS Isaac Sears, BS Carsten Eickhoff, PhD Christian A. Bermudez, MD Daniel Brodie, MD Corey E. Ventetuolo, MD, MS Bo Soo Kim, MD Glenn J.R. Whitman, MD Adeel Abbasi, MD, ScM Sung-Min Cho, DO, MHS Bo Soo Kim David Hager Steven P. Keller Errol L. Bush R. Scott Stephens Shivalika Khanduja Jin Kook Kang Ifeanyi David Chinedozi Zachary Darby Hannah J. Rando Trish Brown Jiah Kim Christopher Wilcox Albert Leng Andrew Geeza Armaan F. Akbar Chengyuan Alex Feng David Zhao Marc Sussman Pedro Alejandro Mendez-Tellez Philip Sun Karlo Capili Ramon Riojas Diane Alejo Scott Stephen Harry Flaster |
| author_sort | Andrew Kalra, BS |
| collection | DOAJ |
| container_title | JTCVS Open |
| description | Objective: We aimed to determine if machine learning can predict acute brain injury and to identify modifiable risk factors for acute brain injury in patients receiving venoarterial extracorporeal membrane oxygenation. Methods: We included adults (age ≥18 years) receiving venoarterial extracorporeal membrane oxygenation or extracorporeal cardiopulmonary resuscitation in the Extracorporeal Life Support Organization Registry (2009-2021). Our primary outcome was acute brain injury: central nervous system ischemia, intracranial hemorrhage, brain death, and seizures. We used Random Forest, CatBoost, LightGBM, and XGBoost machine learning algorithms (10-fold leave-1-out cross-validation) to predict and identify features most important for acute brain injury. We extracted 65 total features: demographics, pre-extracorporeal membrane oxygenation/on-extracorporeal membrane oxygenation laboratory values, and pre-extracorporeal membrane oxygenation/on-extracorporeal membrane oxygenation settings. Results: Of 35,855 patients receiving venoarterial extracorporeal membrane oxygenation (nonextracorporeal cardiopulmonary resuscitation) (median age of 57.8 years, 66% were male), 7.7% (n = 2769) experienced acute brain injury. In venoarterial extracorporeal membrane oxygenation (nonextracorporeal cardiopulmonary resuscitation), the area under the receiver operator characteristic curves to predict acute brain injury, central nervous system ischemia, and intracranial hemorrhage were 0.67, 0.67, and 0.62, respectively. The true-positive, true-negative, false-positive, false-negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively, for acute brain injury. Longer extracorporeal membrane oxygenation duration, higher 24-hour extracorporeal membrane oxygenation pump flow, and higher on-extracorporeal membrane oxygenation partial pressure of oxygen were associated with acute brain injury. Of 10,775 patients receiving extracorporeal cardiopulmonary resuscitation (median age of 57.1 years, 68% were male), 16.5% (n = 1787) experienced acute brain injury. The area under the receiver operator characteristic curves for acute brain injury, central nervous system ischemia, and intracranial hemorrhage were 0.72, 0.73, and 0.69, respectively. Longer extracorporeal membrane oxygenation duration, older age, and higher 24-hour extracorporeal membrane oxygenation pump flow were associated with acute brain injury. Conclusions: In the largest study predicting neurological complications with machine learning in extracorporeal membrane oxygenation, longer extracorporeal membrane oxygenation duration and higher 24-hour pump flow were associated with acute brain injury in nonextracorporeal cardiopulmonary resuscitation and extracorporeal cardiopulmonary resuscitation venoarterial extracorporeal membrane oxygenation. |
| format | Article |
| id | doaj-art-e3e4cc8585cb4eef88e6ee78bd514c99 |
| institution | Directory of Open Access Journals |
| issn | 2666-2736 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Elsevier |
| record_format | Article |
| spelling | doaj-art-e3e4cc8585cb4eef88e6ee78bd514c992025-08-19T23:15:18ZengElsevierJTCVS Open2666-27362024-08-0120648810.1016/j.xjon.2024.06.001Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspectiveAndrew Kalra, BS0Preetham Bachina, BS1Benjamin L. Shou, BS2Jaeho Hwang, MD, MPH3Meylakh Barshay, BS4Shreyas Kulkarni, BS5Isaac Sears, BS6Carsten Eickhoff, PhD7Christian A. Bermudez, MD8Daniel Brodie, MD9Corey E. Ventetuolo, MD, MS10Bo Soo Kim, MD11Glenn J.R. Whitman, MD12Adeel Abbasi, MD, ScM13Sung-Min Cho, DO, MHS14Bo Soo KimDavid HagerSteven P. KellerErrol L. BushR. Scott StephensShivalika KhandujaJin Kook KangIfeanyi David ChinedoziZachary DarbyHannah J. RandoTrish BrownJiah KimChristopher WilcoxAlbert LengAndrew GeezaArmaan F. AkbarChengyuan Alex FengDavid ZhaoMarc SussmanPedro Alejandro Mendez-TellezPhilip SunKarlo CapiliRamon RiojasDiane AlejoScott StephenHarry FlasterDivision of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md; Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PaDivision of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, MdDivision of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, MdDivision of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, MdWarren Alpert Medical School of Brown University, Providence, RIWarren Alpert Medical School of Brown University, Providence, RIWarren Alpert Medical School of Brown University, Providence, RIDepartment of Computer Science, Brown University, Providence, RI; Faculty of Medicine, University of Tübingen, Tübingen, Germany; Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, GermanyDivision of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PaDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MdDivision of Pulmonary, Critical Care and Sleep Medicine, Warren Alpert Medical School of Brown University, Providence, RIDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MdDivision of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, MdDivision of Pulmonary, Critical Care and Sleep Medicine, Warren Alpert Medical School of Brown University, Providence, RIDivision of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md; Division of Neurosciences Critical Care, Department of Neurology, Neurosurgery, Anesthesiology and Critical Care Medicine, Johns Hopkins Hospital, Baltimore, Md; Address for reprints: Sung-Min Cho, DO, MHS, Division of Neurosciences Critical Care, The Johns Hopkins Hospital, 600 N Wolfe St, Phipps 455, Baltimore, MD 21287.Objective: We aimed to determine if machine learning can predict acute brain injury and to identify modifiable risk factors for acute brain injury in patients receiving venoarterial extracorporeal membrane oxygenation. Methods: We included adults (age ≥18 years) receiving venoarterial extracorporeal membrane oxygenation or extracorporeal cardiopulmonary resuscitation in the Extracorporeal Life Support Organization Registry (2009-2021). Our primary outcome was acute brain injury: central nervous system ischemia, intracranial hemorrhage, brain death, and seizures. We used Random Forest, CatBoost, LightGBM, and XGBoost machine learning algorithms (10-fold leave-1-out cross-validation) to predict and identify features most important for acute brain injury. We extracted 65 total features: demographics, pre-extracorporeal membrane oxygenation/on-extracorporeal membrane oxygenation laboratory values, and pre-extracorporeal membrane oxygenation/on-extracorporeal membrane oxygenation settings. Results: Of 35,855 patients receiving venoarterial extracorporeal membrane oxygenation (nonextracorporeal cardiopulmonary resuscitation) (median age of 57.8 years, 66% were male), 7.7% (n = 2769) experienced acute brain injury. In venoarterial extracorporeal membrane oxygenation (nonextracorporeal cardiopulmonary resuscitation), the area under the receiver operator characteristic curves to predict acute brain injury, central nervous system ischemia, and intracranial hemorrhage were 0.67, 0.67, and 0.62, respectively. The true-positive, true-negative, false-positive, false-negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively, for acute brain injury. Longer extracorporeal membrane oxygenation duration, higher 24-hour extracorporeal membrane oxygenation pump flow, and higher on-extracorporeal membrane oxygenation partial pressure of oxygen were associated with acute brain injury. Of 10,775 patients receiving extracorporeal cardiopulmonary resuscitation (median age of 57.1 years, 68% were male), 16.5% (n = 1787) experienced acute brain injury. The area under the receiver operator characteristic curves for acute brain injury, central nervous system ischemia, and intracranial hemorrhage were 0.72, 0.73, and 0.69, respectively. Longer extracorporeal membrane oxygenation duration, older age, and higher 24-hour extracorporeal membrane oxygenation pump flow were associated with acute brain injury. Conclusions: In the largest study predicting neurological complications with machine learning in extracorporeal membrane oxygenation, longer extracorporeal membrane oxygenation duration and higher 24-hour pump flow were associated with acute brain injury in nonextracorporeal cardiopulmonary resuscitation and extracorporeal cardiopulmonary resuscitation venoarterial extracorporeal membrane oxygenation.http://www.sciencedirect.com/science/article/pii/S266627362400158Xacute brain injuryExtracorporeal Life Support Organizationextracorporeal membrane oxygenationmachine learningneurological complications |
| spellingShingle | Andrew Kalra, BS Preetham Bachina, BS Benjamin L. Shou, BS Jaeho Hwang, MD, MPH Meylakh Barshay, BS Shreyas Kulkarni, BS Isaac Sears, BS Carsten Eickhoff, PhD Christian A. Bermudez, MD Daniel Brodie, MD Corey E. Ventetuolo, MD, MS Bo Soo Kim, MD Glenn J.R. Whitman, MD Adeel Abbasi, MD, ScM Sung-Min Cho, DO, MHS Bo Soo Kim David Hager Steven P. Keller Errol L. Bush R. Scott Stephens Shivalika Khanduja Jin Kook Kang Ifeanyi David Chinedozi Zachary Darby Hannah J. Rando Trish Brown Jiah Kim Christopher Wilcox Albert Leng Andrew Geeza Armaan F. Akbar Chengyuan Alex Feng David Zhao Marc Sussman Pedro Alejandro Mendez-Tellez Philip Sun Karlo Capili Ramon Riojas Diane Alejo Scott Stephen Harry Flaster Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective acute brain injury Extracorporeal Life Support Organization extracorporeal membrane oxygenation machine learning neurological complications |
| title | Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective |
| title_full | Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective |
| title_fullStr | Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective |
| title_full_unstemmed | Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective |
| title_short | Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective |
| title_sort | acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree based machine learning an extracorporeal life support organization registry analysiscentral messageperspective |
| topic | acute brain injury Extracorporeal Life Support Organization extracorporeal membrane oxygenation machine learning neurological complications |
| url | http://www.sciencedirect.com/science/article/pii/S266627362400158X |
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