Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

Journal: Advances in therapy
PMID: 33834355

Abstract

INTRODUCTION: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions.

Authors

  • Amod Amritphale
    Division of Cardiology, University Hospital, University of South Alabama, 2451 University Hospital Dr, Suite 10D, Mobile, AL, 36617, USA. aamritphale@health.southalabama.edu.
  • Ranojoy Chatterjee
    Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
  • Suvo Chatterjee
    Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Nupur Amritphale
    University of South Alabama, Children & Women's Hospital, Mobile, AL, 36617, USA.
  • Ali Rahnavard
    Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.
  • G Mustafa Awan
    Division of Cardiology, University Hospital, University of South Alabama, 2451 University Hospital Dr, Suite 10D, Mobile, AL, 36617, USA.
  • Bassam Omar
    Division of Cardiology, University Hospital, University of South Alabama, 2451 University Hospital Dr, Suite 10D, Mobile, AL, 36617, USA.
  • Gregg C Fonarow
    Ahmanson-UCLA Cardiomyopathy Center, Division of Cardiology, Ronald Reagan-UCLA Medical Center, Los Angeles, California.

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