Affiliations: [a] Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland | [b] Luciole Medical AG, Zurich, Switzerland
Correspondence:
[*]
Corresponding author: Jan Willms, Institute of Intensive Care Medicine University Hospital Zurich, Rämistrasse 100, 8091 Zurich, Switzerland. E-mail: [email protected].
Abstract: BACKGROUND: Intracranial pressure (ICP) is a vital parameter that is continuously monitored in patients with severe brain injury and imminent intracranial hypertension. OBJECTIVE: To estimate intracranial pressure without intracranial probes based on transcutaneous near infrared spectroscopy (NIRS). METHODS: We developed machine learning based approaches for noninvasive intracranial pressure (ICP) estimation using signals from transcutaneous near infrared spectroscopy (NIRS) as well as other cardiovascular and artificial ventilation parameters. RESULTS: In a patient cohort of 25 patients, with 22 used for model development and 3 for model testing, the best performing models were Fourier transform based Transformer ICP waveform estimation which produced a mean absolute error of 4.68 mm Hg (SD = 5.4) in estimation. CONCLUSION: We did not find a significant improvement in ICP estimation accuracy by including signals measured by transcutaneous NIRS. We expect that with higher quality and greater volume of data, noninvasive estimation of ICP will improve.
