Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder which affects an estimated 22-million Americans. In patients with OSA, periods of airway closure occur throughout the night resulting in a hypoxic sleep state which contributes increased risk of many conditions including heart disease, depression, stroke, and dementia. Inflammatory events, which occur following acute exposures to irritants lead to an increase in apnea. Exposure to poor air quality or air pollutants has been linked to airway inflammation suggesting that atmospheric pollutants are likely to negatively impact OSA. First-line treatment for OSA is use of a CPAP device which keeps the external air supply at higher pressure to help maintain an open airway. These devices log of apnea events and report daily AHI, a measure of apnea events per hour, for every night of active use. In this epidemiological study, CPAP device data from more than 4000 patients was compared with EPA atmospheric pollutant concentration data from air quality monitoring sites to investigate the impact of air quality on OSA. Machine-learning techniques were utilized to classify “good days,” where no notable apnea changes occurred, and “bad days,” days where more than 10% of patients had an AHI greater than two standard deviations above their mean with 96.7% accuracy. Examining the weighting factors utilized by the algorithm allowed identification of pollutants which drove selection of bad days. Those include PM 2.5, carbon monoxide, and ozone. Through further analysis, it was discovered that pollutant-sensitive sub-populations drive categorization. Overall, some level of pollutant sensitivity impacts ~50% of CPAP patients. Those with sensitivity average a 26% higher AHI and have 46% more clear-airway apneas. These results demonstrate the impact of air pollution on OSA and provide patients and physicians information to make care adjustments for the future.

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