Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

Journal: ESC heart failure
Published Date: May 22, 2024

Abstract

AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emergent) admission ('A'), Charlson comorbidity index ('C') and visits to the emergency department during the previous 6 months ('E')] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.

Authors

  • Xuewu Song
    Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Yitong Tong
    Chengdu Second People's Hospital, Chengdu, China.
  • Feng Xian
    Department of Oncology, Nanchong Central Hospital, the second Clinical Medical College, North Sichuan Medical College, Nanchong, China.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Rongsheng Tong
    Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. 318004031@qq.com.

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