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Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

Abstract:
Background While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification 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 identified with distinct GCS and composite systemic metabolic stress profiles, 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 classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly 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 identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care.publishedVersio
Publication status:
Published
Peer review status:
Peer reviewed

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Author
ORCID:
0000-0003-4918-1482
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Role:
Author
ORCID:
0000-0002-3377-9087
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Author
ORCID:
0000-0003-3250-6834
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Author
ORCID:
0000-0002-7787-0122


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Funder identifier:
10.13039/501100004047


Publisher:
BioMed Central
Journal:
Critical Care More from this journal
Volume:
26
Issue:
1
Pages:
228-228
Article number:
228
Publication date:
2022-07-27
DOI:
EISSN:
1364-8535
ISSN:
1364-8535


Language:
English
Keywords:
Pubs id:
1299814
Local pid:
pubs:1299814
Source identifiers:
W4288723913
Deposit date:
2026-04-29
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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