Sleep-Disordered Breathing in Alcoholics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66229/1/j.1530-0277.1999.tb04034.x.pd

A review of ECG-based diagnosis support systems for obstructive sleep apnea

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

Obstructive sleep apnea is underrecognized and underdiagnosed in patients undergoing bariatric surgery

The aim of this study was to evaluate prevalence of obstructive sleep apnea among patients undergoing bariatric surgery and the predictive value of various clinical parameters: body mass index (BMI), neck circumference (NC) and the Epworth Sleepiness Scale (ESS). We performed a prospective, multidisciplinary, single-center observational study including all patients on the waiting list for bariatric surgery between June 2009 and June 2010, irrespective of history or clinical findings. Patients visited our ENT outpatient clinic for patient history, ENT and general examination and underwent a full night polysomnography, unless performed previously. As much as 69.9% of the patients fulfilled the criteria for OSA (mean BMI 44.2 ± SD 6.4 kg/m2); 40.4% of the patients met the criteria for severe OSA. The regression models found BMI to be the best clinical predictor, while the ROC curve found the NC to be the most accurate predictor of the presence of OSA. The discrepancy of the results and the poor statistical power suggest that all three clinical parameters are inadequate predictors of OSA. In conclusion, in this large patient series, 69.9% of patients undergoing BS meet the criteria for OSA. More than 40% of these patients have severe OSA. A mere 13.3% of the patients were diagnosed with OSA before being placed on the waiting list for BS. On statistical analysis, increased neck circumference, BMI and the ESS were found to be insufficient predictors of the presence of OSA. Polysomnography is an essential component of the preoperative workup of patients undergoing BS. When OSA is found, specific perioperative measures are indicated