Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study.

Journal: PLoS medicine
Published Date: Nov 1, 2018

Abstract

BACKGROUND: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.

Authors

  • Chenxi Huang
    Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America.
  • Karthik Murugiah
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Shiwani Mahajan
    Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America.
  • Shu-Xia Li
    From the Section of Cardiovascular Medicine, Department of Internal Medicine (B.J.M., N.S.D., E.M.B., K.D., H.M.K.), Department of Psychiatry and the Section of General Medicine, Department of Internal Medicine (A.M.), and Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, and Department of Health Policy and Management (H.M.K.), Yale School of Public Health, New Haven, CT; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT (B.J.M., N.S.D., E.M.B., K.D., S.-X.L., H.M.K.); and Department of Statistics, Yale University, New Haven, CT (B.J.M., S.N.N.).
  • Sanket S Dhruva
    Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Julian S Haimovich
    Albert Einstein College of Medicine, Bronx, New York, United States of America.
  • Yongfei Wang
    Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut, United States of America.
  • Wade L Schulz
    Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT.
  • Jeffrey M Testani
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Francis P Wilson
    Section of Nephrology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Carlos I Mena
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Frederick A Masoudi
    Division of Cardiology, School of Medicine, University of Colorado, Aurora, Colorado, United States of America.
  • John S Rumsfeld
    From the Department of Veterans Affairs' Center for Health Equity Research and Promotion, Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA (P.W.G.); Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, Leonard Davis Institute of Health Economics, and Center for Cardiovascular Outcomes, Quality, and Evaluative Research, University of Pennsylvania, Philadelphia (P.W.G.); University of Colorado School of Medicine, Aurora, CO (J.S.R.); Veterans Affairs Eastern Colorado Health System, Denver (J.S.R.); and American College of Cardiology, Washington, DC (J.S.R.).
  • John A Spertus
    Department of Cardiology, Saint Luke's Mid America Heart Institute, Kansas City, Missouri, United States of America.
  • Bobak J Mortazavi
    Texas A&M University, USA.
  • Harlan M Krumholz
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

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