Acute Brain Injury Risk Prediction Models in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: An ELSO Registry Analysis

Andrew Kalra; Preetham Bachina; Benjamin L. Shou; Jaeho Hwang; Meylakh Barshay; Shreyas Kulkarni; Isaac Sears; Carsten Eickhoff; C. Bermúdez; Daniel Brodie; Corey E. Ventetuolo; Bo Soo Kim; Glenn Whitman; Adeel Abbasi; Sung‐Min Cho; Bo Soo Kim; David N. Hager; Steven P. Keller; Errol L. Bush; R. Scott Stephens; Shivalika Khanduja; Jin Kook Kang; Ifeanyi Chinedozi; Zachary Darby; Hannah Rando; Trish Brown; Jiah Kim; Christopher Wilcox; Albert Leng; Andrew Geeza; Armaan F. Akbar; Chengyuan Alex Feng; David Zhao; Marc Sussman; Pedro A. Mendez-Tellez; Philip Y. Sun; Karlo Capili; Ramon Riojas; Diane Alejo; Scott Stephen; Harry Flaster

doi:10.1016/j.xjon.2024.06.001

Key Points

Longer run duration and higher 24-hour pump flow are linked to increased acute brain injury risk in venoarterial extracorporeal membrane oxygenation patients.
Analysis of registry records utilizes tree-based machine learning models and arterial blood gas data to predict neurological injury across extracorporeal circuits.
Highlights clinical parameters for early risk stratification during extracorporeal cardiopulmonary resuscitation, though validation across external cohorts is needed.

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Implication

Registry analysis shows longer support duration and higher pump flow predict acute brain injury in venoarterial ECMO patients, highlighting key factors.

Abstract

ABGarterial blood gas ABI acute brain injury AUC-ROC Conclusions:In the largest study predicting neurological complications with ML in ECMO, longer ECMO duration and higher 24h pump flow were associated with ABI in non-ECPR and ECPR VA-ECMO.

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Kalra et al. (2024) studied this question.

synapsesocial.com/papers/68e64d72b6db6435875de1b8 https://doi.org/10.1016/j.xjon.2024.06.001

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Acute Brain Injury Risk Prediction Models in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: An ELSO Registry Analysis

Key Points

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Implication

Abstract

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Cite This Study

Acute Brain Injury Risk Prediction Models in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: An ELSO Registry Analysis

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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