Clinical Course of Patients in Cardiogenic Shock Stratified by Phenotype.
Publication Title
JACC Heart Fail
Document Type
Article
Publication Date
6-6-2023
Keywords
oregon; cards; cards publication; SCAI stages; acute heart failure; cardiogenic shock; machine learning; mechanical circulatory support; outcomes; phenotypes
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 (odds ratio [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.
Area of Special Interest
Cardiovascular (Heart)
Specialty/Research Institute
Cardiology
DOI
10.1016/j.jchf.2023.05.007
