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Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire.

ABSTRACT: Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5-15; 19% had AHI 15-30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703-0.888], 0.931 [95% CI, 0.772-0.992], and 0.735 [95% CI, 0.589-0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828-0.987], 0.944 [95% CI, 0.727-0.999], and 0.933 [95% CI, 0.779-0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1.

SUBMITTER: Yeh E

PROVIDER: S-EPMC8504733 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Study objectivesThe STOP-Bang questionnaire is a concise and easy screening tool for obstructive sleep apnea (OSA). Using modified body mass index (BMI), we assessed the diagnostic performance of the STOP-Bang questionnaire in predicting OSA in ethnically different groups of patients undergoing surgery.MethodsThis was a multicenter prospective cohort study involving patients with cardiovascular risk factors who were undergoing major noncardiac surgery. Patients underwent home sleep apnea testing. All patients completed the STOP-Bang questionnaire. The predictive parameters of STOP-Bang scores were calculated against the apnea-hypopnea index.ResultsFrom 4 ethnic groups 1,205 patients (666 Chinese, 161 Indian, 195 Malay, and 183 Caucasian) were included in the study. The mean BMI ranged from 25 ± 4 to 30 ± 6 kg/m² and mean age ranged from 64 ± 8 to 71 ± 10 years. For the Chinese and Indian patients, diagnostic parameters are presented using BMI threshold of 27.5 kg/m² with the area under curve to predict moderate-to-severe OSA being 0.709 (0.665-0.753) and 0.722 (0.635-0.808), respectively. For the Malay and Caucasian, diagnostic parameters are presented using BMI threshold of 35 kg/m² with the area under curve for predicting moderate-to-severe OSA being 0.645 (0.572-0.720) and 0.657 (0.578-0.736), respectively. Balancing the sensitivity and specificity, the optimal STOP-Bang thresholds for the Chinese, Indian, Malay, and Caucasian groups were determined to be 4 or greater.ConclusionsFor predicting moderate-to-severe OSA, we recommend BMI threshold of 27.5 kg/m² for Chinese and Indian patients and 35 kg/m² for Malay and Caucasian patients. The optimal STOP-Bang threshold for the Chinese, Indian, Malay and Caucasian groups is 4 or greater.Clinical trial registrationRegistry: ClinicalTrials.gov; Name: Postoperative Vascular Events in Unrecognized Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/study/NCT01494181; Identifier: NCT01494181.

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Detection of obstructive sleep apnea using Belun Sleep Platform wearable with neural network-based algorithm and its combined use with STOP-Bang questionnaire.

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