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Effect of stroke location on the laryngeal cough reflex and pneumonia risk


W Robert Addington, Robert E Stephens, John G Widdicombe, and Kamel Rekab



The purpose of this study was to evaluate the risk of developing pneumonia in acute stroke patients comparing the early anatomical stroke location and laryngeal cough reflex (LCR) testing.


A prospective study of 818 consecutive acute stroke patients utilizing a reflex cough test (RCT), which assesses the neurological status of the LCR compared to magnetic resonance imaging or computerized tomography for stroke location and subsequent pneumonia outcome. Stroke diagnosis and stroke location were made by a neurologist and clinical radiologist, respectively; both were blinded to the RCT results.


Brainstem (p-value < .007) and cerebral strokes (p-value < .005) correlated with the RCT results and pneumonia outcome. Of the 818 patients, 35 (4.3%) developed pneumonia. Of the 736 (90%) patients who had a normal RCT, 26 (3.5%) developed pneumonia, and of the 82 (10%) patients with an abnormal RCT, 9 (11%) developed pneumonia despite preventive interventions (p-value < .005). The RCT had no serious adverse events.


The RCT acted as a reflex hammer or percussor of the LCR and neurological airway protection and indicated pneumonia risk. Despite stroke location, patients may exhibit "brainstem shock," a global neurological condition involving a transient or permanent impairment of respiratory drive, reticular activating system or LCR. Recovery of these functions may indicate emergence from brainstem shock, and help predict morbidity and mortality outcome.

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Sleep Apnea Avoidance Pillow Effects





The study was performed to determine the ability of a new inclined pillow to treat snoring and obstructive sleep apnea syndrome. The SONA pillow® is a triangular pillow with space to place your arm under the head while sleeping on the side. Twenty two patients with NPSG proven OSAS were included in this study, this group included eleven mild, eight moderate and three severe sleep apnea patients. All these patients had a second attended nocturnal polysomnogram performed while utilizing this specific inclined pillow. The pillow was found to be an effective and easily employable treatment for mild (RDI 5-19) and moderate (RDI 20-40) obstructive sleep apnea and snoring. In this group RDI ranged from 5.1 to 35.2 and decreased on the average from 17 events per hour to less than 5 events per hour while utilizing the inclined pillow, which is statistically significant with a Pvalue of 0.000. Also a statistical significant difference was noted in REM RDI decrement in all patients with mild to moderate sleep apnea with a P-value of .001 and the increase in SAO2 was significant with a P-value of 0.004. Snoring was found to be affectively decreased or eliminated by this method P-value 0.017 in all patients.


The SONA inclined pillow® is an effective treatment for obstructive sleep apnea syndrome in patients with mild to moderate obstructive sleep apnea. Utilizing this pillow stops snoring.

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A Markov chain model for predicting the reliability of multi-build software

J.A. Whittakera,*, K. Rekabb, M.G. Thomasonc


In previous work we developed a method to model software testing data, including both failure events and correct behavior, as a finitestate, discrete-parameter, recurrent Markov chain. We then showed how direct computation on the Markov chain could yield various reliability related test measures. Use of the Markov chain allows us to avoid common assumptions about failure rate distributions and allows both the operational profile and test coverage of behavior to be explicitly and automatically incorporated into reliability computation. Current practice in Markov chain based testing and reliability analysis uses only the testing (and failure) activity on the most recent software build to estimate reliability. In this paper we extend the model to allow use of testing data on prior builds to cover the real-world scenario in which the release build is constructed only after a succession of repairs to buggy pre-release builds. Our goal is to enable reliability prediction for future builds using any or all testing data for prior builds. The technique we present uses multiple linear regression and exponential smoothing to merge multi-build test data (modeled as separate Markov chains) into a single Markov chain which acts as a predictor of the next build of testing activity. At the end of the testing cycle, the predicted Markov chain represents field use. It is from this chain that reliability predictions are made. q 2000 Elsevier Science B.V. All rights reserved

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