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

Area of Science:

• Sleep Medicine
• Respiratory Medicine
• Biostatistics

Background:

• Obstructive sleep apnoea (OSA) diagnosis lacks a method to estimate disease duration.
• Estimating OSA duration is crucial for identifying individuals at higher risk of comorbidities.
• Understanding disease chronicity is vital for OSA pathogenesis and treatment studies.

Purpose of the Study:

• To develop and validate an algorithm for estimating the period of time a person has had obstructive sleep apnoea (OSA).
• To enable the identification of individuals with extended OSA exposure and associated health risks.
• To facilitate the consideration of disease chronicity in OSA research.

Main Methods:

• Developed the 'age of OSA Onset' algorithm using data from the Wisconsin Sleep Cohort (WSC) (n=696).
• Validated the algorithm in subsets of the WSC (n=154) and the Sleep Heart Health Study (SHHS) (n=705).
• Utilized regression analyses to identify predictors of Apnea-Hypopnea Index (AHI) change over time.

Main Results:

• The algorithm estimated years with OSA as 10.6 ± 8.2 years in the WSC and 9.0 ± 6.2 years in the SHHS.
• Observed years with OSA were significantly shorter: 3.6 ± 2.6 years (WSC) and 2.7 ± 0.6 years (SHHS).
• The OSA-Onset algorithm demonstrated a mean absolute error of 6.6 to 7.8 years in estimating OSA exposure.

Conclusions:

• The OSA-Onset algorithm provides an estimation of OSA exposure duration.
• Further research is needed to correlate estimated OSA duration with prognosis, cognitive function, and treatment response.
• This algorithm represents a novel tool for assessing OSA disease chronicity.