The result of this three-step process was a 32-item survey of asthma knowledge, attitudes, and perceptions, the Chicago Community Asthma Survey (CCAS-32). Finally, the survey introduction was examined to see if modification would alter the responses to survey items (Step 4).
This project was approved by the Institutional Review Board of Rush-Presbyterian-St. Luke’s Medical Center.
Step 2: Initial Construction Step 1A: Assessment of Relevant Issues
As seen in Figure 1, the survey development process began by identifying relevant content areas. This was accomplished by reviewing published literature in the area of asthma education and by gathering the advice of local practitioners who participate in asthma care. Read the rest of this entry »

Development of a Survey of Asthma Knowledge, Attitudes, and PerceptionsThe Chicago Community Asthma Survey
Recent trends in the social burden of asthma have become part of the debate about the health of the general public. However, there is very little information about the general public’s perception of the diagnosis of asthma and its impact on individuals, their families, and their communities. If the public’s knowledge about asthma is very good, national campaigns targeting asthma awareness, such as the National Asthma Education and Prevention Program, are likely to have little impact. If, on the other hand, the general public has some misconceptions about asthma and its impact, these misconceptions may need to be addressed if asthma outcomes are to be improved. Read the rest of this entry »

Various oximetric indices have been studied for case finding of OSA, with sensitivities ranging from 40 to 100% and specificities ranging from 39 to 100%. In patients with OSA, we found that minSaO2 and mSa02 were lower, and CT80 higher, than in patients without OSA. Indeed, AHI was negatively correlated with minSa02 and mSa02, and positively correlated with CT80. Finally, mSa02 was a predictor of OSA according to logistic regression analysis. After determination of optimal thresholds by ROC curves, the oximetric criteria were the variables that had the best diagnostic values, as expressed by the area under the ROC curve (Table 3); however, this diagnostic value was not good enough to be useful as a screening technique (Fig 2). Read the rest of this entry »

Value of Clinical, Functional, and Oximetric Data for the Prediction of Obstructive Sleep Apnea in Obese Patients: Patients without OSAWe observed a lower diurnal Pa02 in patients with OSA than in patients without OSA. Because only a small proportion of OSA patients had an associated bronchial obstruction (6/40, 15%), this resting hypoxemia may be in part explained by high BMI. Indeed, several studies conducted in predominantly obese populations found values of Pa02 similar to those of our patients. Among them, Gold et al also found a higher PaC02 in sleep apnea patients than in control subjects, which was not the case in our study; this discrepancy is likely related to a higher proportion of overlap syndromes in their population, because their patients with OSA had lower FEV1 and FVC than patients without OSA, which we did not find. there
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Our data show that in an overweight population, CS, PFTs, and nocturnal oximetry taken alone may not accurately predict the presence or absence of OSA. As shown in Figures 1, 2, there is a considerable overlap between patients with and without OSA, even for variables that independently predict OSA according to logistic regression, and even when ROC curve analysis is used to determine the best thresholds for each of these variables. In 72.5% of the population, a complex logistic regression model would predict the presence or absence of OSA with a positive predictive value of 94% and a negative predictive value of 90%. Read the rest of this entry »

Value of Clinical, Functional, and Oximetric Data for the Prediction of Obstructive Sleep Apnea in Obese Patients: ResultsMultivariate logistic regression analysis showed that sex, CS, FEV1/FEV05, and mSa02 were independent predictors of OSA when computed as categorical variables (Table 4). There was no interaction between sex and other predictive variables. The relationships between AHI and these predictive variables are shown in Figures 1, 2.
The parameter estimates calculated by logistic regression when all variables were used in the analysis are shown in Table 5 and were used to calculate P, as described in the methods section. The calculated probability of having OSA (P) correlated to AHI (Spearman’s, r = 0.66; p < 10—11), and a value of P > 0.75 (n = 21, 20.5% of patients) had a positive predictive value of 90% for the diagnosis of OSA, whereas a value of P < 0.35 (n = 53, 52.0% of patients) had a negative predictive value of94% for the diagnosis of absence of OSA (Fig 3, left). Read the rest of this entry »

Finally, individual data were analyzed to determine if some ranges or combinations of ranges of variables were highly predictive of either the presence or the absence of OSA.
Results are expressed as mean ± SD unless indicated. We considered a p value < 0.05 to be significant. Logistic regression analysis was repeated with an AHI threshold of 10 for definition of OSA. Statistical analysis was performed with BMDP (BMDP Statistical Software; Los Angeles, CA), SPSS (SPSS Inc; Chicago, IL) and SAS (SAS Institute; Cary, NC) statistical software. ROC curve analysis was performed with ROC Analyzer software (RM Centor and J Keightley; Richmond, VA). Read the rest of this entry »

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