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001-es BibID:	BIBFORM107337
035-os BibID:	(cikkazonosító)228 (scopus)85135370588 (wos)000831208500002
Első szerző:	Åkerlund, Cecilia
Cím:	Clustering identifies endotypes of traumatic brain injury in an intensive care cohort : a CENTER-TBI study / Ảkerlund Cecilia A. I., Holst Anders, Stocchetti Nino, Steyerberg Ewout W., Menon David K., Ercole Ari, Nelson David W., CENTER-TBI Participants and Investigators
Dátum:	2022
ISSN:	1364-8535
Megjegyzések:	Abstract Background: While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classifcation of traumatic brain injury (TBI) as ♭mild', ♭moderate' or ♭severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights. Methods: We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (<24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N=1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation. Results: Six stable endotypes were identifed with distinct GCS and composite systemic metabolic stress profles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ♭moderate' TBI (by traditional classifcation) and deranged metabolic profle, had a worse outcome than a cluster with ♭severe' GCS and a normal metabolic profle. Addition of cluster labels signifcantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p<0.001). Conclusions: Six stable and clinically distinct TBI endotypes were identifed by probabilistic unsupervised clustering. In addition to presenting neurology, a profle of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refning current TBI classifcations with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care. Trial registration The core study was registered with ClinicalTrials.gov, number NCT02210221, registered on August 06, 2014, with Resource Identifcation Portal (RRID: SCR_015582)
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Traumatic brain injury Endotypes Intensive care unit Critical care Unsupervised clustering Machine learning
Megjelenés:	Critical Care. - 26 : 1 (2022), p. 1-15. -
További szerzők:	Holst, Anders Stocchetti, Nino Steyerberg, Ewout W. Menon, David Krishna Ercole, Ari Nelson, David W. Sándor János (1966-) (orvos-epidemiológus) CENTER-TBI Participants and Investigators
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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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