18 research outputs found

    Successful management of acute thromboembolic disease complicated with heparin induced thrombocytopenia type II (HIT II): a case series

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    Heparin-induced thrombocytopenia type II (HIT II) is a rare immune-mediated complication of heparin. The diagnosis of HIT is considered in patients exposed to heparin, presenting with thrombocytopenia and thrombosis

    Inflammatory Markers in Middle-Aged Obese Subjects: Does Obstructive Sleep Apnea Syndrome Play a Role?

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    Background. Obstructive Sleep Apnea Syndrome (OSAS) is associated with inflammation, but obesity may be a confounding factor. Thus, the aim of this study was to explore differences in serum levels of inflammation markers between obese individuals with or without OSAS. Methods. Healthy individuals (n = 61) from an outpatient obesity clinic were examined by polysomnography and blood analysis, for measurement of TNF-α, IL-6, CRP, and fibrinogen levels. According to Apnea-Hypopnea Index (AHI), participants were divided into two BMI-matched groups: controls (AHI < 15/h, n = 23) and OSAS patients (AHI ≥ 15/h, n = 38). Results. OSAS patients had significantly higher TNF-α levels (P < .001) while no other difference in the examined inflammation markers was recorded between groups. Overall, TNF-α levels were correlated with neck circumference (P < .001), AHI (P = .002), and Oxygen Desaturation Index (P = .002). Conclusions. Obese OSAS patients have elevated TNF-α levels compared to BMI-matched controls, suggesting a role of OSAS in promoting inflammation, possibly mediated by TNF-a

    Effect of continuous positive airway pressure therapy on a large hemangioma complicated with obstructive sleep apnea syndrome: a case report

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    <p>Abstract</p> <p>Introduction</p> <p>Hemangiomas involving the upper airway can be an uncommon cause of obstructive sleep apnea syndrome.</p> <p>Case presentation</p> <p>A 26-year-old Caucasian man with a known history of a large hemangioma of his head and neck presented with sleep-disordered breathing to the sleep unit of our hospital. Severe obstructive sleep apnea syndrome was revealed on polysomnography. Nasal continuous positive airway pressure was implemented effectively, reducing daytime hypersomnolence and significantly improving sleep parameters. After three years of adherent use, the patient remains in a good condition and the hemangioma is stable.</p> <p>Conclusion</p> <p>Application of continuous positive airway pressure can be an effective treatment for patients with obstructive sleep apnea syndrome complicated with vascular tumors. Periodic follow-up of these patients is necessary, as little is known about the long-term effects of continuous positive airway pressure therapy.</p

    The use of Bispectral Index (BIS) values as an indicator for sleep staging

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    SUMMARY. The aim of this study was to examine whether BIS values during sleep correspond to the different sleep stages, in order to assess BIS as an alternative means of sleep staging. Patients-Methods: The study was conducted on 23 patients who were examined concurrently with polysomnography (PSG) for diagnosing sleep-disordered breathing and with BIS. Exclusion criteria were sleep duration <4 hours, sleep efficiency <80% on PSG and signal quality index (SQI) <50% on BIS. Comparisons in recordings were performed. Results: The patients provided 806 different sleep periods. The mean BIS value was 93.6±4.8 in the wakeful state, and in sleep, according to each stage: 84±11.5 in stage 1, 75.4±13.2 in stage 2, 53.4±15.8 in slow wave sleep (SWS), and 81.5±13.3 during REM sleep. A significant difference was observed between BIS values in the wakeful state and stage 1 (p<0.005) and between stages 1 and 2 and SWS (p<0.001), but not between stage 1 and REM (p=0.102). Conclusion: BIS values decrease with sleep and remain low, with the exception of REM sleep, the BIS values in which overlap with those in stage 1, reducing the sensitivity of BIS in sleep staging. Pneumon 2009, 22(3):230-239

    Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

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    INTRODUCTION:Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND METHODS:Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. RESULTS:A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION:We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. CONCLUSIONS:Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. TRIAL REGISTRATION:ClinicalTrials.gov NCT01161381

    Determinants of continuous positive airway pressure compliance in a group of Greek patients with obstructive sleep apnea

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    Objective: The aim of the study was to investigate the patients’ characteristics that correlate with greater compliance to CPAP use. Methods: Patients diagnosed with OSAHS and treated with CPAR who had at least one follow-up visit in the Sleep Clinic during one year, were included in the study. Demographic data, history of symptoms, comorbidities, Body Mass Index (BMI), Epworth Sleepiness Scale questionnaire (ESS), were obtained from patients before and under CPAP use. All variables were correlated with average daily CPAP use. Objective and subjective compliance were estimated and a cutoff point of 4.5 h/d was used to distinguish ‘more compliant’ from less ‘compliant’ patients. Results: Ninety eight patients, with a mean age (+/- SD) of 55.5 (+/- 11.1) years were examined. Patients’ symptoms improved after CPAP use. The objective compliance was 5.3 +/- 1.6 h/d whereas the subjective compliance was higher. Only 25% of patients were characterized as ‘more compliant’. Compliance was positively correlated in a significant way with age and female gender, and negatively correlated with neck circumference, preexisting nasal problems and minimum saturation during sleep. Patients with arterial hypertension showed a trend to better compliance. Weight gain was more frequently observed in ‘less compliant’ patients. Conclusion: To our knowledge this is the first study examining parameters of CPAP compliance in a Greek population of OSAHS patients. Age, gender and minimum saturation during sleep were related to better compliance whereas higher neck circumference and preexisting nasal problems were the parameters related to a worse adherence to treatment. (C) 2009 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved

    Descriptive statistics from the study population (N = 100).

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    <p>BMI = Body Mass Index, T90 = Time with SaO2<90% (in percentage of Total Sleep Time), AHI = Apnea-Hypopnea Index (in events/hour), AI = Apnea Index, HI = Hypopnea Index, LLE = Largest Lyapunov Exponent, f = flow signal, t = thoracic belt signal, DFA = Detrended Fluctuation Analysis α factor (slow-fast), APEN = Approximate Entropy (see text & <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150163#sec014" target="_blank">Supporting Information</a> for further details).</p
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