Application of machine learning to predict in-hospital mortality after transcatheter mitral valve repair.

Journal: Surgery
PMID: 39122592

Abstract

INTRODUCTION: Transcatheter mitral valve repair offers a minimally invasive treatment option for patients at high risk for traditional open repair. We sought to develop dynamic machine-learning risk prediction models for in-hospital mortality after transcatheter mitral valve repair using a national cohort.

Authors

  • Emma O Cruz
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA; Computer Science Department, Stanford University, Palo Alto, CA.
  • Sara Sakowitz
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Saad Mallick
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Nguyen Le
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Nikhil Chervu
    Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA.
  • Syed Shahyan Bakhtiyar
    Cardiovascular Outcomes Research Laboratories, David Geffen School of Medicine at University of California-Las Angeles, CA; Division of Cardiac Surgery, Department of Surgery, David Geffen School of Medicine at University of California-Las Angeles, CA; Department of Surgery, University of Colorado Anschutz Medical Center, Aurora, CO.
  • Peyman Benharash

Keywords

External Resources

Popular Topics
Recent Journals