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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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