Clinical Course of Patients in Cardiogenic Shock Stratified by Phenotype

Elric Zweck, Manreet Kanwar, Song Li, Shashank S. Sinha, A. Reshad Garan, Jaime Hernandez-Montfort, Yijing Zhang, Borui Li, Paulina Baca, Fatou Dieng, Neil M. Harwani, Jacob Abraham, Gavin Hickey, Sandeep Nathan, Detlef Wencker, Shelley Hall, Andrew Schwartzman, Wissam Khalife, Claudius Mahr, Ju H. KimEsther Vorovich, Evan H. Whitehead, Vanessa Blumer, Ralf Westenfeld, Daniel Burkhoff, Navin K. Kapur

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Background: Cardiogenic shock (CS) patients remain at 30% to 60% in-hospital mortality despite therapeutic innovations. Heterogeneity of CS has complicated clinical trial design. Recently, 3 distinct CS phenotypes were identified in the CSWG (Cardiogenic Shock Working Group) registry version 1 (V1) and external cohorts: I, “noncongested;” II, “cardiorenal;” and III, “cardiometabolic” shock. Objectives: The aim was to confirm the external reproducibility of machine learning–based CS phenotypes and to define their clinical course. Methods: The authors included 1,890 all-cause CS patients from the CSWG registry version 2. CS phenotypes were identified using the nearest centroids of the initially reported clusters. Results: Phenotypes were retrospectively identified in 796 patients in version 2. In-hospital mortality rates in phenotypes I, II, III were 23%, 41%, 52%, respectively, comparable to the initially reported 21%, 45%, and 55% in V1. Phenotype-related demographic, hemodynamic, and metabolic features resembled those in V1. In addition, 58.8%, 45.7%, and 51.9% of patients in phenotypes I, II, and III received mechanical circulatory support, respectively (P = 0.013). Receiving mechanical circulatory support was associated with increased mortality in cardiorenal (OR: 1.82 [95% CI: 1.16-2.84]; P = 0.008) but not in noncongested or cardiometabolic CS (OR: 1.26 [95% CI: 0.64-2.47]; P = 0.51 and OR: 1.39 [95% CI: 0.86-2.25]; P = 0.18, respectively). Admission phenotypes II and III and admission Society for Cardiovascular Angiography and Interventions stage E were independently associated with increased mortality in multivariable logistic regression compared to noncongested “stage C” CS (P < 0.001). Conclusions: The findings support the universal applicability of these phenotypes using supervised machine learning. CS phenotypes may inform the design of future clinical trials and enable management algorithms tailored to a specific CS phenotype.

Original languageEnglish (US)
Pages (from-to)1304-1315
Number of pages12
JournalJACC: Heart Failure
Volume11
Issue number10
DOIs
StatePublished - Oct 2023

Keywords

  • SCAI stages
  • acute heart failure
  • cardiogenic shock
  • machine learning
  • mechanical circulatory support
  • outcomes
  • phenotypes

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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