Obstructive sleep apnea-Hypertension link: almost there?

Effect of antihypertensive agents on sleep apnea and ambulatory blood pressure in patients with hypertension: A randomized controlled trial

BACKGROUND

Obstructive sleep apnea (OSA) and hypertension are common conditions that may be linked through sympathetic activation and water retention. We hypothesized that diuretics, which reduce the body water content, may be more effective than amlodipine, a blood pressure (BP)-lowering agent implicated with edema, in controlling OSA in patients with hypertension. We also aimed to compare the effects of these treatments on ambulatory blood pressure monitoring (ABPM).

METHODS

In a randomized, double-blind clinical trial, we compared the effects of chlorthalidone/amiloride 25/5 mg with amlodipine 10 mg on OSA measured by portable sleep monitor and BP measured by ABPM. The study included participants older than 40 who had moderate OSA (10-40 apneas/hour of sleep) and BP within the systolic range of 140-159 mmHg or diastolic range of 90-99 mmHg.

RESULTS

The individuals in the experimental groups were comparable in age, gender, and other relevant characteristics. Neither the combination of diuretics nor amlodipine alone reduced the AHI after 8 weeks of treatment (AHI 26.3 with diuretics and 25.0 with amlodipine. P = 0.713). Both treatments significantly lowered office, 24-h, and nighttime ABP, but the two groups had no significant difference.

CONCLUSION

Chlorthalidone associated with amiloride and amlodipine are ineffective in decreasing the frequency of sleep apnea episodes in patients with moderate OSA and hypertension. Both treatments have comparable effects in lowering both office and ambulatory blood pressure. The notion that treatments could offer benefits for both OSA and hypertension remains to be demonstrated. TRIAL REGISTRATION CLINICALTRIALS.

GOV IDENTIFIER

NCT01896661.

Factors affecting the severity of the apnea hypoapnea index: a retrospective study on 838 Egyptian patients diagnosed with obstructive sleep apnea

Background

Obstructive sleep apnea (OSA) is a common condition in the general population that is highly correlated to obesity, and it is associated with major cardiovascular morbidity and mortality. In Egypt, obesity rates are known to be high; however, OSA remains largely under-diagnosed, and data on its current magnitude is very scarce. Thus, the aims of the study were to identify the percentage of OSA in a large sample of patients referred for polysomnography and to determine the effect of different demographic data on the severity of the disease.

Results

This retrospective study included 1012 patients. Medical data were reviewed by sleep specialists. The correlation between age, body mass index (BMI), and neck circumference (NC) with apnea hypoapnea index (AHI) was explored. Also, gender differences were analyzed. A total of 838 patients (81% males, 19% females) were diagnosed with OSA. Patients with mild, moderate, and severe OSA were 204 (24%), 146 (17%), and 488 (58%), respectively. Females were older than males (58.87 ± 10.25 versus 54.39 ± 22.96, p = 0.001) and BMI was not significantly different between both sexes (34.18 ± 13.53 versus 36.73 ± 23.25, p = 0.07), but NC was higher in men (43.56 ± 5.3 versus 39.34 ± 4.41, p = 0.001). AHI was significantly increased in men compared to women (47.97 ± 31.22 versus 37.95 ± 31.72, p = 0.001) and severe OSA was commonly diagnosed in men than women (p = 0.001). A positive significant correlation was found between BMI, NC with AHI, arousal index, average SpO2, and desaturation index.

Conclusion

OSA is highly prevalent among our patients. Additionally, BMI and NC independently affected the severity of their disease.

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Background

Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring.

Objective

This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered.

Methods

This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory.

Results

By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography.

Conclusions

Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.