Spirometry is an important component of the National Asthma Education and Prevention Program guidelines for asthma, yet published data show variable associations between forced expiratory volume in 1 second percentage (FEV1%) predicted, symptoms and health care utilization. The objective of this analysis was to examine the association between FEV1% and future risk of exacerbations among a well-characterized population of children with asthma.
Using data that are available from the Childhood Asthma Management Program, we examined the relationship between prebronchodilator FEV1% and important clinical outcomes. Multiple observations of FEV1 were available for each patient; multivariate regression analysis, using a general estimating equation approach, was used to control for the correlation between repeated measurements among individuals and potential confounders. FEV1% was categorized into 4 levels and as a continuous variable. Outcomes of interest included mean symptom score (0-3), episode-free days, and asthma-related events (oral steroid use, emergency department visits, and hospitalizations) during the ensuing 4-month period. Our analysis was limited to the placebo group (N = 417).
We observed a clear relationship between prebronchodilator FEV1% and important clinical outcomes. In multivariable models that simultaneously controlled for covariates of interest, age at baseline, time, previous event history, and nocturnal awakenings, a significant relationship between FEV1% and asthma symptoms and serious asthma exacerbations (oral steroids, emergency department visits, and hospitalizations) was observed. Compared with children with an FEV1% > or = 100%, children with FEV1% 80% to 99%, 60% to 79%, and < 60% were 1.3, 1.8, and 4.8, respectively, more likely to have a serious asthma exacerbation during the ensuing 4 months. CONCLUSIONS. In children with mild to moderate asthma, FEV1% predicted is independently associated with future asthma symptoms and health care utilization. Previous asthma-related hospitalizations and nocturnal symptoms also were independently associated with risk for future adverse events. FEV1 is an important component of asthma health status and asthma severity classification.
"Based on data published in the Fuhlbrigge study, if a child had a hospitalization due to asthma in the previous 12 months , their probability of having a serious asthma event increased (Table 3, ). We calculated this multiplicative factor following the same process described above, with the resulting polynomial equation: "
[Show abstract][Hide abstract] ABSTRACT: Background
In the United States, asthma is the most common chronic disease of childhood across all socioeconomic classes and is the most frequent cause of hospitalization among children. Asthma exacerbations have been associated with exposure to residential indoor environmental stressors such as allergens and air pollutants as well as numerous additional factors. Simulation modeling is a valuable tool that can be used to evaluate interventions for complex multifactorial diseases such as asthma but in spite of its flexibility and applicability, modeling applications in either environmental exposures or asthma have been limited to date.
We designed a discrete event simulation model to study the effect of environmental factors on asthma exacerbations in school-age children living in low-income multi-family housing. Model outcomes include asthma symptoms, medication use, hospitalizations, and emergency room visits. Environmental factors were linked to percent predicted forced expiratory volume in 1 second (FEV1%), which in turn was linked to risk equations for each outcome. Exposures affecting FEV1% included indoor and outdoor sources of NO2 and PM2.5, cockroach allergen, and dampness as a proxy for mold.
Model design parameters and equations are described in detail. We evaluated the model by simulating 50,000 children over 10 years and showed that pollutant concentrations and health outcome rates are comparable to values reported in the literature. In an application example, we simulated what would happen if the kitchen and bathroom exhaust fans were improved for the entire cohort, and showed reductions in pollutant concentrations and healthcare utilization rates.
We describe the design and evaluation of a discrete event simulation model of pediatric asthma for children living in low-income multi-family housing. Our model simulates the effect of environmental factors (combustion pollutants and allergens), medication compliance, seasonality, and medical history on asthma outcomes (symptom-days, medication use, hospitalizations, and emergency room visits). The model can be used to evaluate building interventions and green building construction practices on pollutant concentrations, energy savings, and asthma healthcare utilization costs, and demonstrates the value of a simulation approach for studying complex diseases such as asthma.
Environmental Health 09/2012; 11(1):66. DOI:10.1186/1476-069X-11-66 · 3.37 Impact Factor
"The ability to predict severe asthma exacerbations would therefore have direct prognostic significance and might form the basis for the development of novel therapeutic interventions. Severe asthma exacerbations have been associated with several clinical factors including the forced expiratory volume in one second as a percent of predicted (FEV1%), oral corticosteroid usage [9,19], age , and sex . However, these factors by themselves are limited in their ability to successfully predict severe asthma exacerbations [21,22]. "
[Show abstract][Hide abstract] ABSTRACT: Personalized health-care promises tailored health-care solutions to individual patients based on their genetic background and/or environmental exposure history. To date, disease prediction has been based on a few environmental factors and/or single nucleotide polymorphisms (SNPs), while complex diseases are usually affected by many genetic and environmental factors with each factor contributing a small portion to the outcome. We hypothesized that the use of random forests classifiers to select SNPs would result in an improved predictive model of asthma exacerbations. We tested this hypothesis in a population of childhood asthmatics.
In this study, using emergency room visits or hospitalizations as the definition of a severe asthma exacerbation, we first identified a list of top Genome Wide Association Study (GWAS) SNPs ranked by Random Forests (RF) importance score for the CAMP (Childhood Asthma Management Program) population of 127 exacerbation cases and 290 non-exacerbation controls. We predict severe asthma exacerbations using the top 10 to 320 SNPs together with age, sex, pre-bronchodilator FEV1 percentage predicted, and treatment group.
Testing in an independent set of the CAMP population shows that severe asthma exacerbations can be predicted with an Area Under the Curve (AUC)=0.66 with 160-320 SNPs in comparison to an AUC score of 0.57 with 10 SNPs. Using the clinical traits alone yielded AUC score of 0.54, suggesting the phenotype is affected by genetic as well as environmental factors.
Our study shows that a random forests algorithm can effectively extract and use the information contained in a small number of samples. Random forests, and other machine learning tools, can be used with GWAS studies to integrate large numbers of predictors simultaneously.
BMC Medical Genetics 06/2011; 12(1):90. DOI:10.1186/1471-2350-12-90 · 2.08 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In 1989, the National Asthma Education and Prevention Program (NAEPP) convened an expert panel to develop a report that would provide a general approach to the treatment of asthma. Expert Panel Report: Guidelines for the Diagnosis and Management of Asthma, or EPR-1, was published in 1991 and was subsequently updated with 2 other reports, EPR-2 in 1997 and the EPR update in 2002. Advances in science and a greater understanding of the pathophysiology of asthma prompted the NAEPP to convene a 3rd expert panel in 2004. After nearly 3 years of work, Expert Panel Report 3: Guidelines for the Diagnosis and Management of Asthma-Full Report 2007, or EPR-3, was released on August 29, 2007. EPR-3 update from the NAEPP provides health care professionals with new information to improve the care of patients with asthma, including (1) more comprehensive discussion of asthma severity with expanded descriptions of impairment and risk, (2) increased focus on asthma control as a goal of therapy, and (3) expanded discussion of pharmacologic therapy for asthma with updated treatment algorithms.
To (1) extract key educational messages from the EPR-3 update that effectively summarize the appropriate management of the patient with asthma and (2) provide supporting literature to substantiate the development of these educational messages.
A consensus meeting of 9 asthma experts (4 pharmacists and 5 physicians) was held to discuss the EPR-3 update and condense its content into a usable format for the health care professional. Experts were selected on the basis of several criteria, including (1) affiliation with the NAEPP, (2) expertise in asthma management, and (3) familiarity with managed care processes. The author served as the 10th member and moderator of the meeting.
Thorough review of the EPR-3 update resulted in the development of 7 key educational messages that can assist the health care professional in improving the management of the patient with asthma. Each educational message is presented with supporting literature to substantiate its distinction as a key point to be referenced when developing protocols for asthma management within managed care organizations.
The complexity of asthma and its treatment has necessitated the development of several guidelines from the NAEPP, with the most recent EPR-3 update being released in late August 2007. One expert consensus has distilled the EPR-3 document into 7 key educational messages that can assist the health care professional in improving the care of the patient with asthma.
Journal of managed care pharmacy: JMCP 01/2008; 14(1):41-9. · 2.71 Impact Factor
Giselle S Mosnaim, Andrea A Pappalardo, Scott E Resnick, Christopher D Codispoti, Sindhura Bandi, Lisa Nackers, Rabia N Malik, Vimala Vijayaraghavan, Elizabeth B Lynch, Lynda H Powell,
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