Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.