RESUMEN

Objective: In adults with sleep complaints, we assessed the software of automatic analysis of mandibular movements to identify sleep and wake states by confrontation with the polysomnography (PSG) and the actigraphy (ACTG). Material and Methods: Simultaneous and synchronized in-lab PSG, ACTG, and JAWAC were carried out in 100 patients with a sleep complaint. Epoch by epoch analysis was realized to assess the ability to sleep-wake distinction. Sleep parameters as measured by the three devices were compared. These included three regularly reported parameters: total sleep time (TST), sleep onset latency (SOL), and wake after sleep onset (WASO). Also, two supplementary parameters, wake during sleep period (WDSP) and latency to arising (LTA) were added to measure separately the quiet wakefulness states. Results: The epoch by epoch analysis showed that the JAWAC, as compared to ACTG, classified sleep and wake states with greater specificity, while the overall accuracy and sensitivity of the two devices were comparable. The sleep parameters analysis showed that for the JAWAC estimates, the differences in TST, SOL, and LTA values were not statistically significant. However, WDSP and subsequently WASO were slightly underestimated. In contrast, the dissimilarities between ACTG estimates and PSG measurements of all the above sleep parameters were statistically significant; TST was overestimated whilst SOL, LTA, WDSP, and WASO were underestimated. Conclusion: This study indicated that, besides its ability to reliably estimate TST, the JAWAC based on mandibular movements' analysis was able, in adults with sleep complaints, to overcome the important problem of the recognition of the state of quiet wakefulness.

RESUMEN

BACKGROUND: Severe obstructive sleep apnea (sOSA) and preoperative hypoxemia are risk factors of postoperative complications. Patients exhibiting the combination of both factors are probably at higher perioperative risk. Four scores (STOP-Bang, P-SAP, OSA50, and DES-OSA) are currently used to detect OSA patients preoperatively. This study compared their ability to specifically detect hypoxemic sOSA patients. METHODS: One hundred and fifty-nine patients scheduled for an overnight polysomnography (PSG) were prospectively enrolled. The ability of the four scores to predict the occurrence of hypoxemic episodes in sOSA patients was compared using sensitivity (Se), specificity (Sp), Youden Index, Cohen kappa coefficient, and the area under ROC curve (AUROC) analyses. RESULTS: OSA50 elicited the highest Se [95% CI] at detecting hypoxemic sOSA patients (1 [0.89-1]) and was significantly more sensitive than STOP-Bang in that respect. DES-OSA was significantly more specific (0.58 [0.49-0.66]) than the three other scores. The Youden Index of DES-OSA (1.45 [1.33-1.58]) was significantly higher than those of STOP-Bang, P-SAP, and OSA50. The AUROC of DES-OSA (0.8 [0.71-0.89]) was significantly the largest. The highest Kappa value was obtained for DES-OSA (0.33 [0.21-0.45]) and was significantly higher than those of STOP-Bang, and OSA50. CONCLUSIONS: In our population, DES-OSA appears to be more effective than the three other scores to specifically detect hypoxemic sOSA patients. However prospective studies are needed to confirm these findings in a perioperative setting. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov: NCT02050685.

Asunto(s)

RESUMEN

OBJECTIVE: In a device based on midsagittal jaw movements analysis, we assessed a sleep-wake automatic detector as an objective method to measure sleep in healthy adults by comparison with wrist actigraphy against polysomnography (PSG). METHODS: Simultaneous and synchronized in-lab PSG, wrist actigraphy and jaw movements were carried out in 38 healthy participants. Epoch by epoch analysis was realized to assess the ability to sleep-wake distinction. Sleep parameters as measured by the three devices were compared. This included three regularly reported parameters: total sleep time, sleep onset latency, and wake after sleep onset. Also, two supplementary parameters, wake during sleep period and latency time, were added to measure quiet wakefulness state. RESULTS: The jaw movements showed sensitivity level equal to actigraphy 96% and higher specificity level (64% and 48% respectively). The level of agreement between the two devices was high (87%). The analysis of their disagreement by discrepant resolution analysis used PSG as resolver revealed that jaw movements was right (58.9%) more often than actigraphy (41%). In sleep parameters comparison, the coefficient correlation of jaw movements was higher than actigraphy in all parameters. Moreover, its ability to distinct sleep-wake state allowed for a more effective estimation of the parameters that measured the quiet wakefulness state. CONCLUSIONS: Midsagittal jaw movements analysis is a reliable method to measure sleep. In healthy adults, this device proved to be superior to actigraphy in terms of estimation of all sleep parameters and distinction of sleep-wake status.

RESUMEN

The mandible movement (MM) signal provides information on mandible activity. It can be read visually to assess sleep-wake state and respiratory events. This study aimed to assess (1) the training of independent scorers to recognize the signal specificities; (2) intrascorer reproducibility and (3) interscorer variability. MM was collected in the mid-sagittal plane of the face of 40 patients. The typical MM was extracted and classified into seven distinct pattern classes: active wakefulness (AW), quiet wakefulness or quiet sleep (QW/S), sleep snoring (SS), sleep obstructive events (OAH), sleep mixed apnea (MA), respiratory related arousal (RERA) and sleep central events (CAH). Four scorers were trained; their diagnostic capacities were assessed on two reading sessions. The intra- and interscorer agreements were assessed using Cohen's κ. Intrascorer reproducibility for the two sessions ranged from 0.68 [95% confidence interval (CI): 0.59-0.77] to 0.88 (95% CI: 0.82-0.94), while the between-scorer agreement amounted to 0.68 (95% CI: 0.65-0.71) and 0.74 (95% CI: 0.72-0.77), respectively. The overall accuracy of the scorers was 75.2% (range: 72.4-80.7%). CAH MMs were the most difficult to discern (overall accuracy 65.6%). For the two sessions, the recognition rate of abnormal respiratory events (OAH, CAH, MA and RERA) was excellent: the interscorer mean agreement was 90.7% (Cohen's κ: 0.83; 95% CI: 0.79-0.88). The discrimination of OAH, CAH, MA characteristics was good, with an interscorer agreement of 80.8% (Cohen's κ: 0.65; 95% CI: 0.62-0.68). Visual analysis of isolated MMs can successfully diagnose sleep-wake state, normal and abnormal respiration and recognize the presence of respiratory effort.

Asunto(s)

RESUMEN

In-laboratory polysomnography is the 'gold standard' for diagnosing obstructive sleep apnea syndrome, but is time consuming and costly, with long waiting lists in many sleep laboratories. Therefore, the search for alternative methods to detect respiratory events is growing. In this prospective study, we compared attended polysomnography with two other methods, with or without mandible movement automated analysis provided by a distance-meter and added to airflow and oxygen saturation analysis for the detection of respiratory events. The mandible movement automated analysis allows for the detection of salient mandible movement, which is a surrogate for arousal. All parameters were recorded simultaneously in 570 consecutive patients (M/F: 381/189; age: 50±14 years; body mass index: 29±7 kg m(-2) ) visiting a sleep laboratory. The most frequent main diagnoses were: obstructive sleep apnea (344; 60%); insomnia/anxiety/depression (75; 13%); and upper airway resistance syndrome (25; 4%). The correlation between polysomnography and the method with mandible movement automated analysis was excellent (r: 0.95; P<0.001). Accuracy characteristics of the methods showed a statistical improvement in sensitivity and negative predictive value with the addition of mandible movement automated analysis. This was true for different diagnostic thresholds of obstructive sleep severity, with an excellent efficiency for moderate to severe index (apnea-hypopnea index ≥15h(-1) ). A Bland & Altman plot corroborated the analysis. The addition of mandible movement automated analysis significantly improves the respiratory index calculation accuracy compared with an airflow and oxygen saturation analysis. This is an attractive method for the screening of obstructive sleep apnea syndrome, increasing the ability to detect hypopnea thanks to the salient mandible movement as a marker of arousals.

Asunto(s)

RESUMEN

PURPOSE: Estimating the total sleep time in home recording devices is necessary to avoid underestimation of the indices reflecting sleep apnea and hypopnea syndrome severity, e.g., the apnea-hypopnea index (AHI). A new method to distinguish sleep from wake using jaw movement signal processing is assessed. METHODS: In this prospective study, jaw movement signal was recorded using the Somnolter (SMN) portable monitoring device synchronously with polysomnography (PSG) in consecutive patients complaining about a lack of recovery sleep. The automated sleep/wake scoring method is based on frequency and complexity analysis of the jaw movement signal. This computed scoring was compared with the PSG hypnogram, the two total sleep times (TST(PSG) and TST(SMN)) as well. RESULTS: The mean and standard deviation (in minutes) of TST(PSG) on the whole dataset (n = 124) were 407 ± 95.6, while these statistics were 394.2 ± 99.3 for TST(SMN). The Bland and Altman analysis of the difference between the two TST was 12.8 ± 57.3 min. The sensitivity and specificity (in percent) were 85.3 and 65.5 globally. The efficiency decreased slightly when AHI lies between 15 and 30, but remained similar for lower or greater AHI. In the 24 patients with insomnia/depression diagnosis, a mean difference in TST of -3.3 min, a standard deviation of 58.2 min, a sensitivity of 86.3%, and a specificity of 66.2% were found. CONCLUSIONS: Mandible movement recording and its dedicated signal processing for sleep/wake recognition improve sleep disorder index accuracy by assessing the total sleep time. Such a feature is welcome in home screening methods.

Asunto(s)