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 optim

SUBMITTER: Akerlund CAI 

PROVIDER: S-EPMC9327174 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature