Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2.

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.

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Dr Kennedy is supported by 10.13039/100000002NIH/10.13039/100000057NIGMS 2R35GM119519-06 and 10.13039/100000002NIH/10.13039/100000057NIGMS 1R21GM144851-01. Dr Kapur has received Institutional Research Grants from 10.13039/100000046Abbott, Abiomed, 10.13039/100008497Boston Scientific, Getinge, and 10.13039/100013410LivaNova. Dr Baran is currently consulting with LivaNova and Getinge; prior consulting with Abiomed and Abbott; is in the current steering committee of Procyrion and CareDx; and is a speaker (current) for Pfizer. Dr Kapur has received speaker honoraria/consultant fee from Abbott, Abiomed, Boston Scientific, Edwards, Getinge, LivaNova, and Zoll. Dr Mebazaa has received personal fees from Orion, Roche, Adrenomed, and Fire 1; and grants and personal fees from 4TEEN4, Abbott, Roche, and SphingoTec. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.