OSA-Onset: An algorithm for predicting the age of OSA onset

Michelle Olaithe; Erica W. Hagen; Jodi H. Barnet; Peter R. Eastwood; Romola S. Bucks

doi:10.1016/j.sleep.2023.05.018

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OSA-Onset: An algorithm for predicting the age of OSA onset

Journal article

Open access

Peer reviewed

OSA-Onset: An algorithm for predicting the age of OSA onset

Michelle Olaithe, Erica W. Hagen, Jodi H. Barnet, Peter R. Eastwood and Romola S. Bucks

Sleep medicine, Vol.108, pp.100-104

2023

DOI: https://doi.org/10.1016/j.sleep.2023.05.018

PMID: 37348284

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Abstract

Algorithm

Apnea

Disease onset

Disorder

Sleep

There is currently no way to estimate the period of time a person has had obstructive sleep apnoea (OSA). Such information would allow identification of people who have had an extended exposure period and are therefore at greater risk of other medical disorders; and enable consideration of disease chronicity in the study of OSA pathogenesis/treatment. The ‘age of OSA Onset’ algorithm was developed in the Wisconsin Sleep Cohort (WSC), in participants who had ≥2 sleep studies and not using continuous positive airway pressure (n = 696). The algorithm was tested in a participant subset from the WSC (n = 154) and the Sleep Heart Health Study (SHHS; n = 705), those with an initial sleep study showing no significant OSA (apnea-hypopnea index (AHI) < 15 events/hr) and later sleep study showing moderate to severe OSA (AHI≥15 events/hr). Regression analyses were performed to identify variables that predicted change in AHI over time (BMI, sex, and AHI; beta weights and intercept used in the algorithm). In the WSC and SHHS subsamples, the observed years with OSA was 3.6 ± 2.6 and 2.7 ± 0.6 years, the algorithm estimated years with OSA was 10.6 ± 8.2 and 9.0 ± 6.2 years. The OSA-Onset algorithm estimated years of exposure to OSA with an accuracy of between 6.6 and 7.8 years (mean absolute error). Future studies are needed to determine whether the years of exposure derived from the OSA-Onset algorithm is related to worse prognosis, poorer cognitive outcomes, and/or poorer response to treatment. •Greater exposure time to a risk-factor is often associated with poorer health, for example, the association between smoking “pack-years” and the risk of lung cancer.•No algorithm for exposure time exists for obstructive sleep apnea (OSA). Our team developed the first algorithm to estimate years with OSA.•Knowing this information will allow future studies to:•Identify people who have experienced OSA for an extended period;•Enable the examination of the effect of long-term OSA where longitudinal samples are unavailable, or inappropriate, and;•Potentially explain why some people have a worse disease prognosis and poorer response to OSA therapy.

Details

Title: OSA-Onset: An algorithm for predicting the age of OSA onset
Authors/Creators: Michelle Olaithe - The University of Western Australia
Erica W. Hagen - University of Wisconsin School of Medicine and Public Health
Jodi H. Barnet - University of Wisconsin–Madison
Peter R. Eastwood - Flinders University
Romola S. Bucks - The University of Western Australia
Publication Details: Sleep medicine, Vol.108, pp.100-104
Publisher: Elsevier B.V
Identifiers: 991005626968707891
Murdoch Affiliation: Vice Chancellery
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.137 Sleep Science & Circadian Systems; 1.137.382 Obstructive Sleep Apnea
Web Of Science research areas: Clinical Neurology
ESI research areas: Neuroscience & Behavior