Predicting Acute Brain Injury in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: Analysis of the Extracorporeal Life Support Organization Registry

Author:

Kalra Andrew1ORCID,Bachina Preetham1,Shou Benjamin L.1ORCID,Hwang Jaeho1ORCID,Barshay Meylakh2ORCID,Kulkarni Shreyas2ORCID,Sears Isaac2ORCID,Eickhoff Carsten3ORCID,Bermudez Christian A.4,Brodie Daniel1ORCID,Ventetuolo Corey E.2ORCID,Kim Bo Soo1,Whitman Glenn J. R.1ORCID,Abbasi Adeel2,Cho Sung-Min1ORCID

Affiliation:

1. Johns Hopkins University School of Medicine

2. Warren Alpert Medical School of Brown University

3. University of Tübingen

4. Perelman School of Medicine at the University of Pennsylvania, Philadelphia

Abstract

Abstract Objective: To determine if machine learning (ML) can predict acute brain injury (ABI) and identify modifiable risk factors for ABI in venoarterial extracorporeal membrane oxygenation (VA-ECMO) patients. Design: Retrospective cohort study of the Extracorporeal Life Support Organization (ELSO) Registry (2009-2021). Setting: International, multicenter registry study of 676 ECMO centers. Patients: Adults (≥18 years) supported with VA-ECMO or extracorporeal cardiopulmonary resuscitation (ECPR). Interventions: None. Measurements and Main Results: Our primary outcome was ABI: central nervous system (CNS) ischemia, intracranial hemorrhage (ICH), brain death, and seizures. We utilized Random Forest, CatBoost, LightGBM and XGBoost ML algorithms (10-fold leave-one-out cross-validation) to predict and identify features most important for ABI. We extracted 65 total features: demographics, pre-ECMO/on-ECMO laboratory values, and pre-ECMO/on-ECMO settings. Of 35,855 VA-ECMO (non-ECPR) patients (median age=57.8 years, 66% male), 7.7% (n=2,769) experienced ABI. In VA-ECMO (non-ECPR), the area under the receiver-operator characteristics curves (AUC-ROC) to predict ABI, CNS ischemia, and ICH was 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 ABI. Longer ECMO duration, higher 24h ECMO pump flow, and higher on-ECMO PaO2 were associated with ABI. Of 10,775 ECPR patients (median age=57.1 years, 68% male), 16.5% (n=1,787) experienced ABI. The AUC-ROC for ABI, CNS ischemia, and ICH was 0.72, 0.73, and 0.69, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 61%, 70%, 30%, 39%, 29% and 90%, respectively, for ABI. Longer ECMO duration, younger age, and higher 24h ECMO pump flow were associated with ABI. Conclusions: This is the largest study predicting neurological complications on sufficiently powered international ECMO cohorts. Longer ECMO duration and higher 24h pump flow were associated with ABI in both non-ECPR and ECPR VA-ECMO.

Funder

National Heart, Lung, and Blood Institute

Publisher

Research Square Platform LLC

Reference38 articles.

1. Extracorporeal Life Support Organization Registry International Report 2016;Thiagarajan RR;ASAIO J,2017

2. 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;Cho SM;Crit Care Med,2020

3. Neuromonitoring detects brain injury in patients receiving extracorporeal membrane oxygenation support;Ong CS;J Thorac Cardiovasc Surg,2021

4. Arterial oxygen and carbon dioxide tension and acute brain injury in extracorporeal cardiopulmonary resuscitation patients: Analysis of the extracorporeal life support organization registry;Shou BL;J Heart Lung Transplant,2023

5. Early Low Pulse Pressure in VA-ECMO Is Associated with Acute Brain Injury;Shou BL;Neurocrit Care,2022

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