Methods: Twelve asthmatic patients were individually matched Selonsertib cell line with 12 COPD patients by forced expiratory volume during the first second (FEV(1)) as a percent (+/- 5%) of the reference value (Delta FEV(1)) and by age (+/- 4 years). The subjects performed

baseline maximal expiratory flow-volume curves (MEFV) and then repeated the same maneuvers through a valve that occluded the air flow 6 times per second with an open/closed time ratio of 4/1. We then plotted an envelope of the expiration-interrupted curve passing through the SF peaks, measured the increase in flow at 50% of the forced vital capacity between the baseline curve and the envelope curves (Delta Vmax(50)), and compared the FEV(1) of the interrupted curve to the FEV(1) obtained from control MEFV curves (Delta FEV(1)). Results: We found significantly higher values for Delta Vmax(50) (p < 0.03) and Delta FEV(1) (p < 0.01) in the asthmatics compared to the COPD patients. Conclusions: The differences reported here are best explained by

a greater preservation of elastic recoil pressure at a similar degree of airflow limitation in the asthmatics than in the COPD patients. Copyright (C) 2010 S. Karger AG, Basel”
“Objective: To develop a screening process of obstructive sleep apnea in children based on a combination of symptoms and oxygen desaturation index (ODI).

Materials and Methods: We performed Panobinostat manufacturer a retrospective study of 141 Chinese patients who were referred to a pediatric sleep laboratory for possible obstructive sleep apnea (OSA). The parents of each patient answered a questionnaire before their child underwent polysomnography (PSG) in the laboratory. An apnea-hypopnea index (AHI) greater than five on nocturnal PSG was defined as OSA. The nocturnal PSG was interpreted by a sleep laboratory physician. The ODI and occurrence ratio of sleep problems such as snoring, observable apnea during sleep,

mouth breathing: and restless sleep, among others were compared between the OSA and non-OSA groups using the chi-square test. Items that indicated statistically significant differences were tested with non-parametric Spearman selleck compound correlation tests to determine the correlation between these items and AHI. ODI and the items that indicated a statistically significant difference between the OSA and non-OSA groups were analyzed using binary logistic regression. The ODI cut-off point was determined through ODI receiver operating characteristic analysis to distinguish between OSA and non-OSA. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to determine the combination of OSA predictors that exhibited the best diagnostic performance.

Results: Among the 141 patients, 78 (55%) were diagnosed with OSA by PSG.