Using machine learning to predict neurologic injury in venovenous extracorporeal membrane oxygenation recipients: An ELSO Registry analysisCentral MessagePerspective
Andrew Kalra, BS,
Preetham Bachina, BS,
Benjamin L. Shou, BS,
Jaeho Hwang, MD,
Meylakh Barshay, BA,
Shreyas Kulkarni, BS,
Isaac Sears, BS,
Carsten Eickhoff, PhD,
Christian A. Bermudez, MD,
Daniel Brodie, MD,
Corey E. Ventetuolo, MD, MS,
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
Affiliations
Andrew Kalra, BS
Division of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md; Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pa
Preetham Bachina, BS
Division of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md
Benjamin L. Shou, BS
Division of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md
Jaeho Hwang, MD
Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Md
Meylakh Barshay, BA
Warren Alpert Medical School of Brown University, Providence, RI
Shreyas Kulkarni, BS
Warren Alpert Medical School of Brown University, Providence, RI
Isaac Sears, BS
Warren Alpert Medical School of Brown University, Providence, RI
Carsten Eickhoff, PhD
Department 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, Germany
Christian A. Bermudez, MD
Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa
Daniel Brodie, MD
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Md
Corey E. Ventetuolo, MD, MS
Department of Health Services, Policy and Practice, Brown School of Public Health, Providence, RI; Division of Pulmonary, Critical Care and Sleep Medicine, Warren Alpert Medical School of Brown University, Providence, RI
Glenn J.R. Whitman, MD
Division of Cardiac Surgery, Department of Surgery, Johns Hopkins Hospital, Baltimore, Md
Adeel Abbasi, MD, ScM
Division of Pulmonary, Critical Care and Sleep Medicine, Warren Alpert Medical School of Brown University, Providence, RI
Sung-Min Cho, DO, MHS
Division 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.
Background: Venovenous extracorporeal membrane oxygenation (VV-ECMO) is associated with acute brain injury (ABI), including central nervous system (CNS) ischemia (defined as ischemic stroke or hypoxic-ischemic brain injury [HIBI]) and intracranial hemorrhage (ICH). Data on prediction models for neurologic outcomes in VV-ECMO are limited. Methods: We analyzed adult (age ≥18 years) VV-ECMO patients in the Extracorporeal Life Support Organization (ELSO) Registry (2009-2021) from 676 centers. ABI was defined as CNS ischemia, ICH, brain death, and seizures. Data on 67 variables were extracted, including clinical characteristics and pre-ECMO/on-ECMO variables. Random forest, CatBoost, LightGBM, and XGBoost machine learning (ML) algorithms (10-fold leave-one-out cross-validation) were used to predict ABI. Feature importance scores were used to pinpoint the most important variables for predicting ABI. Results: Of 37,473 VV-ECMO patients (median age, 48.1 years; 63% male), 2644 (7.1%) experienced ABI, including 610 (2%) with CNS ischemia and 1591 (4%) with ICH. The areas under the receiver operating characteristic curve for predicting ABI, CNS ischemia, and ICH were 0.70, 0.68, and 0.70, respectively. The accuracy, positive predictive value, and negative predictive value for ABI were 85%, 19%, and 95%, respectively. ML identified higher center volume, pre-ECMO cardiac arrest, higher ECMO pump flow, and elevated on-ECMO serum lactate level as the most important risk factors for ABI and its subtypes. Conclusions: This is the largest study of VV-ECMO patients to use ML to predict ABI reported to date. Performance was suboptimal, likely due to lack of standardization of neuromonitoring/imaging protocols and data granularity in the ELSO Registry. Standardized neurologic monitoring and imaging are needed across ELSO centers to detect the true prevalence of ABI.
