Obstructive sleep apnoea and hypertension: the ESADA study

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients' sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.

Magnitude and Determinants of Patients at Risk of Developing Obstructive Sleep Apnea in a Non-Communicable Disease Clinic

Background and Objective: Obstructive sleep apnea (OSA) is a common chronic disorder worldwide, which can adversely affect the cardiovascular system among non-communicable disease (NCD) patients. It is underdiagnosed—or rather not diagnosed—in primary care settings due to the costly diagnostic techniques involved. This study aimed to assess the number of study participants at risk of developing OSA and to assess and quantify the risk factors associated with this disorder. Materials and Methods: A cross-sectional study was performed in an NCD clinic of a rural health training center, Karikalampakkam, Puducherry of South India from August 2018 to October 2018. A Modified Berlin Questionnaire (MBQ) was used to screen the study participants at risk for OSA. Four-hundred-and-seventy-three people aged 18 years and above were included in the study, using systematic random sampling. Respondents’ socio-demographic and morbidity characteristics, as well as clinical and anthropometric parameters including body weight, height, blood pressure, neck, hip and waist circumference were collected. Data was captured using Epicollect5 and analyzed using SPSS version 20.0. Results: One-fourth (25.8%) of the respondents were at high risk of developing OSA. In terms of gender, 27.9% of the men and 23.8% of the women were at high risk for OSA. In univariate analyses, the risk of developing OSA was significantly associated with a history of diabetes mellitus, hypertension, dyslipidemia and gastro-esophageal reflux disease, weight, body mass index, neck, waist and hip circumference, waist–hip ratio, and systolic and diastolic blood pressure. Multivariate logistic regression analysis showed that a history of dyslipidemia (aOR, 95% CI = 2.34, 1.22–4.48), body mass index (aOR, 95% CI = 1.15, 1.06–1.22) and waist circumference (aOR, 95% CI = 1.10, 1.07–1.14) emerged as significant predictors of risk for OSA. Conclusions: A considerable proportion of NCD patients with easily detectable attributes are at risk of developing OSA, but still remain undiagnosed at a primary health care setting. The results obtained using MBQ in this study were comparable to studies performed using polysomnography. Dyslipidemia, body mass index and waist circumference were independent risk factors for predicting a risk of developing OSA. Prospective studies are needed to confirm whether a reduction in these risk factors could reduce the risk for OSA.

Increased risk of obstructive sleep apnoea in women with polycystic ovary syndrome: a population-based cohort study

OBJECTIVE

Obesity is very common in patients with obstructive sleep apnoea (OSA) and polycystic ovary syndrome (PCOS). Longitudinal studies assessing OSA risk in PCOS and examining the role of obesity are lacking. Our objective was to assess the risk of OSA in women with vs without PCOS and to examine the role of obesity in the observed findings.

DESIGN

Population-based retrospective cohort study utilizing The Health Improvement Network (THIN), UK.

METHODS

76 978 women with PCOS and 143 077 age-, BMI- and location-matched women without PCOS between January 2000 and May 2017 were identified. Hazard ratio (HR) for OSA among women with and without PCOS were calculated after controlling for confounding variables using multivariate Cox models.

RESULTS

Median patient age was 30 (IQR: 25-35) years; median follow-up was 3.5 (IQR: 1.4-7.1) years. We found 298 OSA cases in PCOS women vs 222 in controls, with incidence rates for OSA of 8.1 and 3.3 per 10 000 person years, respectively. Women with PCOS were at increased risk of developing OSA (adjusted HR = 2.26, 95% CI: 1.89-2.69, P < 0.001), with similar HRs for normal weight, overweight and obese PCOS women.

CONCLUSIONS

Women with PCOS are at increased risk of developing OSA compared to control women irrespective of obesity. Considering the significant metabolic morbidity associated with OSA, clinicians should have a low threshold to test for OSA in women with PCOS. Whether OSA treatment has an impact on PCOS symptoms and outcomes needs to be examined.