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001-es BibID:BIBFORM104837
035-os BibID:(Scopus)85088991849 (WOS)000572673400042
Első szerző:Gravesteijn, Benjamin Yaël
Cím:Tracheal intubation in traumatic brain injury : a multicentre prospective observational study / Benjamin Yael Gravesteijn, Charlie Aletta Sewalt, Daan Nieboer, David Krishna Menon, Andrew Maas, Fiona Lecky, Markus Klimek, Hester Floor Lingsma, CENTERTBI collaboratorsy
Dátum:2020
ISSN:0007-0912
Megjegyzések:Abstract Background: We aimed to study the associations between pre- and in-hospital tracheal intubation and outcomes in traumatic brain injury (TBI), and whether the association varied according to injury severity. Methods: Data from the international prospective pan-European cohort study, Collaborative European NeuroTrauma Effectiveness Research for TBI (CENTER-TBI), were used (n?4509). For prehospital intubation, we excluded self- presenters. For in-hospital intubation, patients whose tracheas were intubated on-scene were excluded. The association between intubation and outcome was analysed with ordinal regression with adjustment for the International Mission for Prognosis and Analysis of Clinical Trials in TBI variables and extracranial injury. We assessed whether the effect of intubation varied by injury severity by testing the added value of an interaction term with likelihood ratio tests. Results: In the prehospital analysis, 890/3736 (24%) patients had their tracheas intubated at scene. In the in-hospital analysis, 460/2930 (16%) patients had their tracheas intubated in the emergency department. There was no adjusted overall effect on functional outcome of prehospital intubation (odds ratio?1.01; 95% confidence interval, 0.79e1.28; P?0.96), and the adjusted overall effect of in-hospital intubation was not significant (odds ratio?0.86; 95% confidence interval, 0.65e1.13; P?0.28). However, prehospital intubation was associated with better functional outcome in patients with higher thorax and abdominal Abbreviated Injury Scale scores (P?0.009 and P?0.02, respectively), whereas in- hospital intubation was associated with better outcome in patients with lower Glasgow Coma Scale scores (P?0.01): in- hospital intubation was associated with better functional outcome in patients with Glasgow Coma Scale scores of 10 or lower. Conclusion: The benefits and harms of tracheal intubation should be carefully evaluated in patients with TBI to optimise benefit. This study suggests that extracranial injury should influence the decision in the prehospital setting, and level of consciousness in the in-hospital setting. Clinical trial registration: NCT02210221
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
effectiveness
Europe neurological outcome
prehospital tracheal intubation
traumatic brain injury
Megjelenés:British Journal Of Anaesthesia. - 125 : 4 (2020), p. 505-517. -
További szerzők:Sewalt, Charlie Aletta Nieboer, Daan Menon, David Krishna Maas, Andrew I. R. Lecky, Fiona Klimek, Markus Lingsma, Hester Sándor János (1966-) (orvos-epidemiológus) CENTER-TBI collaborators
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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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Intézményi repozitóriumban (DEA) tárolt változat
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3.

001-es BibID:BIBFORM104832
Első szerző:Gravesteijn, Benjamin Yaël
Cím:Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury / Benjamin Y. Gravesteijn, Daan Nieboer, Ari Ercole, Hester F. Lingsma, David Nelson, Ben van Calster, Ewout W. Steyerberg, CENTER-TBI collaborators
Dátum:2020
ISSN:0895-4356
Megjegyzések:Objective We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Machine learning
Prognosis
Traumatic brain injury
Prediction
Data science
Cohort study
Megjelenés:Journal Of Clinical Epidemiology. - 122 (2020), p. 95-107. -
További szerzők:Nieboer, Daan Ercole, Ari Lingsma, Hester Nelson, David Calster, Ben van Steyerberg, Ewout W. Sándor János (1966-) (orvos-epidemiológus) CENTER-TBI collaborators
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4.

001-es BibID:BIBFORM100333
035-os BibID:(cikkazonosító)113
Első szerző:Sewalt, Charlie Aletta
Cím:Primary versus early secondary referral to a specialized neurotrauma center in patients with moderate/severe traumatic brain injury : a CENTER TBI study / Sewalt Charlie Aletta, Gravesteijn Benjamin Yaël, Menon David, Lingsma Hester Floor, Maas Andrew I. R., Stocchetti Nino, Venema Esmee, Lecky Fiona E., CENTER-TBI Participants and Investigators
Dátum:2021
ISSN:1757-7241
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - 29 : 1 (2021), p. 1-11. -
További szerzők:Gravesteijn, Benjamin Yaël Menon, David Krishna Lingsma, Hester Maas, Andrew I. R. Stocchetti, Nino Venema, Esmee Lecky, Fiona Sándor János (1966-) (orvos-epidemiológus) CENTER-TBI Participants and Investigators
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