Patients with Sleep Apnea Are More than Just a Number

Study finds statistical variations associated with apnea-hypopnea index affect patient treatment eligibility

Boston, MA — A team of researchers from Brigham and Women’s Hospital, Beth Israel Deaconess Medical Center and Boston University have quantified a critical problem in the way patients with sleep apnea are diagnosed and treated. The study, published in Sleep Medicine, suggests that many patients who seek a diagnosis and treatment for sleep apnea are likely being denied treatment eligibility due to statistical variations attributable to chance. This study is the first to quantify the impact of statistical uncertainty on sleep apnea treatment eligibility in a large population and highlights a major shortcoming of the current eligibility standards.

The team found that there is large statistical uncertainty in the apnea-hypopnea index (AHI), the key diagnostic measure used to determine if a patient is eligible for treatment for sleep apnea. “It is like telling a runner that they have lost a race by a fraction of a second if you are using a stopwatch that is only accurate to the minute,” said senior author Michael Prerau, PhD, an associate neuroscientist at the Brigham and an assistant professor of medicine at Harvard Medical School (HMS). Prerau previously conducted work on this study while at Massachusetts General Hospital. “In a large number of cases, there may just not be enough information to make an accurate diagnostic determination.”

Sleep apnea is a condition in which a sleeping person briefly stops breathing or has trouble breathing many times during the night. Sleep apnea is thought to impact at least 10 percent of the adult population, with increasing risk of up to 50 percent in those with certain conditions, such as obesity, type 2 diabetes and cardiac problems. Long-term effects of untreated sleep apnea include increased risk of high blood pressure, heart failure, stroke and depression.

Sleep apnea is typically diagnosed with a sleep study, in which a patient’s sleep and breathing are monitored in a lab or at home. From this study, the average number of nightly apnea-related breathing events per hour — the apnea-hypopnea index (AHI) — is calculated. The value of the AHI is then compared to a value determined by Medicare or an insurer, which serves as a clinical threshold. If the AHI is above that number, the patient is diagnosed as having sleep apnea and is eligible for treatment, such as using a continuous positive airway pressure (CPAP) device, which is reimbursable under insurance or Medicare. If the AHI falls below that number, even by the smallest amount, the patient is denied reimbursable treatment.

Currently, the AHI is treated as a single number that definitively describes the patient’s condition. However, differences in sleep quality across nights, changes in sleep and breathing patterns within a single night, and variability from the limited amount of data collected in a single sleep study can potentially cause a great deal of variability associated with this measurement. Slight alteration in AHI value could mean that an AHI will be on one side of the threshold in one sleep study and the other side of the threshold on a subsequent sleep study, which can be the difference between eligibility and denial of treatment. To address these issues, the researchers devised a method of estimating the statistical uncertainty associated with AHI, which they then applied to 2,049 subjects from a diverse, population-based study of subjects between the ages of 45 and 84. Using this method, they computed the uncertainty around each subject’s AHI measurement, and determined how this might affect eligibility.

The results they found were striking. The degree of uncertainty surrounding a given AHI value was very large with respect to the clinical thresholds, such that 43 percent of the subjects had diagnoses that could potentially be incorrect due to chance. Even when restricting analysis to only those subjects that reported excessive daytime sleepiness, 27 percent of these subjects had uncertain diagnoses. In both groups, most of the uncertainty cases (71 percent and 63 percent, respectively) would have been denied eligibility.

The authors suggest that in cases in which a diagnosis is uncertain, clinicians should be able err on the side of inclusion or be able to use other information available to them to determine eligibility.

“For patients with an indeterminate diagnosis, there’s no harm beginning treatment for sleep apnea even if further data suggest the patient was erroneously diagnosed. Once apnea is ruled out, however, it’s impossible to start treatment for someone who has been deemed ineligible,” said first author Robert Thomas, MD, a physician in the Division of Pulmonary, Critical Care & Sleep Medicine at Beth Israel and an associate professor of Medicine at HMS. “For these patients, any short-term treatment costs will be greatly offset by the long-term health benefits for patients who would otherwise be ineligible.”

Prerau has created free online tools on his laboratory’s website (sleepEEG.org/AHI) to allow clinicians to estimate the uncertainty surrounding a given patient’s AHI — however, this is just the beginning. Future work will focus on developing ways of modeling sleep apnea as a process that evolves throughout the night, rather than an average.

“The way a patient’s apnea rate changes over time may be an important factor related to clinical outcome,” said co-author Uri Eden, PhD, a professor of Mathematics at Boston University. “Understanding the particular time-course of each patient’s symptoms may hold the key to building more effective, personalized treatment plans.”

Overall, the team hopes this work can provide a basis for improving eligibility standards and patient outcomes. “It is vital to understand the degree of confidence inherent in any clinical metric. By quantifying AHI uncertainty, clinicians can place diagnoses within the proper context and can help inform more statistically and clinically principled eligibility standards,” said Prerau.

This work was supported by the National Institutes of Health, National Institute of Neurological Disorders and Stroke R01 NS-096177 and Simons Foundation 542971.

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