Precision Sleep Medicine

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06903481

Enrollment

1055

Registered

2025-03-30

Start date

2022-01-01

Completion date

2024-07-31

Last updated

2025-03-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Obstructive Sleep Apnea

Keywords

Polysomnography (PSG) recordings

Brief summary

This retrospective observational study aims to characterize the prevalence and clinical features of obstructive sleep apnea (OSA) phenotypes and develop a rater-independent algorithm for automated OSA phenotyping, improving diagnosis and personalized treatment.

Detailed description

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

* age 19 years and older (data from University Hospital Basel (USB)) * age 18 years or younger (data from University Children's Hospital Basel (UKBB)) * Epworth Sleepiness Scale (ESS) score available (USB only) * Diagnosis available (e.g. Obstructive sleep apnea (OSA), Central Sleep Apnea (CSA), Cheyne Stokes Breathing (CSR), ...) * signed written general consent

Exclusion criteria

* history of treatment of sleep apnea * tracheostomy * current home oxygen therapy * decline to sign the written general consent, or absence of written general consent

Design outcomes

Primary

Measure	Time frame	Description
Phenotype characterization	2025	To characterize the prevalence and clinical features of distinct obstructive sleep apnea (OSA) phenotypes in a large, diverse patient population, polysomnography (PSG) recordings from two publicly available National Sleep Research Resource data collections are analyzed and compared to datasets from the University Hospital Basel (USB) and University Children's Hospital Basel (UKBB) to assess generalizability.

Secondary

Measure	Time frame	Description
Computer-aided phenotyping	2025	To evaluate the potential of computer-aided pattern recognition in improving the accuracy and efficiency of OSA phenotype identification from PSG data, an algorithm is developed that objectively and automatically identifies OSA phenotypes from PSG recordings.

Countries

Switzerland

Outcome results