Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

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Abstract

Rationale: Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night, which may have important implications for diagnosis and management. Objectives: This study aimed to assess OSA prevalence from multinight in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, nonrandomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. Methods: A total of 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of approximately 170 nights per participant between July 2020 and March 2021. OSA was defined as a nightly mean apnea–hypopnea index (AHI) of more than 15 events/h. Outcomes were multinight global prevalence and likelihood of OSA misclassification from a single night’s AHI value. Measurements and Main Results: More than 11.6 million nights of data were collected and analyzed. OSA global prevalence was 22.6% (95% confidence interval, 20.9–24.3%). The likelihood of misdiagnosis in people with OSA based on a single night ranged between approximately 20% and 50%. Misdiagnosis error rates decreased with increased monitoring nights (e.g., 1-night F1-score = 0.77 vs. 0.94 for 14 nights) and remained stable after 14 nights of monitoring. Conclusions: Multinight in-home monitoring using novel, noninvasive under-mattress sensor technology indicates a global prevalence of moderate to severe OSA of approximately 20%, and that approximately 20% of people diagnosed with a single-night study may be misclassified. These findings highlight the need to consider night-to-night variation in OSA diagnosis and management.

Original languageEnglish
Pages (from-to)563-569
Number of pages7
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume205
Issue number5
DOIs
Publication statusPublished - 1 Mar 2022

Keywords

  • Misdiagnosis
  • Wearables
  • Polysomnography
  • Sleep-disordered breathing

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