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Introduction Electroencephalogram (EEG) provides clinically relevant information for personalized patient health evaluation and comprehensive assessment of sleep. EEG-based indices have been associated with neurodegenerative conditions, psychiatric disorders, and metabolic and cardiovascular disease, and hold promise as a biomarker for brain health. Methods A deep neural network (DNN) model was trained to predict the age of patients using raw EEG signals recorded during clinical polysomnography (PSG). The DNN was trained on N=126,241 PSGs, validated on N=6,638, and tested on a holdout set of N=1,172. The holdout dataset included several categories of patient demographic and diagnostic parameters, allowing us to examine the association between brain age and a variety of medical conditions. Brain age was assessed by subtracting the individual’s chronological brain age from their EEG-predicted brain age (Brain Age Index; BAI), and then taking the absolute value of this variable (Absolute Brain Age Index; ABAI). We then constructed two regression models to test the relationship between BAI/ABAI and the following list of patient parameters: sex, BMI, depression, alcohol/drug problems, memory/concentration problems, epilepsy/seizures, diabetes, stroke, severe excessive daytime sleepiness (e.g., Epworth Sleepiness Scale ≥ 16; EDS), apnea-hypopnea index (AHI), arousal index (ArI), and sleep efficiency (SE). Results The DNN brain age model produced a mean absolute error of 4.604 and a Pearson’s r value of 0.933 which surpass the performance of prior research. In our regression analyses, we found a statistically significant relationship between the ABAI and: epilepsy and seizure disorders, stroke, elevated AHI, elevated ArI, and low SE (all p<0.05). This demonstrates these health conditions are associated with deviations of one’s predicted brain age from their chronological brain age. We also found patients with diabetes, depression, severe EDS, hypertension, and/or memory and concentration problems showed, on average, an elevated BAI compared to the healthy population sample (all p<0.05). Conclusion We show DNNs can accurately predict the brain age of healthy patients based on their raw, PSG derived, EEG recordings. Furthermore, we reveal indices, such as BAI and ABAI, display unique characteristics within different diseased populations, highlighting their potential value as novel diagnostic biomarker and potential “vital sign” of brain health. Support (if any):

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Introduction We explore the impact of obstructive sleep apnea (OSA) and positive airway pressure (PAP) therapy on novel coronavirus (COVID-19) infection rate and severity. Methods Retrospective analysis was performed utilizing a database of patients evaluated by Kaiser Permanente Southern California sleep medicine between 2015–2020 (includes sleep study, daily PAP, and electronic health record data.) Adult patients were analyzed if: on March 1, 2020 patient was alive, had ≥1 month health-plan enrollment, and had sleep diagnostic or PAP data. PAP adherence was calculated between March 1, 2020 to COVID-19 confirmation, death, disenrollment or study end date (July 31, 2020), whichever came earlier. COVID-19 outcomes were evaluated based on OSA status and PAP adherence: patients with PAP <2 hours/night were considered “untreated”; ≥2 hours/night were “treated”; 2–3.9 hours/night were “moderately-treated”; ≥4 hours/night were “well-treated”. Apnea hypopnea index (AHI) defined OSA severity. Multiple logistic regression evaluated the association of various demographic/clinical factors. Results Of 81,932 patients (39.8% female, age 54.0±14.9 years) analyzed, 1493 (1.8%) had COVID-19 with 224 (0.3%) hospitalizations and 61 (0.07%) resulting in intensive care or death. Increased severity of “Untreated” OSA was associated with higher COVID-19 rate and lower when “treated” [No OSA 1.7%; Mild 2%; Moderate 2%; Severe 2.4%; OSA unknown severity 2%; Treated 1.4%; p<0.0001]. Better PAP adherence was associated with reduced infection rate [“untreated” 2.1%; “moderately-treated” 1.7%, “well-treated” 1.3%, No OSA 1.7%; p=<0.0001]. Multivariable analysis confirmed increased infection rate with OSA versus no OSA [OR 0.82(0.70,0.96)] and the benefit of good PAP adherence versus “untreated” [“moderately-treated” OR 0.82 (0.65, 1.03); “well-treated” OR (0.69 (0.59, 0.80)]. Increased infection rate was also associated with obesity, higher Charlson Comorbidity score, Black and Hispanic ethnicities, and Medicaid enrollment; increasing age was associated with reduced infection rate. Separate multivariable analysis showed dose-response association of OSA severity on infection rate [Mild OR 1.21 (1.01,1.44 95%CI); Moderate-Severe OR 1.27 (1.07,1.51) versus no OSA]. Neither OSA presence nor PAP adherence significantly impacted rate of hospitalization nor intensive care or death. Conclusion Significant associations emerged with OSA increasing and PAP therapy reducing COVID-19 infection rate. Findings support continued PAP use during the pandemic. Support (if any) AASM Foundation SRA: 205-SR-19