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. 2025 Oct;43(2):403-413.
doi: 10.1007/s12028-025-02233-0. Epub 2025 Mar 27.

A Novel, Interpretable Machine Learning Model to Predict Neurological Outcomes Following Venoarterial Extracorporeal Membrane Oxygenation

Benjamin L Shou  1 , Albert Leng #  2 , Preetham Bachina  2 , Andrew Kalra  2   3 , Alice L Zhou  2 , Glenn Whitman  2 , Sung-Min Cho  2   4 ; HERALD Investigators
Affiliations

A Novel, Interpretable Machine Learning Model to Predict Neurological Outcomes Following Venoarterial Extracorporeal Membrane Oxygenation

Benjamin L Shou et al. Neurocrit Care. 2025 Oct.

Abstract

Background: We used machine learning models incorporating rich electronic medical record (EMR) data to predict neurological outcomes after venoarterial extracorporeal membrane oxygenation (VA-ECMO).

Methods: This was a retrospective review of adult (≥ 18 years) patients undergoing VA-ECMO between 6/2016 and 4/2022 at a single center. The primary outcome was good neurological outcome, defined as a modified Rankin Scale score of 0 to 3, evaluated at hospital discharge. We extracted every measurement of 74 vital and laboratory values, as well as circuit and ventilator settings, from 24 h before cannulation through the entire duration of ECMO. An XGBoost model with Shapley Additive Explanations was developed and evaluated with leave-one-out cross-validation.

Results: Overall, 194 patients undergoing VA-ECMO (median age 58 years, 63% male) were included. We extracted more than 14 million individual data points from the EMR. Of 194 patients, 39 patients (20%) had good neurological outcomes. Three models were generated: model A, which contained only pre-ECMO data; model B, which added data from the first 48 h of ECMO; and model C, which included data from the entire ECMO run. The leave-one-out cross-validation area under the receiver operator characteristics curves for models A, B, and C were 0.72, 0.81, and 0.90, respectively. The inclusion of on-ECMO physiologic, laboratory, and circuit data greatly improved model performance. Both modifiable and nonmodifiable variables, such as lower body mass index, lower age, higher mean arterial pressure, and higher hemoglobin, were associated with good neurological outcome.

Conclusions: An interpretable machine learning model from EMR-extracted data was able to predict neurological outcomes for patients undergoing VA-ECMO with excellent accuracy.

Keywords: ECMO; Machine learning; Neurological outcomes.

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Conflict of interest statement

Declarations. Conflicts of interest: The authors declare no conflict of interest. Ethical approval/informed consent: IRB00216321, titled: “Retrospective Analysis of Outcomes of Patients on Extracorporeal Membrane Oxygenation” approved on October 22, 2019. Procedures were conducted in line with the ethical standards of the institution and with the Helsinki Declaration of 1975. Study-specific informed consent was waived by the institutional review board.

References

    1. Rao P, Khalpey Z, Smith R, Burkhoff D, Kociol RD. Venoarterial extracorporeal membrane oxygenation for cardiogenic shock and cardiac arrest. Circ Heart Fail. 2018;11(9): e004905. https://doi.org/10.1161/CIRCHEARTFAILURE.118.004905 . - DOI - PubMed
    1. Zangrillo A, Landoni G, Biondi-Zoccai G, et al. A meta-analysis of complications and mortality of extracorporeal membrane oxygenation. Crit Care Resusc. 2013;15(3):172–8. - PubMed
    1. Lorusso R, Barili F, Mauro MD, et al. In-hospital neurologic complications in adult patients undergoing venoarterial extracorporeal membrane oxygenation: results from the extracorporeal life support organization registry. Crit Care Med. 2016;44(10):e964-972. https://doi.org/10.1097/CCM.0000000000001865 . - DOI - PubMed
    1. Cho SM, Canner J, Chiarini G, et al. Modifiable risk factors and mortality from ischemic and hemorrhagic strokes in patients receiving venoarterial extracorporeal membrane oxygenation: results from the extracorporeal life support organization registry. Crit Care Med. 2020;48(10):e897–905. https://doi.org/10.1097/CCM.0000000000004498 . - DOI - PubMed - PMC
    1. Schmidt M, Burrell A, Roberts L, et al. Predicting survival after ECMO for refractory cardiogenic shock: the survival after veno-arterial-ECMO (SAVE)-score. Eur Heart J. 2015;36(33):2246–56. https://doi.org/10.1093/eurheartj/ehv194 . - DOI - PubMed

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