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

Cecilia Åkerlund*, Anders Holst, Nino Stocchetti, Ewout W. Steyerberg, David K. Menon, Ari Ercole, David W. Nelson, the CENTER-TBI Participants and Investigators

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)
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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. Trial registration The core study was registered with ClinicalTrials.gov, number NCT02210221, registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).

Original languageEnglish
Article number228
JournalCritical Care
Volume26
Issue number1
DOIs
Publication statusPublished - 27 Jul 2022

Bibliographical note

Funding Information:
Open access funding provided by Karolinska Institute. CENTER-TBI was supported by the European Union 7 Framework program (EC grant 602150). Additional funding was obtained from the Hannelore Kohl Stiftung (Germany), from OneMind (USA) and from Integra LifeSciences Corporation (USA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. th

Funding Information:
Data for the CENTER-TBI study was collected through Quesgen e-CRF (Quesgen Systems Inc, USA), hosted on the INCF platform and extracted via the INCF Neurobot tool (INCF, Sweden). Version 3.0 of the CENTER-TBI dataset was used in this manuscript.

Funding Information:
DM reports grants, personal fees and non-financial support from GlaxoSmithKline, grants and personal fees from NeuroTrauma Sciences, personal fees from Pfizer Ltd, personal fees from PressuraNeuro, grants and personal fees from Lantmannen AB, grants and personal fees from Integra, outside the submitted work. All other authors declare no competing interests.

Publisher Copyright:
© 2022, The Author(s).

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