Evaluation of Biomarkers in Critical Care and Perioperative Medicine

A Clinician's Overview of Traditional Statistical Methods and Machine Learning Algorithms

Sabri Soussi, M.D., M.Sc.; Gary S. Collins, Ph.D.; Peter Jüni, M.D.; Alexandre Mebazaa, M.D., Ph.D.; Etienne Gayat, M.D., Ph.D.; Yannick Le Manach, M.D., Ph.D.


Anesthesiology. 2020;134(1):15-25. 

In This Article

Phenotyping and Clustering Using Biomarkers

Unsupervised machine learning algorithms are used to identify naturally occurring clusters or subphenotypes of patients who have similar clinical or biologic/molecular features without targeting a specific outcome (Supplemental Digital Content 2, http://links.lww.com/ALN/C504).[55,56] Several popular unsupervised learning algorithms (e.g., latent class analysis, cluster analysis) are described in Table 2.

An example of using this method in critical care is in personalized medicine research. Patients sharing the same clinical/biologic characteristics are more likely to respond to targeted treatments (e.g., ventilation strategy, fluid administration strategy, statins).[13,14,57] For example, Calfee et al. identified two different subphenotypes in acute respiratory distress syndrome (ARDS) patients using latent class analysis (mainly based on clinical data and inflammatory biomarkers) with a different response to a positive end-expiratory pressure (PEEP) strategy.[13] The same group identified two different subphenotypes of ARDS in the Hydroxymethylglutaryl-CoA Reductase Inhibition with Simvastatin in Acute Lung Injury to Reduce Pulmonary Dysfunction cohort, with distinct clinical and biologic features (cytokines) and different clinical outcomes. The hyperinflammatory subphenotype had improved survival with simvastatin compared with placebo.[57] Finally, Seymour et al. retrospectively identified four different phenotypes (mainly based on markers of inflammation, coagulation, and renal injury) in sepsis with different responses to early goal-directed therapy.[14]