Smarter prescription of azithromycin using machine learning, with Prof. Wim Janssens

About this episode

This episode discusses the use of machine learning to improve the prescription of azithromycin for COPD patients.

Prophylactic use of azithromycin is common in the management of COPD patients with frequent acute exacerbations. But its application is conservative due to concerns around adverse events, interactions with other drugs, and bacterial resistance. Given these concerns, it is important to identify patients who are likely to be responders or non-responders. According to the NICE guidance in the UK, you should consider azithromycin if the patient: Doesn't smoke. Has already accounted for non-drug treatments, inhaled therapies and vaccines. And has one or more of the following kinds of exacerbations: Frequent exacerbations, more than four times a year, with high sputum production Prolonged exacerbations with high sputum production. Exacerbations causing hospitalisation. In this episode, Thorax social media editor Dr. Kate Diomede speaks to Prof. Wim Janssens¹, author of a study proposing five parameters to predict the individual treatment effect of azithromycin. These parameters were developed using a machine learning model trained using the MACRO and COLUMBUS datasets. This led to the creation of an online calculator, serving as an aid in decision making. Read the paper: Identifying…

People in this episode

Host: Dr. Kate Diomede

Guest: Prof. Wim Janssens

Topics covered

• azithromycin
• COPD
• machine learning
• predictive modeling
• healthcare decision making

Keywords

• azithromycin
• COPD
• machine learning
• exacerbations
• predictive parameters

Mentioned in this episode

Organizations: NICE, KU Leuven

Books & works: Identifying azithromycin responders with an individual treatment effect model in COPD

Places: Leuven, Belgium

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