TY - JOUR
T1 - Endotyping Sleep Apnea One Breath at a Time An Automated Approach for Separating Obstructive from Central Sleep-disordered Breathing
AU - Parekh, Ankit
AU - Tolbert, Thomas M.
AU - Mooney, Anne M.
AU - Ramos-Cejudo, Jaime
AU - Osorio, Ricardo S.
AU - Treml, Marcel
AU - Herkenrath, Simon Dominik
AU - Randerath, Winfried J.
AU - Ayappa, Indu
AU - Rapoport, David M.
N1 - Publisher Copyright:
Copyright © 2021 by the American Thoracic Society
PY - 2021/12/15
Y1 - 2021/12/15
N2 - 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.
AB - 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.
KW - Airflow limitation
KW - Esophageal pressure swings
KW - Machine learning
KW - Sleep apnea
KW - Upper airway resistance
UR - http://www.scopus.com/inward/record.url?scp=85122319162&partnerID=8YFLogxK
U2 - 10.1164/rccm.202011-4055OC
DO - 10.1164/rccm.202011-4055OC
M3 - Article
C2 - 34449303
AN - SCOPUS:85122319162
SN - 1073-449X
VL - 204
SP - 1452
EP - 1462
JO - American Journal of Respiratory and Critical Care Medicine
JF - American Journal of Respiratory and Critical Care Medicine
IS - 12
ER -