Are we missing obstructive sleep apnea diagnosis?

The Usefulness of Modified Mallampati Score and CT Upper Airway Volume Measurements in Diagnosing OSA among Patients with Breathing-Related Sleep Disorders

Background: Obstructive sleep apnea (OSA) is a condition causing restriction of the airflow through the upper airways during sleep, despite preserved inspiratory muscle activity. This may lead to the development of secondary hypertension, ischemic heart disease, myocardial infarction, and arrhythmia. Moreover, the prevalence of OSA is on the rise. Methods: Comparison of scores from the Berlin Questionnaire, modified Mallampati scores (MMP), pulse oximetry readings and Upper Airway Volume (UAV) data obtained from CBCT (Cone Beam Computed Tomography). The study group of 129 patients of both sexes reporting sleep-related breathing problems completed the Berlin Questionnaire, had their oxygen saturation (SpO2) measured with a PO40 pulse oximeter, and oropharyngeal tissues assessed according to MMP. CBCT scans were put into 3D Amira TM 3D computer analysisto obtain UAV values. Results: Snoring was associated with significantly higher BMI compared to non-snoring patients. Furthermore, snoring patients had higher heart rate, modified Mallampati score, and lower UAV than the non-snoring group. The multifactorial analysis showed MMP as a useful indicator of the risk of snoring (OR = 7.468 (3863–14, 507, p < 0.001)). Conclusions: The composition of MMP together with UAV and the Berlin questionnaire might be reliable indicators to assess the risk of snoring.

Is subjective sleep evaluation a good predictor for obstructive sleep apnea?

OBJECTIVE

To compare subjective sleep evaluation obtained using four questionnaires with polysomnography results for individuals with and without obstructive sleep apnea.

METHODS

Observational and analytical study in which individuals underwent polysomnography were studied retrospectively to investigate sleep disorders. We compared subjective data from a research database used to predict obstructive sleep apnea based on the STOP-BANG questionnaire, evaluation of excessive daytime sleepiness (Epworth Sleepiness Scale), sleep quality questionnaire (Mini Sleep Questionnaire) and Post-Sleep Data Collection Instrument with the self-reported total sleep time and sleep-onset latency for subjects with and without obstructive sleep apnea.

RESULTS

The STOP-BANG questionnaire was a good predictor for the diagnosis of obstructive sleep apnea. However, the other instruments did not show a significant difference between healthy and sick individuals. Patients' perceptions of their sleep onset time were significantly lower than the polysomnographic data, but this difference remained for both subjects with and without obstructive sleep apnea. No difference was found between the subjective duration of sleep and the total sleep time assessed by polysomnography in either the healthy subjects or the patients.

CONCLUSION

Except for the STOP-BANG questionnaire, subjective evaluation of sleepiness, sleep quality, perception of onset, and total sleep time are not important parameters for the diagnosis of obstructive sleep apnea, which reinforces the need for an active search for better management of these patients.

Features of continuous glycemic profile and glycemic variability in patients with obstructive sleep apnea syndrome

AIMS

To investigate glycemic variability (GV) in Obstructive Sleep Apnea Syndrome (OSAS) patients by monitoring continuous blood glucose profile.

METHODS

OSAS group (n=86) and normal control group (n=40) were included. Continuous blood glucose was monitored. The relationship of GV, insulin resistance index (IRI) and the respiratory disturbance index (AHI) were analyzed.

RESULTS

The daily average blood glucose level was significantly higher in the OSAS patients than in the control group (6.31±0.61vs. 4.94±0.78; P<0.01). The postprandial glycemic peaks in the OSAS patients were significantly higher and prolonged. The indicators of GV were all significantly higher in the OSAS patients, including blood glucose fluctuation coefficient (BGFC, 1.93±0.71vs. 1.21±0.38, P<0.05), mean amplitude of glycemic excursions (MAGE, 4.18±0.65vs. 2.18±0.48; P<0.05) and night mean amplitude of glycemic excursions (NMAGE, 2.00±0.53vs. 1.11±0.43; P<0.05). Pearson correlation analysis showed that among the OSAS patients, the severity of OSAS (AHI) was positively correlated with the IRI (r=0.310); and the GV indicators (MAGE and NMAGE) were positively correlated with IRI and AHI (r=0.318 and 0.349, respectively) (P<0.01 or 0.001).

CONCLUSIONS

Continuous glycemic spectrum and GV provide comprehensive glycemic profiles and may reveal important aspects of glucose metabolism abnormality beyond regular examinations, and are therefore of particular significance for glycemic management in OSAS patients.