The use of the sleep apnea-specific hypoxic burden to predict obstructive sleep apnea hypopnea syndrome: Evidence from a large cross-sectional study.

Objectives: Obstructive sleep apnea hypopnea syndrome (OSA) is an independent risk factor for neurocognitive and behavioral problems and cardiovascular and metabolic morbidities, ultimately increasing mortality. However, OSA diagnosis is time-consuming, labor-intensive, and expensive. We evaluated the predictive utility of the sleep apnea-specific hypoxic burden (SASHB) in terms of OSA and the severity thereof in Han Chinese individuals.
Methods: From January 2019 to July 2022, subjects with suspected OSA were recruited in the sleep center of the Shanghai Sixth People's Hospital during sleep evaluation via standard polysomnography. Basic anthropometric measurements and polysomnographic indicators were collected; SASHB was calculated based on the SpO ₂ trends of apnea or hypopnea events. Models predictive of OSA were established via logistic regression in the experimental group and verified in an independent group by drawing receiver operating characteristic (ROC) curves.
Results: A total of 2303 subjects with suspected OSA (1200 in the experimental group and 1103 in the validation group) were included. SASHB was positively correlated with the apnea-hyponea index (AHI) in all subjects (r = 0.823, P < 0.001). SASHB distinguished OSA from non-OSA subjects in both the experimental group {area under the curve (AUC) 0.948 [0.934∼0.962]} and the validation group (AUC 0.931 [0.913∼0.949]). SASHB predicted OSA severity well, better than did the neck, waist, or hip circumference; the lowest or mean oxygen saturation; and the Epworth sleepiness scale score.
Conclusion: SASHB predicted OSA both accurately and efficiently in a Chinese Han population. Further studies are warranted to verify our findings in community samples.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)