Novel Technologies for Detection and Monitoring of Lung Disease - Project Summary/ Abstract Obstructive lung diseases such as chronic obstructive pulmonary disease (COPD) and bronchial asthma affect approximately 42 million individuals in the United States alone. About 70% of those with spirometry-defined obstruction remain undiagnosed. These individuals without a diagnosis suffer from a high symptom burden, frequent exacerbations, and high healthcare utilization. Current methods of diagnosing abnormal lung function have not majorly changed since the 1940s. The diagnosis of airflow obstruction is made using the ratio of the forced expiratory volume in the first second (FEV1) to the forced vital capacity (FVC). Determining an abnormal ratio requires adjustment for population demographics that need frequent updates, so they are representative. Spirometry with forced exhalation is also time-consuming and difficult for many patients to perform; it requires expert coaching and multiple efforts to be repeatable. The course of disease is punctuated by acute worsening, termed exacerbations. The detection of exacerbations currently relies on subjective patient self-report, which can delay diagnosis. There is, therefore, an unmet need for better diagnostic and monitoring. Spiromatics Inc. is addressing this unmet need through the development of a portable, handheld, miniature, spirometer equipped with audiovisual coaching and innovative solutions for assessing and diagnosing abnormal lung function. We have developed novel methods of detecting airflow limitation with which we are able to detect an additional 11% individuals who would remain undiagnosed using traditional criteria. In addition, we have developed an easy to use completely innovative way of measuring lung function that takes only two minutes and is extremely patient friendly. This easier-to-use method of detecting airflow obstruction has several immediate practical applications. It can enhance the use of spirometry methods in primary care clinics which often do not perform spirometry due to its complexity; the method can be used for monitoring lung disease at home; and the method can be used to detect exacerbations or flare-ups of disease earlier than symptomatically reported by patients. The goal of this proposal is to develop a portable handheld customized miniature spirometer with embedded firmware to facilitate deployment of our proprietary algorithms. We propose two specific aims. In Aim 1, we will design and build a customized prototype portable spirometer that meets industry standards. In Aim 2, we will design and implement firmware enabling efficient data analyses for accurate diagnosis of lung disease, and test these algorithms in human subjects. The expected outcomes of this Phase I study include a data- and memory-efficient, spirometer with advanced machine learning capabilities that can be used both in primary care offices for diagnosis and at patients’ homes for disease monitoring. The successful accomplishment of these goals will set the stage for population-based studies in Phase II for diagnosis, monitoring disease activity, and assessing response to treatment.
