The role of intracranial pressure variability as a predictor of intracranial hypertension and mortality in critically ill patients

Abstract

OBJECTIVE Intracranial pressure (ICP) monitoring is a widely utilized and essential tool for tracking neurosurgical patients, but there are limitations to the use of a solely ICP-based paradigm for guiding management. It has been suggested that ICP variability (ICPV), in addition to mean ICP, may be a useful predictor of neurological outcomes, as it represents an indirect measure of intact cerebral pressure autoregulation. However, the current literature regarding the applicability of ICPV shows conflicting associations between ICPV and mortality. Thus, the authors aimed to investigate the effect of ICPV on intracranial hypertensive episodes and mortality using the eICU Collaborative Research Database version 2.0. METHODS The authors extracted from the eICU database 1,815,676 ICP readings from 868 patients with neurosurgical conditions. ICPV was computed using two methods: the rolling standard deviation (RSD) and the absolute deviation from the rolling mean (DRM). An episode of intracranial hypertension was defined as at least 25 minutes of ICP > 22 mm Hg in any 30-minute window. The effects of mean ICPV on intracranial hypertension and mortality were computed using multivariate logistic regression. A recurrent neural network with long short-term memory was used for time-series predictions of ICP and ICPV to prognosticate future episodes of intracranial hypertension. RESULTS A higher mean ICPV was significantly associated with intracranial hypertension using both ICPV definitions (RSD: aOR 2.82, 95% CI 2.07–3.90, p < 0.001; DRM: aOR 3.93, 95% CI 2.77–5.69, p < 0.001). ICPV was significantly associated with mortality in patients with intracranial hypertension (RSD: aOR 1.28, 95% CI 1.04–1.61, p = 0.026, DRM: aOR 1.39, 95% CI 1.10–1.79, p = 0.007). In the machine learning models, both definitions of ICPV achieved similarly good results, with the best F1 score of 0.685 ± 0.026 and an area under the curve of 0.980 ± 0.003 achieved with the DRM definition over 20 minutes. CONCLUSIONS ICPV may be useful as an adjunct for the prognostication of intracranial hypertensive episodes and mortality in neurosurgical critical care as part of neuromonitoring. Further research on predicting future intracranial hypertensive episodes with ICPV may help clinicians react expediently to ICP changes in patients.

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

“…[58][59][60] The integration of ICP variability into recurrent network models has not only improved the prediction of future ICP spikes but also emerged as a strong mortality indicator. 61 As ABI management shifts toward a multimodal monitoring approach, ML is proving useful in deciphering complex patterns from multiple physiological signals. For instance, using data from the Brain Tissue Oxygen Monitoring and Management in Severe Traumatic Brian Injury (BOOST) II trial, ML predicted both ICP spikes and brain tissue hypoxia with a 30-minute lead time.…”