• Mayo Clinic proceedings · May 2023

    Review

    Cardiogenic Shock Classification and Associated Mortality Risk.

    • Katherine L Hill, Mark A Rustin, Michele A Asche, Courtney E Bennett, Parag C Patel, and Jacob C Jentzer.
    • Department of Nursing, Mayo Clinic, Rochester, MN; Doctor of Nursing Program, Winona State University, Winona, MN.
    • Mayo Clin. Proc. 2023 May 1; 98 (5): 771783771-783.

    AbstractThe Society for Cardiovascular Angiography and Interventions (SCAI) Shock Classification was developed to create standardized language describing the severity of cardiogenic shock (CS). The purposes of this review were to evaluate short-term and long-term mortality rates at each SCAI shock stage for patients with or at risk for CS, which has not been studied previously, and to propose using the SCAI Shock Classification to develop algorithms for clinical status monitoring. A detailed literature search was conducted for articles published from 2019 through 2022 in which the SCAI shock stages were used to assess the mortality risk. In total, 30 articles were reviewed. The SCAI Shock Classification at hospital admission revealed a consistent and reproducible graded association between shock severity and mortality risk. Furthermore, shock severity correlated incrementally with mortality risk even after patients were stratified for diagnosis, treatment modalities, risk modifiers, shock phenotype, and underlying cause. The SCAI Shock Classification system can be used to evaluate mortality across populations of patients with or at risk for CS including those with different causes, shock phenotypes, and comorbid conditions. We propose an algorithm that uses clinical parameters incorporating the SCAI Shock Classification into the electronic health record to continually reassess and reclassify the presence and severity of CS across time throughout hospitalization. The algorithm has the potential to alert the care team and a CS team, leading to earlier recognition and stabilization of the patient, and may facilitate the use of treatment algorithms and prevent CS deterioration, leading to improved outcomes.Copyright © 2022 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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