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.

Introduction The standardized diagnostic criteria for computed tomographic angiography (CTA) in diagnosis of brain death (BD) are not yet established. The aim of the study was to compare the sensitivity and interobserver agreement of the three previously used scales of CTA for the diagnosis of BD. Methods Eighty-two clinically brain-dead patients underwent CTA with a delay of 40 s after contrast injection. Catheter angiography was used as the reference standard. CTA results were assessed by two radiologists, and the diagnosis of BD was established according to 10-, 7-, and 4-point scales. Results Catheter angiography confirmed the diagnosis of BD in all cases. Opacification of certain cerebral vessels as indicator of BD was highly sensitive: cortical segments of the middle cerebral artery (96.3 %), the internal cerebral vein (98.8 %), and the great cerebral vein (98.8 %). Other vessels were less sensitive: the pericallosal artery (74.4 %), cortical segments of the posterior cerebral artery (79.3 %), and the basilar artery (82.9 %). The sensitivities of the 10-, 7-, and 4-point scales were 67.1, 74.4, and 96.3 %, respectively (p < 0.001). Percentage interobserver agreement in diagnosis of BD reached 93 % for the 10-point scale, 89 % for the 7-point scale, and 95 % for the 4-point scale (p = 0.37). Conclusions In the application of CTA to the diagnosis of BD, reducing the assessment of vascular opacification scale from a 10- to a 4-point scale significantly increases the sensitivity and maintains high interobserver reliability.

Objectives: To compare the performance of multivariate predictive models incorporating either the Full Outline of UnResponsiveness (FOUR) score or Glasgow Coma Score (GCS) in order to test whether substituting GCS with the FOUR score in predictive models for outcome in patients after TBI is beneficial. Material and methods: A total of 162 TBI patients were prospectively enrolled in the study. Stepwise logistic regression analysis was conducted to compare the prediction of (1) in-ICU mortality and (2) unfavourable outcome at 3 months post-injury using as predictors either the FOUR score or GCS along with other factors that may affect patient outcome. The areas under the ROC curves (AUCs) were used to compare the discriminant ability and predictive power of the models. The internal validation was performed with bootstrap technique and expressed as accuracy rate (AcR). Results: The FOUR score, age, the CT Rotterdam score, systolic ABP and being placed on ventilator within day one (model 1: AUC: 0.906 ± 0.024; AcR: 80.3 ± 4.8%) performed equally well in predicting in-ICU mortality as the combination of GCS with the same set of predictors plus pupil reactivity (model 2: AUC: 0.913 ± 0.022; AcR: 81.1 ± 4.8%). The CT Rotterdam score, age and either the FOUR score (model 3) or GCS (model 4) equally well predicted unfavourable outcome at 3 months post-injury (AUC: 0.852 ± 0.037 vs. 0.866 ± 0.034; AcR: 72.3 ± 6.6% vs. 71.9%±6.6%, respectively). Adding the FOUR score or GCS at discharge from ICU to predictive models for unfavourable outcome increased significantly their performances (AUC: 0.895 ± 0.029, p = 0.05; AcR: 76.1 ± 6.5%; p < 0.004 when compared with model 3; and AUC: 0.918 ± 0.025, p < 0.05; AcR: 79.6 ± 7.2%, p < 0.009 when compared with model 4), but there was no benefit from substituting GCS with the FOUR score. Conclusion: Results showed that FOUR score and GCS perform equally well in multivariate predictive modelling in TBI.

In this preliminary study we investigated the relationship between the time of cerebral desaturation episodes (CDEs), the severity of the haemorrhage, and the short-term outcome in patients with aneurysmal subarachnoid haemorrhage (aSAH). Thirty eight patents diagnosed with aneurysmal subarachnoid haemorrhage were analysed in this study. Regional cerebral oxygenation (rSO₂) was assessed using near infrared spectroscopy (NIRS). A CDE was defined as rSO₂ < 60% with a duration of at least 30 min. The severity of the aSAH was assessed using the Hunt and Hess scale and the short-term outcome was evaluated utilizing the Glasgow Outcome Scale. CDEs were found in 44% of the group. The total time of the CDEs and the time of the longest CDE on the contralateral side were longer in patients with severe versus moderate aSAH [h:min]: 8:15 (6:26–8:55) versus 1:24 (1:18–4:18), p = 0.038 and 2:05 (2:00–5:19) versus 0:48 (0:44–2:12), p = 0.038. The time of the longest CDE on the ipsilateral side was longer in patients with poor versus good short–term outcome [h:min]: 5:43 (3:05–9:36) versus 1:47 (0:42–2:10), p = 0.018. The logistic regression model for poor short-term outcome included median ABP, the extent of the haemorrhage in the Fisher scale and the time of the longest CDE. We have demonstrated that the time of a CDE is associated with the severity of haemorrhage and short-term outcome in aSAH patients. A NIRS measurement may provide valuable predictive information and could be considered as additional method of neuromonitoring of patients with aSAH.

The pathophysiological mechanisms underlying severe cardiac dysfunction after aneurysmal subarachnoid haemorrhage (aSAH) remain poorly understood. In the present study, we focused on two categories of contributing factors describing the brain–heart relationship. The first group includes brain-specific cerebrospinal fluid (CSF) and serum biomarkers, as well as cardiac-specific biomarkers. The secondary category encompasses parameters associated with cerebral autoregulation and the autonomic nervous system. A group of 15 aSAH patients were included in the analysis. Severe cardiac complications were diagnosed in seven (47%) of patients. In the whole population, a significant correlation was observed between CSF S100 calcium-binding protein B (S100B) and brain natriuretic peptide (BNP) (r_S = 0.62; p = 0.040). Additionally, we identified a significant correlation between CSF neuron-specific enolase (NSE) with cardiac troponin I (r_S = 0.57; p = 0.025) and BNP (r_S = 0.66; p = 0.029), as well as between CSF tau protein and BNP (r_S = 0.78; p = 0.039). Patients experiencing severe cardiac complications exhibited notably higher levels of serum tau protein at day 1 (0.21 ± 0.23 [ng/mL]) compared to those without severe cardiac complications (0.03 ± 0.04 [ng/mL]); p = 0.009. Impaired cerebral autoregulation was noted in patients both with and without severe cardiac complications. Elevated serum NSE at day 1 was related to impaired cerebral autoregulation (r_S = 0.90; p = 0.037). On the first day, a substantial, reciprocal correlation between heart rate variability low-to-high frequency ratio (HRV LF/HF) and both GFAP (r_S = −0.83; p = 0.004) and S100B (r_S = −0.83; p = 0.004) was observed. Cardiac and brain-specific biomarkers hold the potential to assist clinicians in providing timely insights into cardiac complications, and therefore they contribute to the prognosis of outcomes.