In the 12-week treatment evaluation of nasal polyposis, pretreatment with oral steroids had no significant advantage for most nasal symptoms other than earlier relief; however, combined oral and nasal steroid therapy more effectively improved hyposmia, polyps size, and nasal airflow. Polyps size grade 3 and/or endoscopic signs of meatal discharge predisposed to a poorer treatment outcome.
Background: Although oral steroids are widely used for the treatment of nasal polyposis, a subset of patients shows an unfavorable therapeutic outcome. The aim of this study was to evaluate the efficacy of a short course of oral prednisolone in nasal polyposis and to evaluate which, if any, clinical variables can predict treatment outcome in these patients. Methodology/principal: Using a 3:2 randomization ratio, 63 patients with nasal polyposis received 50 mg of prednisolone and 46 patients received placebo daily for 14 days. Clinical response was evaluated by total nasal symptoms score (TNSS), peak expiratory flow index (PEFI) and total nasal polyps score (TNPS). Potential predictor variables were assessed by clinical history, nasal endoscopy, allergy skin test and sinus radiography. Results: The prednisolone-treated group showed significantly greater improvements in all nasal symptoms, nasal flow and polyp size than the placebo-treated group (p < 0.001, all). In the prednisolone-treated group, patients with grade 3 polyps and positive nasal endoscopy showed significantly less improvement in TNSS, PEFI and TNPS than patients with grades 1-2 size and with negative nasal endoscopy. Conclusions: A short course of oral steroids showed good clinical efficacy in the treatment of nasal polyposis, however, polyps size grade 3 and/or positive nasal endoscopy predispose to a poorer treatment outcome.
Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.
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