Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch

Upper Airway Surgery or Weight Control? Modified Drug-Induced Sleep Endoscopy for Obstructive Sleep Apnea

OBJECTIVE

To identify the value of head rotation in the supine position and oral appliance (OA) use in drug-induced sleep endoscopy (DISE).

STUDY DESIGN

Eighty-three sleep apnea adults undergoing target-controlled infusion-DISE (TCI-DISE) were recruited from a tertiary academic medical center.

SETTING

During DISE, 4 positions were utilized: supine position (position 1), head rotation (position 2), mandibular advancement using an OA (position 3), and head rotation with an OA (position 4).

METHODS

Polysomnography (PSG) data and anthropometric variables during DISE were analyzed.

RESULTS

Eighty-three patients (65 men and 18 women; mean [standard deviation, SD], 48.5 [11.0] years) who underwent PSG and TCI-DISE were included. The mean (SD) apnea-hypopnea index (AHI) was 35.5 (22.4) events/h. Twenty-three patients had persistent complete concentric velopharyngeal collapse in the supine position, even with concurrent head rotation and OA (position 4). Their mean (SD) AHI was 54.7 (24.6) events/h, significantly higher than that of the 60 patients without such collapse in position 4 (p < .001). Their mean (SD) body mass index (BMI) was 29.0 (4.1) kg/m2 , also significantly higher (p = .005). After adjustment for age, BMI, tonsil size, and tongue position, the degree of velum and tongue base obstruction was significantly associated with sleep apnea severity in positions 2, 3, and 4.

CONCLUSION

We showed the feasibility, safety, and usefulness of using simple edge-to-edge, reusable OA in DISE. Patients who are not responsive to head rotation and OA during TCI-DISE may need upper airway surgery and/or weight control.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles

Objective

Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence.

Methods

Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance.

Results

InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence.

Conclusions

The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Using a Non-Wearable Actigraphy in Nursing Care for Dementia With Lewy Bodies

People who have dementia with Lewy bodies often have sleep disorders. We used non-wearable devices to record and categorize the sleep patterns of patients with Lewy body dementia. Individual sleep data at a dementia-care unit in Japan were recorded using non-wearables. One week's worth of data from 18 patients was analyzed. Median metrics for all participants were the following: sleep efficiency, 68% (23-89); sleep duration at night, 6.8 hours (1.6-11.1); times getting out of bed at night, 3.5 (0-13). We identified three types of abnormal sleep: extremely short sleep duration, excessive sleep duration at night, and excessive number of times getting out of bed at night. Sleep disturbances in Lewy body dementia patients are treated using various practices; staff must choose the most effective plan for each patient's situation. Monitoring patient sleep using non-wearable provides more objective data that can help staff better personalize nursing care.