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Asthma phenotyping and endotyping has emerged as a novel approach to asthma classification inspired by precision medicine which separates the clinical presentations of asthma, or asthma phenotypes, from their underlying causes, or asthma endotypes. The best-supported endotypic distinction is the type 2-high/type 2-low distinction.
Early-onset atopic asthma is the most common phenotype of asthma in childhood, called intrinsic asthma in the earlier intrinsic/extrinsic classification. Patients with early-onset atopic asthma frequently have a family history of atopy, and are sensitised to common allergens. This phenotype usually responds well to inhaled corticosteroids, and ...
Children affected by allergies in the developed world: [2] 1 in 13 have eczema; 1 in 8 have allergic rhinitis; 3-6% are affected by food allergy; Children in the United States under 18 years of age: [3] Percent with any allergy: 27.2%; Percent with seasonal allergy: 18.9%; Percent with eczema: 10.8%; Percent with food allergy: 5.8%
Asthma or cystic fibrosis. [1] Diagnostic method: Chest X-rays, CT scans, blood tests, immunological tests, and sputum cultures. [2] Differential diagnosis: Asthma with fungal sensitivity, cystic fibrosis, bronchiectasis, eosinophilic pneumonia, eosinophilic granulomatosis with polyangiitis, bronchocentric granulomatosis, tuberculosis, and ...
The fundamental problem in asthma appears to be immunological: young children in the early stages of asthma show signs of excessive inflammation in their airways. Epidemiological findings give clues as to the pathogenesis : the incidence of asthma seems to be increasing worldwide, and asthma is now very much more common in affluent countries.
Vasomotor rhinitis, for example, is one of many illnesses that share symptoms with allergic rhinitis, underscoring the need for professional differential diagnosis. [125] Once a diagnosis of asthma, rhinitis, anaphylaxis, or other allergic disease has been made, there are several methods for discovering the causative agent of that allergy.
Machine differential diagnosis is the use of computer software to partly or fully make a differential diagnosis. It may be regarded as an application of artificial intelligence. Alternatively, it may be seen as "augmented intelligence" if it meets the FDA criteria, namely that (1) it reveals the underlying data, (2) reveals the underlying logic ...
AERD affects an estimated 0.3–0.9% of the general population in the US, including around 7% of all asthmatics, about 14% of adults with severe asthma, and ~5-10% of patients with adult onset asthma. [2] [3] [8] AERD is uncommon among children, with around 6% of patients, predominantly female, reporting disease onset during childhood. [9]
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