A Device to Accurately and Efficiently Detect Asthma Attacks in the Emergency Room – Entrepreneurial Track

Asthma is a chronic condition, and it is characterized by inflammation of the airways and over-production of mucus. This makes breathing difficult, and it can produce shortness of breath, wheezing, and coughing. According to the Center for Disease Control and Prevention (CDC), more than 25 million Americans have asthma; which is 1 in every 13 people. Asthma is the leading chronic disease in children, and many times, it can be deadly. Each day, ten Americans die from asthma, and most of these deaths can be prevented with the proper treatment and care. In hospitals, the main way to detect an asthma attack is to look for the symptoms, such as listening to the lungs for wheezing, and looking at oxygen saturation levels. However, asthma is not a disease of the alveoli; rather, it is the inflammation of the airways. Therefore, many patients present almost perfect oxygen saturation levels while having an asthma attack, misleading the hospital staff to believe that the patient is stable. This occurs until the airways close to a point where the oxygen saturation levels plummet, and the patient has a smaller chance of survival. Once an attack is severe and breathing is completely obstructed, an asthmatic has about six minutes to receive help or the consequences can be brain damage, due to oxygen deprivation, or even death. Therefore, there is a need to develop a device that better detects asthma attacks in the ER.

The purpose of this project is to create a device that can better detect an asthma attack in the Emergency Room and can determine the severity of the attack. Pulse oxymeters are not efficient or accurate indicators of an attack. Other devices, such as lung function tests, can be effective but are very expensive, take 15 to 20 minutes, and are mostly provided by pulmonologists, not by staff in the ER. The device should: 1) quantify lung volume, airway inflammation, production of nitric oxide, or other indicators of an attack, 2) show the severity and progress of an attack ranging from mild to life threatening, 3) be easy to use by all staff in the ER and require minimal training, 4) be compact, accurate, cost-efficient, and fast.

Project Mentor:

Engineering Mentor: Christine King, PhD, Department of Biomedical Engineering, UC Irvine, kingce@uci.edu

Team: