MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
Acute brain dysfunction (ABD) is a common, severe ICU complication,
presenting as delirium or coma and leading to prolonged stays, increased
mortality, and cognitive decline. Traditional screening tools like the Glasgow
Coma Scale (GCS), Confusion Assessment Method (CAM), and Richmond
Agitation-Sedation Scale (RASS) rely on intermittent assessments, causing
delays and inconsistencies. In this study, we propose MANDARIN
(Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU
Patients), a 1.5M-parameter mixture-of-experts neural network to predict ABD in
real-time among ICU patients. The model integrates temporal and static data
from the ICU to predict the brain status in the next 12 to 72 hours, using a
multi-branch approach to account for current brain status. The MANDARIN model
was trained on data from 92,734 patients (132,997 ICU admissions) from 2
hospitals between 2008-2019 and validated externally on data from 11,719
patients (14,519 ICU admissions) from 15 hospitals and prospectively on data
from 304 patients (503 ICU admissions) from one hospital in 2021-2024. Three
datasets were used: the University of Florida Health (UFH) dataset, the
electronic ICU Collaborative Research Database (eICU), and the Medical
Information Mart for Intensive Care (MIMIC)-IV dataset. MANDARIN significantly
outperforms the baseline neurological assessment scores (GCS, CAM, and RASS)
for delirium prediction in both external (AUROC 75.5% CI: 74.2%-76.8% vs 68.3%
CI: 66.9%-69.5%) and prospective (AUROC 82.0% CI: 74.8%-89.2% vs 72.7% CI:
65.5%-81.0%) cohorts, as well as for coma prediction (external AUROC 87.3% CI:
85.9%-89.0% vs 72.8% CI: 70.6%-74.9%, and prospective AUROC 93.4% CI:
88.5%-97.9% vs 67.7% CI: 57.7%-76.8%) with a 12-hour lead time. This tool has
the potential to assist clinicians in decision-making by continuously
monitoring the brain status of patients in the ICU.
