# Predicting ECMO NeuroLogICal Injuries using mAchiNe Learning (PELICAN)

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $511,676

## Abstract

Project Summary
Extracorporeal Membrane Oxygenation (ECMO) is a form of cardiopulmonary bypass used in
critically ill children and adults to support the heart and lungs when conventional therapies fail.
More than 79,762 children worldwide have been supported to date, and global use of this tool is
expanding. Advances in ECMO and critical care have improved survival of otherwise fatal
illnesses, thereby unmasking neurologic injury which itself reduces survival by 50-60% and leads
to significant long-term neurologic morbidity. The mechanisms of ECMO-related cerebral injuries
are poorly understood. Existing research focuses on evaluating discrete elements, such as
underlying illness, coagulation abnormalities, anticoagulation management, or markers of end-
organ perfusion as factors associated with brain injury. Prior studies have not considered the
temporal and dynamic element of clinical events that may play a large role in the genesis of brain
injury, and few have explored which variables could predict significant neurologic injury without
the bias of pre-selecting variables of interest. Machine learning is a form of artificial intelligence
that employs algorithms to discover patterns in an iterative manner directly from input data: in the
context of ECMO, it can identify dynamic patterns and relationships between variables prior to
neurologic injury.
 The long-term goal of this research is to identify modifiable bedside predictors of neurologic
impairment and thereby drive the development of early interventions to improve neurologic
outcomes of children undergoing ECMO. Towards this goal, we have assembled a multi-
disciplinary team with clinical and computational expertise. Our central hypothesis is that a robust
risk predictive model for SNI in ECMO patients can be developed based on the physiological and
laboratory data routinely collected in real-world clinical settings and that this model can be used
to identify parameters of SNI for potential intervention. This proposal will utilize advanced machine
learning algorithms to build this prediction model in a large multicenter cohort (0-18 years, n=750).
In Aim 1, we will use novel probabilistic machine learning algorithms to train and develop a model
to predict SNI by neuroimaging. In Aim 2, we will validate and refine the model from Aim 1 using
neuroimaging scores and explore a personalized anytime query algorithm that predicts the timing
and type of SNI. This collaborative proposal between clinical and computational scientists will lay
the groundwork for a neuroprotective interventional study by identifying modifiable risk factors to
improve the tragically high neurologic morbidity and mortality in ECMO survivors.

## Key facts

- **NIH application ID:** 10877793
- **Project number:** 5R01NS133142-02
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Lakshmi Raman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $511,676
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877793

## Citation

> US National Institutes of Health, RePORTER application 10877793, Predicting ECMO NeuroLogICal Injuries using mAchiNe Learning (PELICAN) (5R01NS133142-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10877793. Licensed CC0.

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