Reversible focal intracranial hypertension swine model with continuous multimodal neuromonitoring

Abstract

Background: Intracranial hypertension (HI) is associated with worse neurological outcomes and higher mortality. Although there are several experimental models of HI, in this article we present a reproducible, reversible, and reliable model of intracranial hypertension, with continuous multimodal monitoring. New method: A reversible intracranial hypertension model in swine with multimodal monitoring including intracranial pressure, arterial blood pressure, heart rate variation, brain tissue oxygenation, and electroencephalogram is developed to understand the relationship of ICP and EEG. By inflating and deflating a balloon, located 20 mm anterior to the coronal suture and a 15 mm sagittal suture, we generate intracranial hypertension events and simultaneously measure intracranial pressure and oxygenation in the contralateral hemisphere and the EEG, simulating the usual configuration in humans. Results: We completed 5 experiments and in all of them, we were able to complete at least 6 events of intracranial hypertension in a stable and safe way. For events of 20–40 mmHg of ICP we need an median (IQR) of 4.2 (3.64) ml of saline solution into the Foley balloon, a median (IQR) infusion time of 226 (185) second in each event and for events of 40–50 mmHg of ICP we need a median (IQR) of 5.1 (4.66) ml of saline solution, a median (IQR) infusion time of 280 (48) seconds and a median (IQR). The median (IQR) maintenance time was 352 (77) seconds and 392 (166) seconds for 20–40 mmHg and 40–50 mmHg of ICP, respectively. Comparison with existing method(s): Existing methods do not include EEG measures and do not present the reversibility of intracranial hypertension. Conclusions: Our model is fully reproducible, it is capable of generating reversible focal intracranial hypertension through strict control of the injected volume, it is possible to generate different infusion rates of the volume in the balloon, in order to generate different scenarios, the data obtained are sufficient to determine the brain complacency in real time. and useful for understanding the pathophysiology of ICP and the relationship between ICP (CPP) and EEG.