The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.

Journal: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
PMID: 39745438

Abstract

STUDY OBJECTIVES: Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea; however, some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective was to develop predictive models for CPAP outcomes and assess whether the inclusion of physiological traits enhances prediction.

Authors

  • Jui-En Lo
    School of Medicine, National Taiwan University College of Medicine, Taipei 106, Taiwan.
  • Christopher N Schmickl
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California San Diego, San Diego, California.
  • Florin Vaida
    Division of Biostatistics and Bioinformatics, University of California, San Diego, La Jolla, California.
  • Shamim Nemati
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
  • Karandeep Singh
    Department of Internal Medicine and School of Information, University of Michigan, Ann Arbor, Michigan.
  • Scott A Sands
    Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Robert L Owens
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California San Diego, San Diego, California.
  • Atul Malhotra
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California, San Diego, La Jolla, CA. Electronic address: amalhotra@health.ucsd.edu.
  • Jeremy E Orr
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California San Diego, San Diego, California.

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