A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease.

Journal: Clinical rheumatology
PMID: 36627530

Abstract

INTRODUCTION: In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions.

Authors

  • Yuto Sunaga
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Atsushi Watanabe
  • Nobuyuki Katsumata
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Takako Toda
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan. takakotojp@yahoo.co.jp.
  • Masashi Yoshizawa
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Yosuke Kono
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Yohei Hasebe
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Keiichi Koizumi
    Division of Kampo Diagnostics, Institute of Natural Medicine, University of Toyama, Toyama, 930-0194, Japan. kkoizumi@inm.u-toyama.ac.jp.
  • Minako Hoshiai
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Eiryo Kawakami
    Medical Sciences Innovation Hub Program, RIKEN, Yokohama, Kanagawa, Japan.
  • Takeshi Inukai
    Faculty of Medicine, Department of Pediatrics, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.

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