Endotyping Sleep Apnea One Breath at a Time An Automated Approach for Separating Obstructive from Central Sleep-disordered Breathing

Ankit Parekh, Thomas M. Tolbert, Anne M. Mooney, Jaime Ramos-Cejudo, Ricardo S. Osorio, Marcel Treml, Simon Dominik Herkenrath, Winfried J. Randerath, Indu Ayappa, David M. Rapoport

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Rationale: Determining whether an individual has obstructive or central sleep apnea is fundamental to selecting the appropriate treatment. Objectives: Here we derive an automated breath-by-breath probability of obstruction, as a surrogate of gold-standard upper airway resistance, using hallmarks of upper airway obstruction visible on clinical sleep studies. Methods: From five nocturnal polysomnography signals (airflow, thoracic and abdominal effort, oxygen saturation, and snore), nine features were extracted and weighted to derive the breath-by-breath probability of obstruction (Pobs). A development and initial test set of 29 subjects (development = 6, test = 23) (New York, NY) and a second test set of 39 subjects (Solingen, Germany), both with esophageal manometry, were used to develop Pobs and validate it against gold-standard upper airway resistance. A separate dataset of 114 subjects with 2 consecutive nocturnal polysomnographies (New York, NY) without esophageal manometry was used to assess the night-to-night variability of Pobs. Measurements and Main Results: A total of 1,962,229 breaths were analyzed. On a breath-by-breath level, Pobs was strongly correlated with normalized upper airway resistance in both test sets (set 1: cubic adjusted [adj.] R2 = 0.87, P< 0.001, area under the receiver operating characteristic curve = 0.74; set 2: cubic adj. R2 = 0.83, P, 0.001, area under the receiver operating characteristic curve = 0.7). On a subject level, median Pobs was associated with the median normalized upper airway resistance (set 1: linear adj. R2 = 0.59, P< 0.001; set 2: linear adj. R2 = 0.45, P< 0.001). Median Pobs exhibited low night-to-night variability [intraclass correlation(2, 1) = 0.93]. Conclusions: Using nearly 2 million breaths from 182 subjects, we show that breath-by-breath probability of obstruction can reliably predict the overall burden of obstructed breaths in individual subjects and can aid in determining the type of sleep apnea.

Original languageEnglish
Pages (from-to)1452-1462
Number of pages11
JournalAmerican Journal of Respiratory and Critical Care Medicine
Volume204
Issue number12
DOIs
StatePublished - 15 Dec 2021

Keywords

  • Airflow limitation
  • Esophageal pressure swings
  • Machine learning
  • Sleep apnea
  • Upper airway resistance

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