Feasibility and Safety of Integrating Extended TCD Assessments in a Full Multimodal Neuromonitoring Protocol After Traumatic Brain Injury.

OBJECTIVE: Targeting single monitoring modalities such as intracranial pressure (ICP) or cerebral perfusion pressure alone has shown to be insufficient in improving outcome after traumatic brain injury (TBI). Multimodality monitoring (MMM) allows for a more complete description of brain function and for individualized management. Transcranial Doppler (TCD) represents the gold standard for continuous cerebral blood flow velocity assessment, but requires high levels skill and time. In TBI, the practical aspects of conducting extended TCD monitoring sessions have yet to be evaluated. METHODS: Patients with acute moderate-to-severe TBI admitted to the neurocritical care unit between March 2022 and December 2023 receiving invasive ICP measurements were evaluated for inclusion. Exclusion criteria included trauma incompatible with TCD monitoring and if MMM was unwarranted. Daily MMM sessions (in addition to regular monitoring) were performed using TCD (Delica EMS 9D System or the DWL Doppler Box) for ≤5 d. Quantitative and qualitative feasibility, safety, and quality metrics were assessed. RESULTS: Of 74 patients, 36 (75% male; mean age, 44 ± 17 y) were included. Common reasons for exclusion were skull fractures (n = 12) and decompressive craniectomy (n = 9). We acquired 88 recordings (mean, 275 ± 88 min). Overall monitoring times increased, and set-up times decreased. Physiologic variables (including ICP/brain temperature) did not change with TCD application. A single adverse event (dislodging of a microdialysis catheter) occurred. CONCLUSION: Implementing extended TCD monitoring in MMM protocols is feasible and safe. Considering these results, inclusion of long-term TCD as part of the MMM is strongly encouraged to allow for in-depth description and direct evaluation of hemodynamic changes after TBI.

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study.

BACKGROUND: Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet. METHODS: aSAH patients from 2 prospective cohorts (Zurich-derivation cohort, Aachen-validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1-4 vs. 5-8) or ordinal outcome (GOSE-extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination. RESULTS: A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity. CONCLUSIONS: MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Pitfalls and possibilities of using Root SedLine for continuous assessment of EEG waveform-based metrics in intensive care research.

Objective.The Root SedLine device is used for continuous electroencephalography (cEEG)-based sedation monitoring in intensive care patients. The cEEG traces can be collected for further processing and calculation of relevant metrics not already provided. Depending on the device settings during acquisition, the acquired traces may be distorted by max/min value cropping or high digitization errors. We aimed to systematically assess the impact of these distortions on metrics used for clinical research in the field of neuromonitoring.Approach.A 16 h cEEG acquired using the Root SedLine device at the optimal screen settings was analyzed. Cropping and digitization error effects were simulated by consecutive reduction of the maximum cEEG amplitude by 2µV or by reducing the vertical resolution. Metrics were calculated within ICM+ using minute-by-minute data, including the total power, alpha delta ratio (ADR), and 95% spectral edge frequency. Data were analyzed by creating violin- or box-plots.Main Results.Cropping led to a continuous reduction in total and band power, leading to corresponding changes in variability thereof. The relative power and ADR were less affected. Changes in resolution led to relevant changes. While the total power and power of low frequencies were rather stable, the power of higher frequencies increased with reducing resolution.Significance.Care must be taken when acquiring and analyzing cEEG waveforms from Root SedLine for clinical research. To retrieve good quality metrics, the screen settings must be kept within the central vertical scale, while pre-processing techniques must be applied to exclude unacceptable periods.

Mathematical modelling of cerebral haemodynamics and their effects on ICP.

Introduction: Electrical-equivalence mathematical models that integrate vascular and cerebrospinal fluid (CSF) compartments perform well in simulations of dynamic cerebrovascular variations and their transient effects on intracranial pressure (ICP). However, ICP changes due to sustained vascular diameter changes have not been comprehensively examined. We hypothesise that changes in cerebrovascular resistance (CVR) alter the resistance of the bulk flow of interstitial fluid (ISF). Research question: We hypothesise that changes in CVR alter the resistance of the bulk flow of ISF, thus allowing simulations of ICP in response to sustained vascular diameter changes. Material and methods: A lumped parameter model with vascular and CSF compartments was constructed and converted into an electrical analogue. The flow and pressure responses to transient hyperaemic response test (THRT) and CSF infusion test (IT) were observed. Arterial blood pressure (ABP) was manipulated to simulate ICP plateau waves. The experiments were repeated with a modified model that included the ISF compartment. Results: Simulations of the THRT produced identical cerebral blood flow (CBF) responses. ICP generated by the new model reacted in a similar manner as the original model during ITs. Plateau pressure reached during ITs was however higher in the ISF model. Only the latter was successful in simulating the onset of ICP plateau waves in response to selective blood pressure manipulations. Discussion and conclusion: Our simulations highlighted the importance of including the ISF compartment, which provides mechanism explaining sustained haemodynamic influences on ICP. Consideration of such interactions enables accurate simulations of the cerebrovascular effects on ICP.

A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis.

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this article, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework produces high quality reconstructions and segmentations, leading to undersampling factors that are optimised on a scan-by-scan basis. This results in reduced scan times and automated analysis, enabling robust and accurate estimation of functional biomarkers. To demonstrate the feasibility of the proposed approach, we perform simulations of radial k-space acquisitions using in-vivo cine CMR data from 270 subjects from the UK Biobank (with synthetic phase) and in-vivo cine CMR data from 16 healthy subjects (with real phase). The results demonstrate that the optimal undersampling factor varies for different subjects by approximately 1 to 2 seconds per slice. We show that our method can produce quality-controlled images in a mean scan time reduced from 12 to 4 seconds per slice, and that image quality is sufficient to allow clinically relevant parameters to be automatically estimated to lie within 5% mean absolute difference.