Evaluation of atopy through an expert system: description of the database

Age at asthma diagnosis and onset of symptoms among adults with allergic and non-allergic asthma

Background

Childhood-onset allergic asthma is the best-known phenotype of asthma. Adult-onset asthma, also an important entity, is instead often shown to be more non-allergic. There is still a lack of studies concerning the association of allergies and age at asthma onset from childhood to late adulthood. The aim of the study was to assess the age at onset of asthma symptoms and age at asthma diagnosis among adults with allergic and non-allergic asthma.

Methods

Questionnaires were sent to 2000 randomly selected Finnish adults aged 18-80 years who were dispensed medication for obstructive airway diseases during the previous year. The corrected sample size was 1978 subjects after exclusion of non-analysable data. The response rate was 40.6%. Self-reported doctor-diagnosed asthma was considered allergic if a concomitant self-reported doctor-diagnosed pollen and/or animal allergy was reported with asthma symptoms upon allergen exposure.

Results

Of the 496 participants with asthma, 42.7% were considered to have allergic asthma. The median ages at asthma diagnosis and onset of asthma symptoms were 31 (IQR 17-46) and 20 (9.25-40) years in participants with allergic asthma and 49 (37.75-58) and 40.5 (30-50) years in participants with non-allergic asthma (p < 0.001), respectively. Of the participants with asthma diagnosed at ≥30 years of age, 18% of allergic and 7% of non-allergic participants reported having had asthma symptoms under 20 years of age.

Conclusions

Both the onset of symptoms and diagnosis occurred at a younger age among adults with allergic asthma than among those with non-allergic asthma. Only a minority of adults with non-allergic asthma had already had symptoms in younghood.

Application of intelligent systems in asthma disease: designing a fuzzy rule-based system for evaluating level of asthma exacerbation

This paper discusses the capacities of artificial intelligence in the process of asthma diagnosing and asthma treatment. Developed intelligent systems for asthma disease have been classified in five categories including diagnosing, evaluating, management, communicative facilities, and prediction. Considering inputs, results, and methodologies of the systems show that by focusing on meticulous analysis of quality of life as an input variable and developing patient-based systems, under-diagnosing and asthma morbidity and mortality would decrease significantly. Regard to the importance of accurate evaluation in accurate prescription and expeditious treatment, the methodology of developing a fuzzy expert system for evaluating level of asthma exacerbation is presented in this paper too. The performance of this system has been tested in Asthma, Allergy, and Immunology Center of Iran using 25 asthmatic patients. Comparison between system's results and physicians' evaluations using Kappa coefficient (K) reinforces the value of K = 1. In addition this system assigns a degree in gradation (0-10) to every patient representing the slight differences between patients assigned to a specific category.

Phenotypes of asthma revisited upon the presence of atopy

Immunological studies claimed that atopic and non-atopic asthma share more similarities than differences. However, these two phenotypes of asthma are considered to be distinguishable upon distinct clinical patterns, which were not systematically assessed before in a large population. We studied characteristics discriminating atopic from non-atopic asthma among 751 asthmatic patients and 80 factors were analysed in univariate and multivariate analysis. Age, age of onset of asthma, female/male ratio were higher in non-atopic (n=200) than in atopic (n=551) asthmatics. Familial asthma, seasonal symptoms, rhinitis, conjunctivitis, allergen-triggered symptoms, improvement in altitude, exercise-induced asthma were associated with atopy. Non-atopic asthmatics displayed lower FEV(1) and FVC. Smoking was more frequent and asthma was more severe in these patients. Younger age, early onset, male sex, rhinitis and smoking were independent factors discriminating atopic from non-atopic asthma. This study establishes in a large population of asthmatics that although similarities exist between atopic and non-atopic asthma, two clinical phenotypes can still distinguish both kinds of asthma.

Development of an expert system knowledge base: a novel approach to promote guideline congruent asthma care

Existing guidelines for the clinical management of asthma provide a good framework for such tasks as diagnosing asthma, determining severity, and prescribing pharmacological treatment. Guidance is less explicit, however, about establishing a patient-provider partnership and overcoming barriers to asthma management by patients in a way that can be easily adopted in clinical practice. We report herein the first developmental phase of the "Stop Asthma" expert system. We describe the establishment of a knowledge base related to both the clinical management of asthma and the enhancement of patient and family self-management (including environmental management). The resultant knowledge base comprises 142 multilayered decision rules that describe clinical and behavioral management in three domains: 1) determination of asthma severity and control; 2) pharmacotherapy, including prescription of medicine for chronic maintenance, acute exacerbation, exercise pretreatment, and rhinitis relief; and 3) patient self-management, including the process of intervening to facilitate the patient's asthma medication management, environmental control, and well-visit scheduling. The knowledge base provides a systematic and accessible approach for intervening with family asthma-related behaviors.