Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning.

Journal: Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
PMID: 31739849

Abstract

STUDY OBJECTIVES: Home sleep apnea testing (HSAT) is an efficient and cost-effective method of diagnosing obstructive sleep apnea (OSA). However, nondiagnostic HSAT necessitates additional tests that erode these benefits, delaying diagnoses and increasing costs. Our objective was to optimize this diagnostic pathway by using predictive modeling to identify patients who should be referred directly to polysomnography (PSG) due to their high probability of nondiagnostic HSAT.

Authors

  • Robert Stretch
    David Geffen School of Medicine at University of California, Los Angeles, California.
  • Armand Ryden
    David Geffen School of Medicine at University of California, Los Angeles, California.
  • Constance H Fung
    David Geffen School of Medicine at University of California, Los Angeles, California.
  • Joanne Martires
    VA Greater Los Angeles Healthcare System, Los Angeles, California.
  • Stephen Liu
    VA Greater Los Angeles Healthcare System, Los Angeles, California.
  • Vidhya Balasubramanian
    Quantitative Sciences Unit (H.K.H., V.B.), in the Department of Medicine, Stanford University, CA.
  • Babak Saedi
    VA Greater Los Angeles Healthcare System, Los Angeles, California.
  • Dennis Hwang
    Southern California Permanente Medical Group, Los Angeles, California.
  • Jennifer L Martin
    David Geffen School of Medicine at University of California, Los Angeles, California.
  • Nicolás Della Penna
    Laboratory of Computational Physiology at Massachusetts Institute of Technology, Boston, Massachusetts.
  • Michelle R Zeidler
    David Geffen School of Medicine at University of California, Los Angeles, California.

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