Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis.

ABSTRACT:

Importance

Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. There are gaps, however, in understanding how ICP monitor type may bias waveform characteristics used for these predictive tools since differences between external ventricular drain (EVD) and intraparenchymal monitor (IPM)-derived waveforms have not been well accounted for.

Objectives

We sought to develop a proof-of-concept ML model differentiating ICP waveforms originating from an EVD or IPM.

Design, setting, and participants

We examined raw ICP waveform data from the ICU physiology cohort within the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury multicenter study.

Main outcomes and m

SUBMITTER: Ack SE 

PROVIDER: S-EPMC11254120 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature