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001-es BibID:	BIBFORM042451
035-os BibID:	PMID:22435839
Első szerző:	Czeiter Endre
Cím:	Brain injury biomarkers may improve the predictive power of the IMPACT outcome calculator / Endre Czeiter, Stefania Mondello, Noemi Kovacs, Janos Sandor, Andrea Gabrielli, Kara Schmid, Frank Tortella, Kevin K. W. Wang, Ronald L. Hayes, Pal Barzo, Erzsebet Ezer, Tamas Doczi, Andras Buki
Dátum:	2012
ISSN:	0897-7151
Megjegyzések:	Outcome prediction following severe traumatic brain injury (sTBI) is a widely investigated field of research. A major breakthrough is represented by the IMPACT prognostic calculator based on admission data of more than 8500 patients. A growing body of scientific evidence has shown that clinically meaningful biomarkers, including glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), and αII-spectrin breakdown product (SBDP145), could also contribute to outcome prediction. The present study was initiated to assess whether the addition of biomarkers to the IMPACT prognostic calculator could improve its predictive power. Forty-five sTBI patients (GCS score8) from four different sites were investigated. We utilized the core model of the IMPACT calculator (age, GCS motor score, and reaction of pupils), and measured the level of GFAP, UCH-L1, and SBDP145 in serum and cerebrospinal fluid (CSF). The forecast and actual 6-month outcomes were compared by logistic regression analysis. The results of the core model itself, as well as serum values of GFAP and CSF levels of SBDP145, showed a significant correlation with the 6-month mortality using a univariate analysis. In the core model, the Nagelkerke R(2) value was 0.214. With multivariate analysis we were able to increase this predictive power with one additional biomarker (GFAP in CSF) to R(2)=0.476, while the application of three biomarker levels (GFAP in CSF, GFAP in serum, and SBDP145 in CSF) increased the Nagelkerke R(2) to 0.700. Our preliminary results underline the importance of biomarkers in outcome prediction, and encourage further investigation to expand the predictive power of contemporary outcome calculators and prognostic models in TBI.
Tárgyszavak:	Orvostudományok Egészségtudományok idegen nyelvű folyóiratközlemény külföldi lapban biomarkers IMPACT calculator outcome prognostic models traumatic brain injury külföldön készült közlemény
Megjelenés:	Journal of Neurotrauma. - 29 : 9 (2012), p. 1770-1778. -
További szerzők:	Mondello, Stefania Kovács Noémi Sándor János (1966-) (orvos-epidemiológus) Gabrielli Andrea Schmid, Kara Tortella, Frank Wang, Kevin K. W. Hayes, Ronald L. Barzó Pál Ezer Erzsébet Dóczi Tamás Büki András (1990-) (általános orvos)
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001-es BibID:	BIBFORM104823
035-os BibID:	(WOS)000524528800009 (Scopus)85083076089
Első szerző:	Gravesteijn, Benjamin Yaël
Cím:	Toward a New Multi-Dimensional Classification of Traumatic Brain Injury : a Collaborative European NeuroTrauma Effectiveness Research for Traumatic Brain Injury Study / Benjamin Gravesteijn, Charlie Sewalt, Ari Ercole, Cecilia Akerlund, David Nelson, Andrew Maas, David Menon, Hester F. Lingsma, Ewout W. Steyerberg, CENTER-TBI collaboration
Dátum:	2020
ISSN:	0897-7151
Megjegyzések:	Traumatic brain injury (TBI) is currently classified as mild, moderate, or severe TBI by trichotomizing the Glasgow Coma Scale (GCS). We aimed to explore directions for a more refined multidimensional classification system. For that purpose, we performed a hypothesis-free cluster analysis in the Collaborative European NeuroTrauma Effectiveness Research for TBI (CENTER-TBI) database: a European all-severity TBI cohort (n?=?4509). The first building block consisted of key imaging characteristics, summarized using principal component analysis from 12 imaging characteristics. The other building blocks were demographics, clinical severity, secondary insults, and cause of injury. With these building blocks, the patients were clustered into four groups. We applied bootstrap resampling with replacement to study the stability of cluster allocation. The characteristics that predominantly defined the clusters were injury cause, major extracranial injury, and GCS. The clusters consisted of 1451, 1534, 1006, and 518 patients, respectively. The clustering method was quite stable: the proportion of patients staying in one cluster after resampling and reclustering was 97.4% (95% confidence interval [CI]: 85.6?99.9%). These clusters characterized groups of patients with different functional outcomes: from mild to severe, 12%, 19%, 36%, and 58% of patients had unfavorable 6 month outcome. Compared with the mild and the upper intermediate cluster, the lower intermediate and the severe cluster received more key interventions. To conclude, four types of TBI patients may be defined by injury mechanism, presence of major extracranial injury and GCS. Describing patients according to these three characteristics could potentially capture differences in etiology and care pathways better than with GCS only.
Tárgyszavak:	Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Journal Of Neurotrauma. - 37 : 7 (2020), p. 1002-1010. -
További szerzők:	Sewalt, Charlie Aletta Ercole, Ari Åkerlund, Cecilia Nelson, David Maas, Andrew I. R. Menon, David Krishna Lingsma, Hester Steyerberg, Ewout W. Sándor János (1966-) (orvos-epidemiológus) CENTER-TBI collaborators
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001-es BibID:	BIBFORM072261
Első szerző:	Mondello, Stefania
Cím:	Blood-based protein biomarkers for the management of traumatic brain injuries in adults presenting to emergency departments with mild brain injury : a living systematic review and meta-analysis / Stefania Mondello, Abayomi Sorinola, Endre Czeiter, Zoltán Vámos, Krisztina Amrein, Anneliese Synnot, Emma Donoghue, János Sándor, Kevin K. W. Wang, Ramon Diaz-Arrastia, Ewout W. Steyerberg, David K. Menon, Andrew I. R. Maas, Andras Buki
Dátum:	2021
ISSN:	0897-7151 1557-9042
Megjegyzések:	Accurate diagnosis of traumatic brain injury (TBI) is critical to effective management and intervention, but can be challenging in patients with mild TBI. A substantial number of studies have reported the use of circulating biomarkers as signatures for TBI, capable of improving diagnostic accuracy and clinical decision-making beyond current practice standards. We performed a systematic review and meta-analysis to comprehensively and critically evaluate the existing body of evidence for the use of blood protein biomarkers (S100B, GFAP, NSE, UCH-L1, Tau and Neurofilament proteins) for diagnosis of intracranial lesions on CT following mild TBI. Effects of potential confounding factors and differential diagnostic performance of the included markers were explored. Furthermore, appropriateness of study design, analysis, quality and demonstration of clinical utility were assessed. Studies published up to October 2016 were identified through a MEDLINE, EMBASE and CINHAL search. Following screening of the identified articles, 26 were selected as relevant. We found that measurement of S100B can help informed decision making in the emergency department possibly reducing resource use, but there is insufficient evidence that any of the other markers is ready for clinical application. Our work pointed out serious problems in the design, analysis and reporting of many of the studies and identified a substantial heterogeneity and research gaps. These findings emphasize the importance of methodologically rigorous studies focused on a biomarker's intended use and defining standardized, validated and reproducible approaches. The living nature of this systematic review, which will summarize key updated information as it becomes available, can inform and guide future implementation of biomarkers in the clinical arena.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban Biomarkers Traumatic Brain Injury Diagnosis Living Systematic review Meta analysis
Megjelenés:	Journal of Neurotrauma. - 38 : 8 (2021), p. 1086-1106. -
További szerzők:	Sorinola, Abayomi Czeiter Endre Vámos Zoltán Amrein Krisztina Synnot, Anneliese Donoghue, Emma Sándor János (1966-) (orvos-epidemiológus) Wang, Kevin K. W. Diaz-Arrastia, Ramon Steyerberg, Ewout W. Menon, David Krishna Maas, Andrew I. R. Buki András
Pályázati támogatás:	602150 FP7
Internet cím:	DOI Intézményi repozitóriumban (DEA) tárolt változat
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