Asthma is one of the most common chronic conditions in the United States. Asthma affects about one in fifteen people. It affects children more than adults and blacks more than whites. People with asthma experience attacks of wheezing, breathlessness, chest tightness, and coughing. Asthma can be fatal and the costs for the disease (direct and indirect) are approximated to be tens of billions of dollars each year.
There is no cure for asthma. However; for most people if asthma is controlled well they can lead normal, active lives. Therefore asthma controllability is a main factor in clinical practice. In order to control asthma, the disease has to be completely understood. Asthma is very heterogeneous and this makes the exact diagnosis and control procedures difficult. To better evaluate and study asthma, mathematical tools can be very beneficial.
In this study we first develop a complete system for lung impedance analysis of laboratory models of asthma. Our designed system is capable of precisely diagnosing the diseased models and predicting the severity of their condition. We also evaluate the treatment progress in mouse models of asthma. We then study an asthma database of humans including measurements of four related laboratory parameters and cluster patients based on inherent properties of the study variables. This mathematical approach clustered patients with specific characteristics and segregated the unstable asthmatic patients in a single group. Our method is very promising in predicting the instability of asthma, which is highly correlated with frequent asthma attacks and increased utilization of care
