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

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...

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Detalles Bibliográficos
Autores principales: Parekh, Ankit, Tolbert, Thomas M., Mooney, Anne M., Ramos-Cejudo, Jaime, Osorio, Ricardo S., Treml, Marcel, Herkenrath, Simon-Dominik, Randerath, Winfried J., Ayappa, Indu, Rapoport, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Thoracic Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8865720/
https://www.ncbi.nlm.nih.gov/pubmed/34449303
http://dx.doi.org/10.1164/rccm.202011-4055OC
Descripción
Sumario: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 (P(obs)). 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 P(obs) 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 P(obs). MEASUREMENTS AND MAIN RESULTS: A total of 1,962,229 breaths were analyzed. On a breath-by-breath level, P(obs) was strongly correlated with normalized upper airway resistance in both test sets (set 1: cubic adjusted [adj.] R(2) = 0.87, P < 0.001, area under the receiver operating characteristic curve = 0.74; set 2: cubic adj. R(2) = 0.83, P < 0.001, area under the receiver operating characteristic curve = 0.7). On a subject level, median P(obs) was associated with the median normalized upper airway resistance (set 1: linear adj. R(2) = 0.59, P < 0.001; set 2: linear adj. R(2) = 0.45, P < 0.001). Median P(obs) 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.