Background Cerebrospinal compliance describes the ability of the cerebrospinal space to buffer changes in volume. Diminished compliance is associated with increased risk of potentially threatening increases in intracranial pressure (ICP) when changes in cerebrospinal volume occur. However, despite various methods of estimation proposed so far, compliance is seldom used in clinical practice. This study aimed to compare three measures of cerebrospinal compliance. Methods ICP recordings from 36 normal-pressure hydrocephalus patients who underwent infusion tests with parallel recording of transcranial Doppler blood flow velocity were retrospectively analysed. Three methods were used to calculate compliance estimates during changes in the mean ICP induced by infusion of fluid into the cerebrospinal fluid space: (a) based on Marmarou’s model of cerebrospinal fluid dynamics (CCSF), (b) based on the evaluation of changes in cerebral arterial blood volume (CCaBV), and (c) based on the amplitudes of peaks P1 and P2 of ICP pulse waveform (CP1/P2). Results Increase in ICP caused a significant decrease in all compliance estimates (p < 0.0001). Time courses of compliance estimators were strongly positively correlated with each other (group-averaged Spearman correlation coefficients: 0.94 [0.88–0.97] for CCSF vs. CCaBV, 0.77 [0.63–0.91] for CCSF vs. CP1/P2, and 0.68 [0.48–0.91] for CCaBV vs. CP1/P2). Conclusions Indirect methods, CCaBV and CP1/P2, allow for the assessment of relative changes in cerebrospinal compliance and produce results exhibiting good correlation with the direct method of volumetric manipulation. This opens the possibility of monitoring relative changes in compliance continuously.
Objective. Mean intracranial pressure (ICP) is commonly used in the management of patients with intracranial pathologies. However, the shape of the ICP signal over a single cardiac cycle, called ICP pulse waveform, also contains information on the state of the craniospinal space. In this study we aimed to propose an end-to-end approach to classification of ICP waveforms and assess its potential clinical applicability. Methods. ICP pulse waveforms obtained from long-term ICP recordings of 50 neurointensive care unit (NICU) patients were manually classified into four classes ranging from normal to pathological. An additional class was introduced to simultaneously identify artifacts. Several deep learning models and data representations were evaluated. An independent testing dataset was used to assess the performance of final models. Occurrence of different waveform types was compared with the patients' clinical outcome. Results. Residual Neural Network using 1-D ICP signal as input was identified as the best performing model with accuracy of 93% in the validation and 82% in the testing dataset. Patients with unfavorable outcome exhibited significantly lower incidence of normal waveforms compared to the favorable outcome group even at ICP levels below 20 mm Hg (median [first-third quartile]: 9 [1-36] % vs. 63 [52-88] %, p=0.002).Conclusions. Results of this study confirm the possibility of analyzing ICP pulse waveform morphology in long-term recordings of NICU patients. Proposed approach could potentially be used to provide additional information on the state of patients with intracranial pathologies beyond mean ICP.
Our results indicated that lower values of HRV indices and BRS correlate with mortality and that there is a link between cerebral dysautoregulation and the analysed estimates of the ANS in aSAH patients.
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