Multi-modal prediction of extracorporeal support-a resource intensive therapy, utilizing a large national database.

ABSTRACT:

Objective

Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.

Material and methods

We leveraged multimodal data from the National COVID Cohort Collaborative (N3C) to develop a hierarchical deep learning model, labeled "PreEMPT-ECMO" (Prediction, Early Monitoring, and Proactive Triage for ECMO) which integrates static and multi-granularity time series features to generate continuous predictions of ECMO utilization. Model performance was assessed across time points ranging from 0 to 96 hours prior to ECMO initiation, using both accuracy

SUBMITTER: Zhu D 

PROVIDER: S-EPMC11702361 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature