Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysis

被引:10
作者
Kalra, Andrew [1 ,2 ]
Bachina, Preetham [1 ]
Shou, Benjamin L. [1 ]
Hwang, Jaeho [3 ]
Barshay, Meylakh [4 ]
Kulkarni, Shreyas [4 ]
Sears, Isaac [4 ]
Eickhoff, Carsten [5 ,6 ,7 ]
Bermudez, Christian A. [8 ]
Brodie, Daniel [9 ]
Ventetuolo, Corey E. [10 ]
Kim, Bo Soo [9 ]
Whitman, Glenn J. R. [1 ]
Abbasi, Adeel [10 ]
Cho, Sung-Min [1 ,11 ]
机构
[1] Johns Hopkins Univ Hosp, Dept Surg, Div Cardiac Surg, Baltimore, MD USA
[2] Thomas Jefferson Univ, Sidney Kimmel Med Coll, Philadelphia, PA USA
[3] Johns Hopkins Univ Hosp, Dept Neurol, Div Epilepsy, Baltimore, MD USA
[4] Brown Univ, Warren Alpert Med Sch, Providence, RI USA
[5] Brown Univ, Dept Comp Sci, Providence, RI USA
[6] Univ Tubingen, Fac Med, Tubingen, Germany
[7] Univ Tubingen, Inst Bioinformat & Med Informat, Tubingen, Germany
[8] Univ Penn, Perelman Sch Med, Dept Surg, Div Cardiovasc Surg, Philadelphia, PA USA
[9] Johns Hopkins Univ, Dept Med, Sch Med, Div Pulm & Crit Care Med, Baltimore, MD USA
[10] Brown Univ, Warren Alpert Med Sch, Div Pulm Crit Care & Sleep Med, Providence, RI USA
[11] Johns Hopkins Univ Hosp, Dept Neurol Neurosurg Anesthesiol & Crit Care Med, Div Neurosci Crit Care, Baltimore, MD USA
来源
JTCVS OPEN | 2024年 / 20卷
关键词
acute brain injury; Extracorporeal Life Support Organization; extracorporeal membrane oxygenation; machine learning; neurological complications; SUSTAINING THERAPY; WITHDRAWAL; MORTALITY; SURVIVAL; ECMO;
D O I
10.1016/j.xjon.2024.06.001
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Objective: We aimed to determine if machine learning can predict acute brain injury and to identify modifiable fi able 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/onextracorporeal 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 fl ow, 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 fl ow 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 fl ow were associated with acute brain injury in nonextracorporeal cardiopulmonary resuscitation and extracorporeal cardiopulmonary resuscitation venoarterial extracorporeal membrane oxygenation. (JTCVS Open 2024;20:64-88)
引用
收藏
页码:64 / 88
页数:25
相关论文
共 45 条
[1]   Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation [J].
Abbasi, Adeel ;
Karasu, Yasmin ;
Li, Cindy ;
Sodha, Neel R. ;
Eickhoff, Carsten ;
Ventetuolo, Corey E. .
CRITICAL CARE, 2020, 24 (01)
[2]   A novel mortality risk score predicting intensive care mortality in cardiogenic shock patients treated with veno-arterial extracorporeal membrane oxygenation [J].
Akin, Sakir ;
Caliskan, Kadir ;
Soliman, Osama ;
Muslem, Rahatullah ;
Guven, Goksel ;
van Thiel, Robert J. ;
Struijs, Ard ;
Gommers, Diederik ;
Zijlstra, Felix ;
Bakker, Jan ;
Miranda, Dinis dos Reis .
JOURNAL OF CRITICAL CARE, 2020, 55 :35-41
[3]   Duration of Hyperoxia and Neurologic Outcomes in Patients Undergoing Extracorporeal Membrane Oxygenation [J].
Al-Kawaz, Mais N. ;
Canner, Joseph ;
Caturegli, Giorgio ;
Kannapadi, Nivedha ;
Balucani, Clotilde ;
Shelley, Leah ;
Kim, Bo Soo ;
Choi, Chun Woo ;
Geocadin, Romergryko G. ;
Whitman, Glenn ;
Cho, Sung-Min .
CRITICAL CARE MEDICINE, 2021, 49 (10) :E968-E977
[4]   Statistics notes - The cost of dichotomising continuous variables [J].
Altman, DG ;
Royston, P .
BRITISH MEDICAL JOURNAL, 2006, 332 (7549) :1080-1080
[5]  
[Anonymous], 2024, 4 Key advantages of using decision trees for predictive analytics
[6]   Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning [J].
Ayers, Brian ;
Wood, Katherine ;
Gosev, Igor ;
Prasad, Sunil .
ANNALS OF THORACIC SURGERY, 2020, 110 (04) :1193-1200
[7]   Risk prediction of in-hospital mortality in patients with venoarterial extracorporeal membrane oxygenation for cardiopulmonary support: The ECMO-ACCEPTS score [J].
Becher, Peter M. ;
Twerenbold, Raphael ;
Schrage, Benedikt ;
Schmack, Bastian ;
Sinning, Christoph R. ;
Fluschnik, Nina ;
Schwarzl, Michael ;
Waldeyer, Christoph ;
Seiffert, Moritz ;
Clemmensen, Peter ;
Neumann, Johannes T. ;
Bernhardt, Alexander M. ;
Zeymer, Uwe ;
Thiele, Holger ;
Reichenspurner, Hermann ;
Blankenberg, Stefan ;
Westermann, Dirk .
JOURNAL OF CRITICAL CARE, 2020, 56 :100-105
[8]   Central versus Peripheral Postcardiotomy Veno-Arterial Extracorporeal Membrane Oxygenation: Systematic Review and Individual Patient Data Meta-Analysis [J].
Biancari, Fausto ;
Kaserer, Alexander ;
Perrotti, Andrea ;
Ruggieri, Vito G. ;
Cho, Sung-Min ;
Kang, Jin Kook ;
Dalen, Magnus ;
Welp, Henryk ;
Jonsson, Kristjan ;
Ragnarsson, Sigurdur ;
Perez, Francisco J. Hernandez ;
Gatti, Giuseppe ;
Alkhamees, Khalid ;
Loforte, Antonio ;
Lechiancole, Andrea ;
Rosato, Stefano ;
Spadaccio, Cristiano ;
Pettinari, Matteo ;
Fiore, Antonio ;
Makikallio, Timo ;
Sahli, Sebastian D. ;
L'Acqua, Camilla ;
Arafat, Amr A. ;
Albabtain, Monirah A. ;
AlBarak, Mohammed M. ;
Laimoud, Mohamed ;
Djordjevic, Ilija ;
Krasivskyi, Ihor ;
Samalavicius, Robertas ;
Puodziukaite, Lina ;
Alonso-Fernandez-Gatta, Marta ;
Wilhelm, Markus J. ;
Mariscalco, Giovanni .
JOURNAL OF CLINICAL MEDICINE, 2022, 11 (24)
[9]   Cardiopulmonary resuscitation in veno-venous-ECMO patients-A retrospective study on incidence, causes and outcome [J].
Booke, Hendrik ;
Zacharowski, Kai ;
Adam, Elisabeth Hannah ;
Raimann, Florian Juergen ;
Bauer, Frederike ;
Flinspach, Armin Niklas .
PLOS ONE, 2023, 18 (08)
[10]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32