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Study objectivesEvaluate the performance of a novel home sleep apnea test with embedded ECG (SANSA, Huxley Medical, Inc.) in the diagnosis of obstructive sleep apnea (OSA). MethodsThis prospective multicenter validation study included 340 participants who underwent simultaneous polysomnography (PSG) and SANSA recordings across 7 clinical sites. Participants were diverse across age, sex, race, skin tone, and body mass index. Diagnostic performance was assessed with the apnea-hypopnea index (AHI) using both Rule 1A and Rule 1B across standard cutoffs for mild, moderate, or severe (≥5 events/h), moderate-to-severe (≥15 events/h), and severe (≥30 events/h) OSA. The agreement for AHI and total sleep time (TST) between SANSA and consensus PSG scores from three independent scorers was evaluated using Pearson’s correlation and Bland–Altman analysis. Sensitivity and specificity were calculated at each OSA severity level. Performance of participating site PSG scores were also evaluated against consensus PSG scores for comparison. ResultsSANSA demonstrated excellent agreement with PSG for most performance parameters. AHI correlation was 0.91 (95% CI: 0.89, 0.93) using Rule 1B and 0.90 (95% CI: 0.87, 0.92) using Rule 1A. Compared to consensus scored PSG, the device detected moderate-to-severe OSA using Rule 1B (the primary endpoint) with a sensitivity of 88% (95% CI: 81, 93%) and specificity of 87% (95% CI: 82, 91%), while site PSG sensitivity was 89% (95% CI: 82, 94%) and specificity was 93% (95% CI: 88, 96%). SANSA TST highly correlated with PSG TST (R = 0.82, 95% CI: 0.78, 0.85) and classified sleep epochs with an accuracy of 87.2% (95% CI: 87.0, 87.5%). ConclusionThe SANSA home sleep apnea test demonstrated robust diagnostic performance for OSA detection including measurement of sleep compared to PSG. Its patch morphology and embedded ECG confer ease of use and multi-diagnostic potential in sleep medicine and cardiology for the detection of OSA and cardiac arrhythmias across diverse clinical populations. Clinical trial registration[https://www.clinicaltrials.gov/study/NCT06070389], identifier [NCT06070389]
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From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms
Abstract BackgroundAtrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60–90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs. MethodsWe analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI<0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features. ResultsOn the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10−52). ConclusionsOur proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.
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- Award ID(s):
- 2014438
- PAR ID:
- 10547654
- Publisher / Repository:
- medRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- medRxiv
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
- Posted Content:
- https://doi.org/10.1101/2024.06.04.24308444
