Akademik Veri Yönetim Sistemi
Journal of Health, Medicine and Nursing, cilt.74, ss.36-41, 2020 (Hakemli Dergi)
Objective: Apnea developing during sleep prevents gas exchange in the body for 10 seconds or more. As a result, there is a significant decrease in partial oxygen pressure (PaO2) and a significant increase in partial carbon dioxide pressure (PaCO2). Patients with Obstructive Sleep Apnea Syndrome (OSAS) have systemic hypertension, heart failure, arrhythmia, chronicle artery disease, cerebrovascular diseases, and sudden death. The aim of this study is to determine the undiagnosed OSAS by looking at arterial blood gas changes in the radial artery.Materials and Methods: In this study, the patients were divided into two groups according to the severity of OSAS (the first group consisted of 13 patients, Apnea-Hypopnea Index 5 Ap15 and the second group consisted of 11 patients, Apnea-Hypopnea Index 16 and above). Blood samples were taken from radial arteries of patients in two groups. Arterial blood gases between groups; PaO2, PaCO2, arterial oxygen saturation (SaO2), hydrogen ion concentration (pH) values and accompanying diseases were examined.Results: There was no significant difference between the arterial blood gases values of patients in two groups. This suggests that arterial blood gas values in the blood taken from the radial artery alone cannot diagnose or fail to diagnose OSAS. However, patients in the two groups; 88% chronic obstructive pulmonary disease (COPD) was found as the concomitant disease. This suggests us thinking that the abnormal blood gas values (PaO2 level below normal limits and PaCO2 level above normal limits) is associated with COPD which accompanies the disease with OSAS.Discussion: Identification and treatment of OSAS prevent the development of complications associated with OSAS and may provide treatment for OSAS. Undiagnosed OSAS may be considered in the presence of abnormalities in the examination of arterial blood gas in the radial artery in patients presenting with OSAS findings (such as snoring, witnessed apnea, daytime sleepiness etc.) and accompanying COPD.This paper is just a sample template for the prospective authors of IISTE Over the decades, the concepts of holons and holonic systems have been adopted in many research fields, but they are scarcely attempted on labour planning. A literature gap exists, thus motivating the author to come up with a holonic model that uses exponential smoothing to forecast some quantitative variables in labour-intensive production. These varying parameters include the machine utilisation that reflects the demand and the worker absenteeism and turnover that constitute the disturbance. Collective equations are formulated to periodically compute the number of workers required. For model validation purpose, twenty-four-month data analysis is conducted on a mock-up basis.