Precision Sleep Medicine: Hardware and Software Innovations for the Combined Diagnosis and Treatment of Sleep Apnea at the Point-of-Care

Obstructive sleep apnea (OSA) is characterized by repeated upper airway blockages during sleep, but it presents with a range of phenotypic variations, each with potentially distinct clinical implications. Current clinical definitions are not always precise, making it difficult to clearly classify patients with overlapping features. This phenotypic overlap poses challenges for understanding the true prevalence of "pure" versus "mixed" OSA phenotypes and their respective clinical implications.

To comprehensively characterize the prevalence and clinical features of distinct OSA phenotypes in a large, diverse patient population, this retrospective study analyzes polysomnography (PSG) data from two publicly available National Sleep Research Resource datasets. The findings are then compared to datasets from the University Hospital Basel and University Children's Hospital Basel to assess generalizability.

Furthermore, the study employs computer-aided analysis of PSG data to develop rater-independent algorithms for objective and automated OSA phenotyping. These advancements aim to improve understanding of OSA heterogeneity, facilitating more precise diagnoses and personalized treatment strategies.