Annals of the American Thoracic Society

Rationale: There are at least four key pathophysiological endotypes that contribute to obstructive sleep apnea (OSA) pathophysiology. These include 1) upper-airway collapsibility (Pcrit); 2) arousal threshold; 3) loop gain; and 4) pharyngeal muscle responsiveness. However, an easily interpretable model to examine the different ways and the extent to which these OSA endotypes contribute to conventional polysomnography-defined OSA severity (i.e., the apnea–hypopnea index) has not been investigated. In addition, clinically deployable approaches to estimate OSA endotypes to advance knowledge on OSA pathogenesis and targeted therapy at scale are not currently available.

Objectives: Develop an interpretable data-driven model to 1) determine the different ways and the extent to which the four key OSA endotypes contribute to polysomnography-defined OSA severity and 2) gain insight into how standard polysomnographic and clinical variables contribute to OSA endotypes and whether they can be used to predict OSA endotypes.

Methods: Age, body mass index, and eight polysomnography parameters from a standard diagnostic study were collected. OSA endotypes were also quantified in 52 participants (43 participants with OSA and nine control subjects) using gold-standard physiologic methodology on a separate night. Unsupervised multivariate principal component analyses and data-driven supervised machine learning (decision tree learner) were used to develop a predictive algorithm to address the study objectives.

Results: Maximum predictive performance accuracy of the trained model to identify standard polysomnography-defined OSA severity levels (no OSA, mild to moderate, or severe) using the using the four OSA endotypes was approximately twice that of chance. Similarly, performance accuracy to predict OSA endotype categories (“good,” “moderate,” or “bad”) from standard polysomnographic and clinical variables was approximately twice that of chance for Pcrit and slightly lower for arousal threshold.

Conclusions: This novel approach provides new insights into the different ways in which OSA endotypes can contribute to polysomnography-defined OSA severity. Although further validation work is required, these findings also highlight the potential for routine sleep study and clinical data to estimate at least two of the key OSA endotypes using data-driven predictive analysis methodology as part of a clinical decision support system to inform scalable research studies to advance OSA pathophysiology and targeted therapy for OSA.

Correspondence and requests for reprints should be addressed to Danny J. Eckert, Ph.D., Adelaide Institute for Sleep Health, Flinders University, 5 Laffer Drive, Bedford Park, SA 5042, Australia. E-mail: .

Supported by a Collaborative Research Centre Consortium Grant between the Australian Government, Academia, and Industry (industry partner: Oventus Medical). D.J.E. is supported by a National Health and Medical Research Council of Australia Senior Research Fellowship (1116942).

Author Contributions: R.D. and G.D. led the machine learning analyses. B.T. led the biostatistics analyses. A.S.J., A.W., and D.J.E. contributed to data collection and obstructive sleep apnea endotyping analyses. D.P.W. conceived the original obstructive sleep apnea endotyping project. D.J.E. conceived and designed the current study. All authors reviewed and contributed to the manuscript.

This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.

Author disclosures are available with the text of this article at www.atsjournals.org.

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Annals of the American Thoracic Society
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