Differences of Upper Airway Morphology According to Obesity: Study with Cephalometry and Dynamic MD-CT

ObjectivesWe investigated difference of parameters of polysomnography, cephalometry and dynamic multi-detector computerized tomography (MD-CT) in wake and sleep states according to obesity.MethodsWe evaluated 93 patients who underwent polysomnography and cephalometry. MD-CT was performed in 68 of these 93 patients. Fifty-nine and 34 patients were classified as obese and non-obese, with obesity defined as BMI ≥25. Cephalometry results were analyzed for 12 variables. Using the MD-CT, we evaluated dynamic upper airway morphology in wake and sleep states and divided the upper airway into four parts named as high retropalatal (HRP), low retropalatal (LRP), high retroglossal (HRG), and low retroglossal (LRG). A minimal cross sectional area (mCSA) and collapsibility index (CI) were calculated for each airway level.ResultsDiastolic blood pressure (P=0.0005), neck circumference (P<0.0001), and apnea-hypopnea index (P<0.0001) were statistically significantly different between the obese and non-obese group. Among 12 cephalometric variables, there was a significant difference in only the distance from mandibular plane to hyoid bone (P=0.003). There was statistical difference in CI of HRG and LRG in sleep state (P=0.0449, 0.0281) but no difference in mCSA in wake and sleep states.ConclusionThe obese group had more severe sleep apnea than the non-obese group. We believe that the increased severity of apnea in the obese group may be have been due to increased collapsibility of the upper airway rather than decreased size of the upper airway

Volume Management in SAN Environment

Logical volume managers have long been key components of a storage system. Their key features are creation of logical or virtual views of physical storage devices and support for various software RAID levels. These make it possible to overcome the limits to capacity, availability and performance of a physical storage device. Most logical volume managers are operated in a single system environment. They are not adequate for SAN (storage area network) environments where several hosts share and access a logical volume at the same time. Some recent logical volume managers are run in a multi-host environment. However, they cannot support the enterprise computing environments in which the system must support 24*7*365 uptime operations such as online resizing and online backup. We propose a logical volume manager called \u27SANtopia Volume Manager\u27 that supports multihost environments and provides various volume management features to support enterprise computing. Also it is a cluster enabled logical volume manager that maximizes the parallelism for high performance, and provides high scalability and high availability

Modeling the human classification of acute decompensated heart failure using abstracted medical record with machine learning

Heart failure (HF) is a clinical syndrome in which the heart is not able to properly pump blood because of structural and functional defects, resulting in the body not getting enough blood. It affects millions of people in the United States and often leads to hospitalization or mortality. Acute decompensated heart failure (ADHF) is a sudden worsening of HF symptoms and is a powerful predictor of readmission for HF and death of patients with chronic HF post-discharge. In this thesis, we used data from the Atherosclerosis Risk in Communities (ARIC) study’s community surveillance of heart failure hospitalizations to develop machine learning classification models to accurately classify if a patient did have or did not have ADHF. We used abstracted hospital records and ADHF diagnosis, done by clinician review, to train the classifiers to identify ADHF cases. After data preparation through imputation and handling collinearity in the data, we had 2,925 records in our training set and 116 records in our test dataset. Data preparation, cross-validation, model creation, and data analysis were all done in R, and we created a decision tree using the rpart package and a boosted decision tree using the adabag package. Using these models, we observed classification accuracy rates of approximately 75% in the decision tree model and 79% in the boosted decision tree model. These rates were fairly consistent with those found in literature of machine learning models that were used to classify general HF and general heart disease cases. The success of our models, relative to those in literature, demonstrate the potential for machine learning to help identify ADHF cases among HF-related hospitalizations in the clinical setting.Bachelor of Science in Public Healt

Characteristics of Mechanical Ventilation Employed in Intensive Care Units: A Multicenter Survey of Hospitals

A 1D point-prevalence study was performed to describe the characteristics of conventional mechanical ventilation in intensive care units (ICUs). In addition, a survey was conducted to determine the characteristics of ICUs. A prospective, multicenter study was performed in ICUs at 24 university hospitals. The study population consisted of 223 patients who were receiving mechanical ventilation or had been weaned off mechanical ventilation within the past 24 hr. Common indications for the initiation of mechanical ventilation included acute respiratory failure (66%), acute exacerbation of chronic respiratory failure (15%) (including tuberculosis-destroyed lung [5%]), coma (13%), and neuromuscular disorders (6%). Mechanical ventilation was delivered via an endotracheal tube in 68% of the patients, tracheostomy in 28% and facial mask with noninvasive ventilation (NIV) in 4%. NIV was used in 2 centers. In patients who had undergone tracheostomy, the procedure had been performed 16.9±8.1 days after intubation. Intensivists treated 29% of the patients. A need for additional educational programs regarding clinical practice in the ICU was expressed by 62% of the staff and 42% of the nurses. Tuberculosis-destroyed lung is a common indication for mechanical ventilation in acute exacerbation of chronic respiratory failure, and noninvasive ventilation was used in a limited number of ICUs

The Impact of Overactive Bladder on Health-Related Quality of Life, Sexual Life and Psychological Health in Korea

Purpose We aimed to estimate the prevalence of overactive bladder (OAB) in Korea, to assess the variation in prevalence by sex and age, and to measure the impact of OAB on quality of life. Methods A population-based, cross-sectional telephone survey was conducted between April and June 2010 with a questionnaire regarding the prevalence of OAB, demographics, and the impact of OAB on quality of life. A geographically stratified random sample of men and women aged ≥30 years was selected. Results The overall prevalence of OAB was 22.9% (male, 19%; female, 26.8%). Of a total of 458 participants with OAB, 37.6% and 19.9% reported moderate or severe impact on their daily life and sexual life (5.6% and 3.5%, respectively, in participants without OAB). Anxiety and depression were reported by 22.7% and 39.3% of participants with OAB, respectively (9.7% and 22.8%, respectively, in participants without OAB). Only 19.7% of participants with OAB had consulted a doctor for their voiding symptoms, but 50.7% of respondents with OAB were willing to visit a hospital for the management of their OAB symptoms. Conclusions This study confirmed that OAB symptoms are highly prevalent in Korea, and many sufferers appear to have actively sought medical help. OAB has severe effects on daily and sexual life as well as psychological health