Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

Journal: International journal of cardiology
PMID: 40120825

Abstract

BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered by stress. Data on risk scores predicting mortality in TC is sparse. We developed a machine-learning risk score model to predict in-hospital mortality in patients with TC.

Authors

  • Ankit Agrawal
    Northwestern University, Evanston, IL 60201 USA.
  • Umesh Bhagat
    Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Aro Daniela Arockiam
    Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Elio Haroun
    Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Michael Faulx
    Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Milind Y Desai
    Heart and Vascular Institute Cleveland Clinic, Cleveland, OH, USA.
  • Wael Jaber
    Dept. of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, Ohio.
  • Venu Menon
    Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH.
  • Brian Griffin
    Aortic Center, Miller Family Heart and Vascular Institute, Cleveland, Ohio; Department of Cardiovascular Medicine, Miller Family Heart and Vascular Institute, Cleveland, Ohio.
  • Tom Kai Ming Wang
    Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA. Electronic address: wangt2@ccf.org.

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