Articles: neurocritical-care.
-
Eur J Trauma Emerg Surg · Aug 2024
Observational StudyPrediction of neurocritical care intensity through automated infrared pupillometry and transcranial doppler in blunt traumatic brain injury: the NOPE study.
This pilot study aimed to determine the capacity of automated infrared pupillometry (AIP) alone and in combination with transcranial doppler (TCD) on admission to rule out need for intense neuroAQ2 critical care (INCC) in severe traumatic brain injury (TBI). ⋯ This pilot study suggests a possible useful contribution of NPi to determine the need for INCC in severe blunt TBI patients on admission.
-
Journal of neurosurgery · Aug 2024
Multicenter StudyA supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.
In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications. ⋯ Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.