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Introduction The impact of not treating OSA identified using AASM standards (hypopneas scored using a minimum 3% O2 desaturation or arousal), but misclassified by CMS standards (hypopneas scored only if minimum 4% O2 desaturation) remains unclear. This analysis determined the ~5 year incident hypertension rates using the new 2018 ACC/AHA blood pressure (BP) guidelines in these individuals. Methods Data were analyzed from all Sleep Heart Health Study exam 2 study participants (N=1219) who were normotensive (BP≤120/80) at exam 1. The apnea hypopnea index (AHI) at exam 1 was classified into 4 categories of OSA severity: 120/80), Stage 1 (>130/80) and Stage 2 (>140/90) to determine incidence rates at exam 2. Results Five year follow-up data were available for 476 participants classified as having OSA (AHI ≥5) by AASM criteria, but not by CMS standards at exam 1. Incident hypertension rates in these misclassified participants for ACC/AHA defined BP categories were 15% (Elevated BP), 15% (Stage 1) and 6% (Stage 2). 4% of normotensive participants used hypertensive medications. Overall incidence rate of at least an elevated BP was 40% (191/476) in those with OSA defined using AASM, but not by CMS criteria and 17% (191/1219) of the overall population at risk. In comparison to those with incident hypertension and OSA identified by CMS standards, BMI (27.7 vs 30.1 kg/m2, p Conclusion Use of the CMS hypopnea definition as a component of the AHI resulted in the failure to identify a significant number of individuals with OSA who eventually developed hypertension and could have benefited from earlier diagnosis and treatment. Support HL53938

Introduction Sleep Apnea Syndrome (SAS) is largely underdiagnosed due to the cost and availability of Polysomnography (PSG). We aimed at evaluating the diagnosis of SAS with the WITHINGS Sleep Apnea Detector (SAD), a non-intrusive pressure and sound sensor placed under the mattress. Methods 118 patients (67 F, 49 years, BMI 33kg/m²) suspected of SAS had an in-laboratory PSG together with Sleep Apnea Detector. From the pressure signal, Sleep Apnea Detector derives respiratory and cardiac signals and movements. From the microphone, snoring and snorting are detected. These features are used to detect sleep periods with a Random Forest classifier and apnea and hypopnea events with a Convolutional Neural Network. The Total Sleep Time (TST) and Apnea Hypopnea Index (AHI) deduced (TSTsad, AHIsad) are compared with the PSG results scored according to AASM rules (TSTpsg, AHIpsg). AHI and TST were compared using bias and Mean Absolute Error (MAE). Sensitivity, specificity, likelihood ratios (LR) and AUROC were calculated for AHI thresholds of 15 and 30/hr. Results The average (SD) TSTpsg was 367 (61) minutes. Sleep Apnea Detector overestimated TST by 25 minutes, 7.0% of the average duration in the sample. The precision is acceptable, with a MAE=53 minutes. Average AHIpsg was 32.5 (30.1) and AHIsad 32.8 (29.9). The bias was 0.3 (95% CI [-2.7, 3.3]), MAE=10.3. The sensitivity (Se15) and specificity (Sp15) and their 95% confidence intervals were Se15=88.0% [79.0, 94.1] and Sp15=88.6% [73.3, 96.8]. Positive and negative LR were respectively LR+15=7.70 and LR-15=0.136. AUROC15=0.926. At the 30 threshold, Se30=86.0% [73.3, 94.2] and Sp30=91.2% [81.8, 96.7]. Positive and negative LR were LR+30=9.75 and LR-30=0.153. AUROC30=0.954. Conclusion Sleep Apnea Detector has excellent sensitivity and specificity, low bias and good precision. Thus it can be used as an unattended SAS screening device in patients likely to suffer from SAS. Support WITHINGS