Continuous Monitoring of the Complexity of Intracranial Pressure After Head Injury

Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury

BACKGROUND

Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.

METHODS

We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.

RESULTS

The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.

CONCLUSIONS

The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Influence of mild-moderate hypocapnia on intracranial pressure slow waves activity in TBI

BACKGROUND

In traumatic brain injury (TBI) the patterns of intracranial pressure (ICP) waveforms may reflect pathological processes that ultimately lead to unfavorable outcome. In particular, ICP slow waves (sw) (0.005-0.05 Hz) magnitude and complexity have been shown to have positive association with favorable outcome. Mild-moderate hypocapnia is currently used for short periods to treat critical elevations in ICP. Our goals were to assess changes in the ICP sw activity occurring following sudden onset of mild-moderate hypocapnia and to examine the relationship between changes in ICP sw activity and other physiological variables during the hypocapnic challenge.

METHODS

ICP, arterial blood pressure (ABP), and bilateral middle cerebral artery blood flow velocity (FV), were prospectively collected in 29 adult severe TBI patients requiring ICP monitoring and mechanical ventilation in whom a minute volume ventilation increase (15-20% increase in respiratory minute volume) was performed as part of a clinical CO₂-reactivity test. The time series were first treated using FFT filter (pass-band set to 0.005-0.05 Hz). Power spectral density analysis was performed. We calculated the following: mean value, standard deviation, variance and coefficient of variation in the time domain; total power and frequency centroid in the frequency domain; cerebrospinal compliance (Ci) and compensatory reserve index (RAP).

RESULTS

Hypocapnia led to a decrease in power and increase in frequency centroid and entropy of slow waves in ICP and FV (not ABP). In a multiple linear regression model, RAP at the baseline was the strongest predictor for the decrease in the power of ICP slow waves (p < 0.001).

CONCLUSION

In severe TBI patients, a sudden mild-moderate hypocapnia induces a decrease in mean ICP and FV, but also in slow waves power of both signals. At the same time, it increases their higher frequency content and their morphological complexity. The difference in power of the ICP slow waves between the baseline and the hypocapnia period depends on the baseline cerebrospinal compensatory reserve as measured by RAP.

B waves: a systematic review of terminology, characteristics, and analysis methods

Background

Although B waves were introduced as a concept in the analysis of intracranial pressure (ICP) recordings nearly 60 years ago, there is still a lack consensus on precise definitions, terminology, amplitude, frequency or origin. Several competing terms exist, addressing either their probable physiological origin or their physical characteristics. To better understand B wave characteristics and ease their detection, a literature review was carried out.

Methods

A systematic review protocol including search strategy and eligibility criteria was prepared in advance. A literature search was carried out using PubMed/MEDLINE, with the following search terms: B waves + review filter, slow waves + review filter, ICP B waves, slow ICP waves, slow vasogenic waves, Lundberg B waves, MOCAIP.

Results

In total, 19 different terms were found, B waves being the most common. These terminologies appear to be interchangeable and seem to be used indiscriminately, with some papers using more than five different terms. Definitions and etiologies are still unclear, which makes systematic and standardized detection difficult.

Conclusions

Two future lines of action are available for automating macro-pattern identification in ICP signals: achieving strict agreement on morphological characteristics of “traditional” B waveforms, or starting a new with a fresh computerized approach for recognition of new clinically relevant patterns.

Non-linear Dynamical Analysis of Intraspinal Pressure Signal Predicts Outcome After Spinal Cord Injury

The injured spinal cord is a complex system influenced by many local and systemic factors that interact over many timescales. To help guide clinical management, we developed a technique that monitors intraspinal pressure from the injury site in patients with acute, severe traumatic spinal cord injuries. Here, we hypothesize that spinal cord injury alters the complex dynamics of the intraspinal pressure signal quantified by computing hourly the detrended fluctuation exponent alpha, multiscale entropy, and maximal Lyapunov exponent lambda. 49 patients with severe traumatic spinal cord injuries were monitored within 72 h of injury for 5 days on average to produce 5,941 h of intraspinal pressure data. We computed the spinal cord perfusion pressure as mean arterial pressure minus intraspinal pressure and the vascular pressure reactivity index as the running correlation coefficient between intraspinal pressure and arterial blood pressure. Mean patient follow-up was 17 months. We show that alpha values are greater than 0.5, which indicates that the intraspinal pressure signal is fractal. As alpha increases, intraspinal pressure decreases and spinal cord perfusion pressure increases with negative correlation between the vascular pressure reactivity index vs. alpha. Thus, secondary insults to the injured cord disrupt intraspinal pressure fractality. Our analysis shows that high intraspinal pressure, low spinal cord perfusion pressure, and impaired pressure reactivity strongly correlate with reduced multi-scale entropy, supporting the notion that secondary insults to the injured cord cause de-complexification of the intraspinal pressure signal, which may render the cord less adaptable to external changes. Healthy physiological systems are characterized by edge of chaos dynamics. We found negative correlations between the percentage of hours with edge of chaos dynamics (−0.01 ≤ lambda ≤ 0.01) vs. high intraspinal pressure and vs. low spinal cord perfusion pressure; these findings suggest that secondary insults render the intraspinal pressure more regular or chaotic. In a multivariate logistic regression model, better neurological status on admission, higher intraspinal pressure multi-scale entropy and more frequent edge of chaos intraspinal pressure dynamics predict long-term functional improvement. We conclude that spinal cord injury is associated with marked changes in non-linear intraspinal pressure metrics that carry prognostic information.