Background: A previous retrospective single-centre study suggested that the percentage of time spent with cerebral perfusion pressure (CPP) below the individual lower limit of reactivity (LLR) is associated with mortality in traumatic brain injury (TBI) patients. We aim to validate this in a large multicentre cohort. Methods: Recordings from 171 TBI patients from the high-resolution cohort of the CENTER-TBI study were processed with ICM+ software. We derived LLR as a time trend of CPP at a level for which the pressure reactivity index (PRx) indicates impaired cerebrovascular reactivity with low CPP. The relationship with mortality was assessed with Mann-U test (first 7-day period), Kruskal–Wallis (daily analysis for 7 days), univariate and multivariate logistic regression models. AUCs (CI 95%) were calculated and compared using DeLong’s test. Results: Average LLR over the first 7 days was above 60 mmHg in 48% of patients. %time with CPP < LLR could predict mortality (AUC 0.73, p = < 0.001). This association becomes significant starting from the third day post injury. The relationship was maintained when correcting for IMPACT covariates or for high ICP. Conclusions: Using a multicentre cohort, we confirmed that CPP below LLR was associated with mortality during the first seven days post injury.

Purpose: CPPopt denotes a Cerebral Perfusion Pressure (CPP) value at which the Pressure-Reactivity index, reflecting the global state of Cerebral Autoregulation, is best preserved. CPPopt has been investigated as a potential dynamically individualised CPP target in traumatic brain injury patients admitted in intensive care unit. The prospective bedside use of the concept requires ensured safety and reliability of the CPP recommended targets based on the automatically-generated CPPopt. We aimed to: Increase stability and reliability of the CPPopt automated algorithm by fine-tuning; perform outcome validation of the adjusted algorithm in a multi-centre TBI cohort. Methods: ICM + software was used to derive CPPopt and fine-tune the algorithm. Parameters for improvement of the algorithm were selected based on qualitative and quantitative assessment of stability and reliability metrics. Patients enrolled in the Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) high-resolution cohort were included for retrospective validation. Yield and stability of the new algorithm were compared to the previous algorithm using Mann–U test. Area under the curves for mortality prediction at 6 months were compared with the DeLong Test. Results: CPPopt showed higher stability (p < 0.0001), but lower yield compared to the previous algorithm [80.5% (70—87.5) vs 85% (75.7—91.2), p < 0.001]. Deviation of CPPopt could predict mortality with an AUC of [AUC = 0.69 (95% CI 0.59–0.78), p < 0.001] and was comparable with the previous algorithm. Conclusion: The CPPopt calculation algorithm was fine-tuned and adapted for prospective use with acceptable lower yield, improved stability and maintained prognostic power.

Adoption of the ICM+® brain monitoring software by clinical research centres worldwide has been continuously growing over the past 20 years. This has necessitated ongoing updates to accommodate evolving neuromonitoring research needs, including recent explosion of artificial intelligence (AI). We sought to provide an update on the current features of the software. In particular, we aimed to highlight the new options of integrating AI models. We reviewed all currently available ICM+ analytical areas and discussed potential AI based extensions in each. We tested a proof-of-concept integration of an AI model and evaluated its performance for real-time data processing. ICM+ current analytical tools serve both real-time (bed-side) and offline (file based) analysis, including the calculation engine, Signal Calculator, Custom Statistics, Batch tools, ScriptLab and charting. The ICM+ Python plugin engine allows to execute custom Python scripts and take advantage of complex AI frameworks. For the proof-of-concept, we used a neural network convolutional model with 207,000 trainable parameters that classifies morphology of intracranial pressure (ICP) pulse waveform into 5 pulse categories (normal to pathological plus artefactual). When evaluated within ICM+ plugin script on a Windows 10 laptop the classification of a 5 min ICP waveform segment took only 0.19s with a 2.3s of initial, one-off, model loading time required. Modernised ICM+ analytical tools, reviewed in this manuscript, include integration of custom AI models allowing them to be shared and run in real-time, facilitating rapid prototyping and validating of new AI ideas at the bed-side.

Background: Despite the quantitative information derived from testing of the CSF circulation, there is still no consensus on what the best approach could be in defining criteria for shunting and predicting response to CSF diversion in normal pressure hydrocephalus (NPH). Objective: We aimed to review the lessons learned from assessment of CSF dynamics in our center and summarize our findings to date. We have focused on reporting the objective perspective of CSF dynamics testing, without further inferences to individual patient management. Discussion: No single parameter from the CSF infusion study has so far been able to serve as an unquestionable outcome predictor. Resistance to CSF outflow (Rout) is an important biological marker of CSF circulation. It should not, however, be used as a single predictor for improvement after shunting. Testing of CSF dynamics provides information on hydrodynamic properties of the cerebrospinal compartment: the system which is being modified by a shunt. Our experience of nearly 30 years of studying CSF dynamics in patients requiring shunting and/or shunt revision, combined with all the recent progress made in producing evidence on the clinical utility of CSF dynamics, has led to reconsidering the relationship between CSF circulation testing and clinical improvement. Conclusions: Despite many open questions and limitations, testing of CSF dynamics provides unique perspectives for the clinician. We have found value in understanding shunt function and potentially shunt response through shunt testing in vivo. In the absence of infusion tests, further methods that provide a clear description of the pre and post-shunting CSF circulation, and potentially cerebral blood flow, should be developed and adapted to the bed-space.

Classic methods for assessing cerebral autoregulation involve a transfer function analysis performed using the Fourier transform to quantify relationship between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV). This approach usually assumes the signals and the system to be stationary. Such an presumption is restrictive and may lead to unreliable results. The aim of this study is to present an alternative method that accounts for intrinsic non-stationarity of cerebral autoregulation and the signals used for its assessment. Continuous recording of CBFV, ABP, ECG, and end-tidal CO₂ were performed in 50 young volunteers during normocapnia and hypercapnia. Hypercapnia served as a surrogate of the cerebral autoregulation impairment. Fluctuations in ABP, CBFV, and phase shift between them were tested for stationarity using sphericity based test. The Zhao-Atlas-Marks distribution was utilized to estimate the time—frequency coherence (TFCoh) and phase shift (TFPS) between ABP and CBFV in three frequency ranges: 0.02–0.07 Hz (VLF), 0.07–0.20 Hz (LF), and 0.20–0.35 Hz (HF). TFPS was estimated in regions locally validated by statistically justified value of TFCoh. The comparison of TFPS with spectral phase shift determined using transfer function approach was performed. The hypothesis of stationarity for ABP and CBFV fluctuations and the phase shift was rejected. Reduced TFPS was associated with hypercapnia in the VLF and the LF but not in the HF. Spectral phase shift was also decreased during hypercapnia in the VLF and the LF but increased in the HF. Time-frequency method led to lower dispersion of phase estimates than the spectral method, mainly during normocapnia in the VLF and the LF. The time—frequency method performed no worse than the classic one and yet may offer benefits from lower dispersion of phase shift as well as a more in-depth insight into the dynamic nature of cerebral autoregulation.

Baroreflex is a mechanism of short-term neural control responsible for maintaining stable levels of arterial blood pressure (ABP) in an ABP-heart rate negative feedback loop. Its function is assessed by baroreflex sensitivity (BRS)—a parameter which quantifies the relationship between changes in ABP and corresponding changes in heart rate (HR). The effect of postural change as well as the effect of changes in blood O₂ and CO₂ have been the focus of multiple previous studies on BRS. However, little is known about the influence of the combination of these two factors on dynamic baroreflex response. Furthermore, classical methods used for BRS assessment are based on the assumption of stationarity that may lead to unreliable results in the case of mostly nonstationary cardiovascular signals. Therefore, we aimed to investigate BRS during repeated transitions between squatting and standing in normal end-tidal CO₂ (EtCO₂) conditions (normocapnia) and conditions of progressively increasing EtCO₂ with a decreasing level of O₂ (hypercapnia with hypoxia) using joint time and frequency domain (TF) approach to BRS estimation that overcomes the limitation of classical methods. Noninvasive continuous measurements of ABP and EtCO₂ were conducted in a group of 40 healthy young volunteers. The time course of BRS was estimated from TF representations of pulse interval variability and systolic pressure variability, their coherence, and phase spectra. The relationship between time-variant BRS and indices of ABP and HR was analyzed during postural change in normocapnia and hypercapnia with hypoxia. In normocapnia, observed trends in all measures were in accordance with previous studies, supporting the validity of presented TF method. Similar but slightly attenuated response to postural change was observed in hypercapnia with hypoxia. Our results show the merits of the nonstationary methods as a tool to study the cardiovascular system during short-term hemodynamic changes.