@article{MTMT:35198213, title = {Predicting recovery in patients with mild traumatic brain injury and a normal CT using serum biomarkers and diffusion tensor imaging (CENTER-TBI): an observational cohort study}, url = {https://m2.mtmt.hu/api/publication/35198213}, author = {Richter, S and Winzeck, S and Correia, MM and Czeiter, Endre and Whitehouse, D and Kornaropoulos, EN and Williams, GB and Verheyden, J and Das, T and Tenovuo, O and Posti, JP and Vik, A and Moen, KG and Håberg, AK and Wang, K and Büki, András and Maas, A and Steyerberg, E and Menon, DK and Newcombe, VFJ}, doi = {10.1016/j.eclinm.2024.102751}, journal-iso = {ECLINICALMEDICINE}, journal = {ECLINICALMEDICINE}, unique-id = {35198213}, issn = {2589-5370}, year = {2024}, eissn = {2589-5370}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @{MTMT:34823697, title = {Az agyi neurobiomarkerek diagnosztikus és prognosztikus értéke}, url = {https://m2.mtmt.hu/api/publication/34823697}, author = {Czeiter, Endre and Amrein, Krisztina}, booktitle = {Súlyos baleseti agysérültek ellátása}, unique-id = {34823697}, year = {2024}, pages = {63-66}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @{MTMT:34823375, title = {Az agysérülés neuroanatómiája}, url = {https://m2.mtmt.hu/api/publication/34823375}, author = {Czeiter, Endre and Fazekas, Bálint and Amrein, Krisztina}, booktitle = {Súlyos baleseti agysérültek ellátása}, unique-id = {34823375}, year = {2024}, pages = {33-46}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944; Fazekas, Bálint/0000-0002-8445-4100} } @article{MTMT:34724965, title = {Clinical descriptors of disease trajectories in patients with traumatic brain injury in the intensive care unit (CENTER-TBI). a multicentre observational cohort study.}, url = {https://m2.mtmt.hu/api/publication/34724965}, author = {Åkerlund, Cecilia A I and Holst, Anders and Bhattacharyay, Shubhayu and Stocchetti, Nino and Steyerberg, Ewout and Smielewski, Peter and Menon, David K and Ercole, Ari and Nelson, David W}, doi = {10.1016/S1474-4422(23)00358-7}, journal-iso = {LANCET NEUROL}, journal = {LANCET NEUROLOGY}, volume = {23}, unique-id = {34724965}, issn = {1474-4422}, abstract = {Patients with traumatic brain injury are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the harmful secondary events that can occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical variables that might distinguish disease trajectories among patients with traumatic brain injury admitted to the ICU.We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated.Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E ≤4) was improved.First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.European Union 7th Framework program, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences Corporation, and NeuroTrauma Sciences.}, year = {2024}, eissn = {1474-4465}, pages = {71-80}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @article{MTMT:34477399, title = {Screening Performance of S100B, GFAP and UCH-L1 For Intracranial Injury Within 6 hours of Injury and beyond}, url = {https://m2.mtmt.hu/api/publication/34477399}, author = {Trivedi, Dhanisha and Forssten, Maximilian Peter and Cao, Yang and Mohammad Ismail, Ahmad and Czeiter, Endre and Amrein, Krisztina and Kobeissy, Firas and Wang, Kevin K W and DeSoucy, Erik and Büki, András and Mohseni, Shahin}, doi = {10.1089/neu.2023.0322}, journal-iso = {J NEUROTRAUM}, journal = {JOURNAL OF NEUROTRAUMA}, volume = {41}, unique-id = {34477399}, issn = {0897-7151}, abstract = {The Scandinavian NeuroTrauma Committee (SNC) guidelines recommend S100B as a screening tool for early detection of Traumatic brain injury (TBI) in patients presenting with an initial Glasgow coma scale (GCS) of 14-15. The objective of the current study was to compare S100B's diagnostic performance within the recommended 6-hour window after injury, compared to GFAP and UCH-L1. The secondary outcome of interest was the ability of these biomarkers in detecting traumatic intracranial pathology beyond the 6-hour mark.The Center-TBI core database (2014-2017) was queried for data pertaining to all TBI patients with an initial GCS of 14-15 who had a blood sample taken within 6 hours of injury in which the levels of S100B, GFAP, and UCH-L1 were measured. As a subgroup analysis, data involving patients with blood samples taken within 6-9 hours, and 9-12 hours were analyzed separately for diagnostic ability. The diagnostic ability of these biomarkers for detecting any intracranial injury was evaluated based on the area under the receiver operating characteristic curve (AUC). Each biomarker's sensitivity, specificity, and accuracy were also reported at the cutoff that maximized Youden's index.A total of 531 TBI patients with GCS 14-15 on admission had a blood sample taken within 6 hours, of whom 24.9% (N = 132) had radiologically confirmed intracranial injury. The AUCs of GFAP (0.86, 95% confidence interval (CI): 0.82-0.90) and UCH-L1 (0.81, 95% CI: 0.76-0.85) were statistically significantly higher than that of S100B (0.74, 95% CI: 0.69-0.79) during this time. There was no statistically significant difference in the predictive ability of S100B when sampled within 6 hours, 6-9 hours, and 9-12 hours of injury, as the p-values were >0.05 when comparing the AUCs. Overlapping AUC 95% CI suggests no benefit of a combined GFAP and UCH-L1 screening tool over GFAP during the time periods studied [ 0.87 (0.83-0.90) vs 0.86 (0.82-0.90) when sampled within 6 hours of injury, 0.83 (0.78-0.88) vs 0.83 (0.78-0.89) within 6-to-9 hours and 0.81 (0.73-0.88) vs 0.79 (0.72-0.87) within 9-12 hours].Targeted analysis of the CENTER-TBI core database, with focus on the patient category for which biomarker testing is recommended by the SNC guidelines, revealed that GFAP and UCH-L1 perform superior to S100B in predicting CT-positive intracranial lesions within 6 hours of injury. GFAP continued to exhibit superior predictive ability to S100B during the time periods studied. S100B displayed relatively unaltered screening performance beyond the diagnostic timeline provided by SNC guidelines. These findings suggest the need for a re-evaluation of the current SNC TBI guidelines.}, keywords = {Biomarkers; traumatic brain injury; Head trauma; adult brain injury}, year = {2024}, eissn = {1557-9042}, pages = {349-358}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @article{MTMT:34474356, title = {Blood biomarkers for traumatic brain injury: A narrative review of current evidence}, url = {https://m2.mtmt.hu/api/publication/34474356}, author = {Hossain, I. and Marklund, N. and Czeiter, Endre and Hutchinson, P. and Büki, András}, doi = {10.1016/j.bas.2023.102735}, journal-iso = {BRAIN SPINE}, journal = {BRAIN AND SPINE}, volume = {4}, unique-id = {34474356}, issn = {2772-5294}, year = {2024}, eissn = {2772-5294}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @article{MTMT:35141163, title = {Relationship between the shape of intracranial pressure pulse waveform and computed tomography characteristics in patients after traumatic brain injury}, url = {https://m2.mtmt.hu/api/publication/35141163}, author = {Kazimierska, Agnieszka and Uryga, Agnieszka and Mataczynski, Cyprian and Czosnyka, Marek and Lang, Erhard W. and Kasprowicz, Magdalena}, doi = {10.1186/s13054-023-04731-z}, journal-iso = {CRIT CARE}, journal = {CRITICAL CARE}, volume = {27}, unique-id = {35141163}, issn = {1364-8535}, abstract = {Background Midline shift and mass lesions may occur with traumatic brain injury (TBI) and are associated with higher mortality and morbidity. The shape of intracranial pressure (ICP) pulse waveform reflects the state of cerebrospinal pressure-volume compensation which may be disturbed by brain injury. We aimed to investigate the link between ICP pulse shape and pathological computed tomography (CT) features.Methods ICP recordings and CT scans from 130 TBI patients from the CENTER-TBI high-resolution sub-study were analyzed retrospectively. Midline shift, lesion volume, Marshall and Rotterdam scores were assessed in the first CT scan after admission and compared with indices derived from the first 24 h of ICP recording: mean ICP, pulse amplitude of ICP (AmpICP) and pulse shape index (PSI). A neural network model was applied to automatically group ICP pulses into four classes ranging from 1 (normal) to 4 (pathological), with PSI calculated as the weighted sum of class numbers. The relationship between each metric and CT measures was assessed using Mann-Whitney U test (groups with midline shift > 5 mm or lesions > 25 cm(3) present/absent) and the Spearman correlation coefficient. Performance of ICP-derived metrics in identifying patients with pathological CT findings was assessed using the area under the receiver operating characteristic curve (AUC).Results PSI was significantly higher in patients with mass lesions (with lesions: 2.4 [1.9-3.1] vs. 1.8 [1.1-2.3] in those without; p << 0.001) and those with midline shift (2.5 [1.9-3.4] vs. 1.8 [1.2-2.4]; p < 0.001), whereas mean ICP and AmpICP were comparable. PSI was significantly correlated with the extent of midline shift, total lesion volume and the Marshall and Rotterdam scores. PSI showed AUCs > 0.7 in classification of patients as presenting pathological CT features compared to AUCs <= 0.6 for mean ICP and AmpICP.Conclusions ICP pulse shape reflects the reduction in cerebrospinal compensatory reserve related to space-occupying lesions despite comparable mean ICP and AmpICP levels. Future validation of PSI is necessary to explore its association with volume imbalance in the intracranial space and a potential complementary role to the existing monitoring strategies.}, keywords = {CLASSIFICATION; PREDICTION; MANAGEMENT; traumatic brain injury; computed tomography; Intracranial Pressure; Decompressive craniectomy; elastance; Morphological analysis; pulse waveform; VOLUME-PRESSURE}, year = {2023}, eissn = {1466-609X}, orcid-numbers = {Uryga, Agnieszka/0000-0001-8183-7643; Czeiter, Endre/0000-0002-9578-6944} } @article{MTMT:35073328, title = {Acute thalamic connectivity precedes chronic post-concussive symptoms in mild traumatic brain injury.}, url = {https://m2.mtmt.hu/api/publication/35073328}, author = {Woodrow, Rebecca E and Winzeck, Stefan and Luppi, Andrea I and Kelleher-Unger, Isaac R and Spindler, Lennart R B and Wilson, J T Lindsay and Newcombe, Virginia F J and Coles, Jonathan P and Menon, David K and Stamatakis, Emmanuel A}, doi = {10.1093/brain/awad056}, journal-iso = {BRAIN}, journal = {BRAIN}, volume = {146}, unique-id = {35073328}, issn = {0006-8950}, abstract = {Chronic post-concussive symptoms are common after mild traumatic brain injury (mTBI) and are difficult to predict or treat. Thalamic functional integrity is particularly vulnerable in mTBI and may be related to long-term outcomes but requires further investigation. We compared structural MRI and resting state functional MRI in 108 patients with a Glasgow Coma Scale (GCS) of 13-15 and normal CT, and 76 controls. We examined whether acute changes in thalamic functional connectivity were early markers for persistent symptoms and explored neurochemical associations of our findings using PET data. Of the mTBI cohort, 47% showed incomplete recovery 6 months post-injury. Despite the absence of structural changes, we found acute thalamic hyperconnectivity in mTBI, with specific vulnerabilities of individual thalamic nuclei. Acute fMRI markers differentiated those with chronic post-concussive symptoms, with time- and outcome-dependent relationships in a sub-cohort followed longitudinally. Moreover, emotional and cognitive symptoms were associated with changes in thalamic functional connectivity to known serotonergic and noradrenergic targets, respectively. Our findings suggest that chronic symptoms can have a basis in early thalamic pathophysiology. This may aid identification of patients at risk of chronic post-concussive symptoms following mTBI, provide a basis for development of new therapies and facilitate precision medicine application of these therapies.}, keywords = {Mild traumatic brain injury; Thalamus; functional connectivity; resting-state fMRI; Postconcussive symptoms}, year = {2023}, eissn = {1460-2156}, pages = {3484-3499}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @article{MTMT:35069617, title = {Towards autoregulation-oriented management after traumatic brain injury. increasing the reliability and stability of the CPPopt algorithm.}, url = {https://m2.mtmt.hu/api/publication/35069617}, author = {Beqiri, Erta and Ercole, Ari and Aries, Marcel J H and Placek, Michal M and Tas, Jeanette and Czosnyka, Marek and Stocchetti, Nino and Smielewski, Peter}, doi = {10.1007/s10877-023-01009-1}, journal-iso = {J CLIN MONITOR COMP}, journal = {JOURNAL OF CLINICAL MONITORING AND COMPUTING}, volume = {37}, unique-id = {35069617}, issn = {1387-1307}, abstract = {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.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.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.The CPPopt calculation algorithm was fine-tuned and adapted for prospective use with acceptable lower yield, improved stability and maintained prognostic power.}, keywords = {STABILITY; Reliability; traumatic brain injury; Cerebral autoregulation; CPPopt; Multiwindow weighted approach}, year = {2023}, eissn = {1573-2614}, pages = {963-976}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} } @article{MTMT:35069615, title = {Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.}, url = {https://m2.mtmt.hu/api/publication/35069615}, author = {Carra, Giorgia and Güiza, Fabian and Piper, Ian and Citerio, Giuseppe and Maas, Andrew and Depreitere, Bart and Meyfroidt, Geert}, doi = {10.1089/neu.2022.0251}, journal-iso = {J NEUROTRAUM}, journal = {JOURNAL OF NEUROTRAUMA}, volume = {40}, unique-id = {35069615}, issn = {0897-7151}, abstract = {Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is associated with secondary brain injury, and both intensity and duration of an episode of intracranial hypertension, often referred to as "ICP dose," are associated with worse outcomes. Prediction of such harmful episodes of ICP dose could allow for a more proactive and preventive management of TBI, with potential implications on patients' outcomes. The goal of this study was to develop and validate a machine-learning (ML) model to predict potentially harmful ICP doses in patients with severe TBI. The prediction target was defined based on previous studies and included a broad range of doses of elevated ICP that have been associated with poor long-term neurological outcomes. The ML models were used, with minute-by-minute ICP and mean arterial blood pressure signals as inputs. Harmful ICP episodes were predicted with a 30 min forewarning. Models were developed in a multi-center dataset of 290 adult patients with severe TBI and externally validated on 264 patients from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) dataset. The external validation of the prediction model on the CENTER-TBI dataset demonstrated good discrimination and calibration (area under the curve: 0.94, accuracy: 0.89, precision: 0.87, sensitivity: 0.78, specificity: 0.94, calibration-in-the-large: 0.03, calibration slope: 0.93). The proposed prediction model provides accurate and timely predictions of harmful doses of ICP on the development and external validation dataset. A future interventional study is needed to assess whether early intervention on the basis of ICP dose predictions will result in improved outcomes.}, keywords = {PREDICTION; machine learning; traumatic brain injury; Intracranial Pressure; intracranial pressure dose}, year = {2023}, eissn = {1557-9042}, pages = {514-522}, orcid-numbers = {Czeiter, Endre/0000-0002-9578-6944} }