Cost-Utility of Three Approaches to the Diagnosis of Sleep Apnea: Polysomnography, Home Testing, and Empirical Therapy

  1. Ronald D. Chervin, MD, MS;
  2. Daniel L. Murman, MD, MS;
  3. Beth A. Malow, MD, MS; and
  4. Vicken Totten, MD, MS
  1. From the University of Michigan, Ann Arbor, Michigan; Michigan State University, East Lansing, Michigan; and Catholic Medical Center of Brooklyn and Queens, Brooklyn, New York.

    Abstract

    Background: Obstructive sleep apnea syndrome (OSAS) is usually diagnosed with overnight polysomnography in a sleep laboratory. Home sleep studies can be performed at lower cost, but results are somewhat less reliable. Bedside diagnosis of OSAS without any testing has also been discussed.

    Objective: To model the costs and utility of laboratory polysomnography, home study, and no testing during the 5 years after initial evaluation for OSAS.

    Design: Cost-utility analysis.

    Data Sources: Published data.

    Target Population: Hypothetical cohort of persons suspected of having OSAS.

    Time Horizon: The 5 years after initial evaluation for OSAS.

    Perspective: Societal.

    Intervention: Nasal continuous positive airway pressure when OSAS was diagnosed.

    Measurements: Quality of life, survival and charges (as proxies for costs) for each diagnostic method.

    Results of Base-Case Analysis: Under almost all modeled conditions, polysomnography provided maximal quality-adjusted life-years in the 5 years after the initial diagnostic evaluation. The incremental charges for polysomnography over home study or no testing were about $13 400 and $9200, respectively, per quality-adjusted life-year gained during this period.

    Results of Sensitivity Analysis: Results were sensitive to the utility of treatment in the absence of OSAS.

    Conclusions: The cost–utility of polysomnography instead of home study or no testing in the diagnosis of OSAS compares favorably with that of other procedures for which society judges the added utility per dollar spent to be worthwhile. More precise determination of certain key variables in this model should be a goal of future research.

    Article and Author Information

    • Acknowledgments: This manuscript was developed, in part, during graduate work done by the authors at the University of Michigan School of Public Health. The authors thank Dr. Richard Cornell and Dr. Michael Chernew, who provided excellent introductions to decision analysis and cost–utility analysis, respectively, and Dr. Rajesh R. Bandekar of the Consortium for Health Outcomes, Innovation, and Cost-Effectiveness Studies (CHOICES) for assistance with Monte Carlo simulation.

    • Requests for Reprints: Ronald D. Chervin, MD, MS, Sleep Disorders Center, University Hospital 8D8702, Box 0117, 1500 East Medical Center Drive, Ann Arbor, MI 48109-0117.

    • Current Author Addresses: Drs. Chervin and Malow: Sleep Disorders Center, University Hospital 8D8702, Box 0117, 1500 East Medical Center Drive, Ann Arbor, MI 48109-0117.

    • Dr. Murman: Clinical Neuroscience, A-217 Clinical Center, 138 Service Road, East Lansing, MI 48824.

    • Dr. Totten: Department of Emergency Medicine, Catholic Medical Center, 58-25 153rd Street, Brooklyn, NY 11432.

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