The use of clustering algorithms in critical care research to unravel patient heterogeneity

Excerpt

Critically ill patients constitute the most heterogeneous population in the hospital, with the highest rates of acute and chronic multimorbidity. Daily, two critically ill patients are admitted to the ICU with the same syndrome-based diagnosis, receive similar treatment, and yet have diametrically opposite outcomes. Knowledge regarding disease mechanisms and effectiveness of interventions that could explain this is lacking, but it is increasingly clear that syndrome-based patient categorisation inevitably leads to grouping of patients with different risk profiles, responses to interventions, and outcomes. In septic shock, for instance, cardiovascular sub-phenotypes are insufficiently characterised, which compromises the effectiveness of hemodynamic support [1]. …