Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height.

Journal: BMC anesthesiology
PMID: 33882838

Abstract

BACKGROUND: Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation.

Authors

  • Jong Ho Kim
    Department of Ophthalmology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
  • Haewon Kim
    Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, 77 Sakju-ro, Chuncheon, 24253, South Korea.
  • Ji Su Jang
    Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, 77 Sakju-ro, Chuncheon, 24253, South Korea.
  • Sung Mi Hwang
    Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, 77 Sakju-ro, Chuncheon, 24253, South Korea.
  • So Young Lim
    Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, 77 Sakju-ro, Chuncheon, 24253, South Korea.
  • Jae Jun Lee
    Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Korea.
  • Young Suk Kwon
    Section of Urologic Oncology, Rutgers Cancer Institute of New Jersey and Division of Urology, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.

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