Chung-Wai Chow

Administrative Assistant: Joyce.Wu@uhn.ca

Research Key Words: Respiratory oscillometry, pulmonary function tests, lung transplant, interstitial lung disease, COVID

Research Methods: Biostatistics, machine learning, lung physiology 

Research Synopsis

My research program is focused on the respiratory oscillometry as a diagnostic tool for detection of lung disease. 

The conventional pulmonary function tests (PFT) such as spirometry is the current gold standard but is beset by problems of accessibility and low sensitivity for early detection of disease. 
These tests also require significant patient cooperation and physical maneuvers that patients with lung disease cannot do. Oscillometry has several advantages over standard PFTs as it can be performed by anyone who can breathe while wearing a nose clip. However, it is a new test and is not familiar to many physicians.

My research project aims to provide the needed data to facilitate implementation of oscillometry as a standard diagnostic tool. There are three distinct research themes.

We have multiple ongoing longitudinal studies in different patient populations where oscillometry is performed in patients with every clinically-indicated PFT such as spirometry. The clinical cohorts include patients following lung transplant, patients with underlying interstitial lung diseases, rare lung diseases, COVID, and obstructive lung diseases.

Theme 1: These research projects are focused on the role of oscillometry in identifying important clinical outcomes. These studies compare oscillometry to conventional PFTs to identify the metrics that will identify disease earlier and/or with higher sensitivity and specificity. Clinical data and imaging data are also avaiable. These projects are particularly suitable for students with a clinical interest who wish to develop advance biostatistics skills. 

Theme 2: These projects are focused on understanding the physiological basis of the oscillometry and standard PFT parameters. These projects are aimed at students with an interest in lung physiology and/or with an interest in data science and working with large dataset. Our growing database has amassed thousands of datapoints for investigation. 

Theme 3: Projects in this theme are focused on development of new technologies for measurement of lung function and new methods to analysis the data collected during oscillometry and standard PFTs, including the application of machine learning for interpretation and diagnosis. These are interdisciplinary projects with significant collaborations with biomedical engineers and computer scientists.