@article{ref1,
title="Data-driven approaches to reveal the pathobiological heterogeneity in patients with traumatic brain injury",
journal="Intensive care medicine",
year="2023",
author="Åkerlund, Cecilia and Ercole, Ari",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="In traumatic brain injury (TBI), the inability of the Glasgow Coma Scale (GCS) to capture the inherent heterogeneity of the disease may provide some explanation as to why randomised trials of biologically plausible therapies have largely failed. Such considerations provide strong motivation for the development of precision medicine approaches in this domain to improve outcome [1,2,3]. Subgroups of patients with distinct pathophysiological or pathobiological mechanisms--so-called endotypes--can be sought as a step to identifying individualised treatments. This can be done by data-driven approaches such as using unsupervised clustering algorithms.   Two of the most important contributions to identifying endotypes in the intensive care unit (ICU) population are by Calfee and Seymour. Calfee identified subgroups of patients with acute respiratory distress syndrome (ARDS) showing distinct inflammatory profiles by performing latent class analysis on patient data from two previous studies with negative outcome. These subgroups were found to respond differently to positive end-expiratory pressure (PEEP) [4]. Seymour did a similar analysis of sepsis patients using consensus k means clustering. He could identify distinct subgroups defined by the inflammatory response which benefited from different fluid management strategies suggesting a substrate for individualised care...<p /> <p>Language: en</p>",
language="en",
issn="0342-4642",
doi="10.1007/s00134-023-07156-y",
url="http://dx.doi.org/10.1007/s00134-023-07156-y"
}