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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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DOI
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2.

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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3.

001-es BibID:BIBFORM100330
Első szerző:Huie, J. Russell
Cím:Biomarkers for Traumatic Brain Injury : Data Standards and Statistical Considerations / Huie J. Russell, Mondello Stefania, Lindsell Christopher J., Antiga Luca, Yuh Esther L., Zanier Elisa R., Masson Serge, Rosario Bedda L., Ferguson Adam R., TRACK-TBI Investigators, CENTER-TBI Participants and Investigators
Dátum:2021
ISSN:0897-7151
Megjegyzések:Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps?discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Journal Of Neurotrauma. - 38 : 18 (2021), p. 2514-2529. -
További szerzők:Mondello, Stefania Lindsell, Christopher J. Antiga, Luca Yuh, Esther L. Zanier, Elisa R. Masson, Serge Rosario, Bedda L. Ferguson, Adam R. Sándor János (1966-) (orvos-epidemiológus) TRACK-TBI Investigators CENTER-TBI Participants and Investigators
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4.

001-es BibID:BIBFORM116192
035-os BibID:(Scopus)85168315877 (WOS)000993931100001
Első szerző:Mikolić, Ana
Cím:Prognostic Models for Global Functional Outcome and Post-Concussion Symptoms Following Mild Traumatic Brain Injury : A Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study / Ana Mikolic, Ewout W. Steyerberg, Suzanne Polinder, Lindsay Wilson, Marina Zeldovich, Nicole von Steinbuechel, Virginia F. J. Newcombe, David K. Menon, Joukjevander Naalt, Hester F. Lingsma, Andrew I. R. Maas, David van Klaveren, CENTER-TBI Participants and Investigators
Dátum:2023
ISSN:0897-7151
Megjegyzések:After mild traumatic brain injury (mTBI), a substantial proportion of individuals do not fully recover on the Glasgow Outcome Scale Extended (GOSE) or experience persistent post-concussion symptoms (PPCS). We aimed to develop prognostic models for the GOSE and PPCS at 6 months after mTBI and to assess the prognostic value of different categories of predictors (clinical variables; questionnaires; computed tomography [CT]; blood biomarkers). From the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we included participants aged 16 or older with Glasgow Coma Score (GCS) 13-15. We used ordinal logistic regression to model the relationship between predictors and the GOSE, and linear regression to model the relationship between predictors and the Rivermead Post-concussion Symptoms Questionnaire (RPQ) total score. First, we studied a pre-specified Core model. Next, we extended the Core model with other clinical and sociodemographic variables available at presentation (Clinical model). The Clinical model was then extended with variables assessed before discharge from hospital: early post-concussion symptoms, CT variables, biomarkers, or all three categories (extended models). In a subset of patients mostly discharged home from the emergency department, the Clinical model was extended with 2-3-week post-concussion and mental health symptoms. Predictors were selected based on Akaike's Information Criterion. Performance of ordinal models was expressed as a concordance index (C) and performance of linear models as proportion of variance explained (R2). Bootstrap validation was used to correct for optimism. We included 2376 mTBI patients with 6-month GOSE and 1605 patients with 6-month RPQ. The Core and Clinical models for GOSE showed moderate discrimination (C = 0.68 95% confidence interval 0.68 to 0.70 and C = 0.70[0.69 to 0.71], respectively) and injury severity was the strongest predictor. The extended models had better discriminative ability (C = 0.71[0.69 to 0.72] with early symptoms; 0.71[0.70 to 0.72] with CT variables or with blood biomarkers; 0.72[0.71 to 0.73] with all three categories). The performance of models for RPQ was modest (R2 = 4% Core; R2 = 9% Clinical), and extensions with early symptoms increased the R2 to 12%. The 2-3-week models had better performance for both outcomes in the subset of participants with these symptoms measured (C = 0.74 [0.71 to 0.78] vs. C = 0.63[0.61 to 0.67] for GOSE; R2 = 37% vs. 6% for RPQ). In conclusion, the models based on variables available before discharge have moderate performance for the prediction of GOSE and poor performance for the prediction of PPCS. Symptoms assessed at 2-3 weeks are required for better predictive ability of both outcomes. The performance of the proposed models should be examined in independent cohorts.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
biomarkers
Glasgow Outcome Scale Extended
mild traumatic brain injury
post-concussion symptoms
predictors
prognostic model
Megjelenés:Journal Of Neurotrauma. - 40 : 15-16 (2023), p. 1651-1670. -
További szerzők:Steyerberg, Ewout W. Polinder, Suzanne Wilson, Lindsay Zeldovich, Marina von Steinbuechel, Nicole Newcombe, Virginia F. J. Menon, David Krishna Naalt, Joukjevander Lingsma, Hester Maas, Andrew I. R. van Klaveren, David Sándor János (1966-) (orvos-epidemiológus) CENTER-TBI Participants and Investigators
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5.

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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