Q&A with Dr. Grandjean Lapierre

Could you share a bit about your background and what sparked your interest in cough measurement?

I'm a physician specializing in infectious diseases and medical microbiology. I split my research between cough analysis and using DNA sequencing to study tuberculosis transmission. My interest in cough measurement began during my postdoc work in Madagascar, focusing on tuberculosis transmission. We saw widespread coughing in remote communities with limited access to care and wondered if technology could identify households at higher risk for active TB. Initially, the idea was to use syndromic surveillance of cough sounds to target high-risk households. Privacy and ethical concerns prevented this project, but it evolved into various collaborations with Hyfe. 
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What is your current research about? What is the aim of the trial?

The bigger trial is called "Making Cough Count for Tuberculosis." It has two main components:

1. Building a global database of cough sounds: This database will allow scientists to develop and test cough triage or diagnostic algorithms. Over 2,000 cough sounds from patients in seven countries have already been collected and made available as an open-source dataset. A subset of the data is kept blinded for assessing the performance of future cough analysis models. Hyfe has supported us in collecting and curating the data as well as making it available.
2. Longitudinal cough monitoring: A subset of participants from the database were enrolled in longer studies (2 weeks to 6 months) where their cough was monitored after seeking care or being diagnosed with TB. This data is used to investigate whether cough trends can indicate treatment success, treatment failure, or relapse.

The goal is to develop cough as a biomarker that can be used not only for diagnosing diseases but also for supporting clinical management throughout the course of the disease.

Who will benefit the most from the results of this research?

Primarily scientists and clinicians working in high TB-burden areas with limited healthcare resources. The cough data can help in triaging patients and improving clinical management by identifying those who need more urgent care. The real value is that it’s highly accessible and virtually cost-neutral because once the model is developed it can be made freely accessible. 

What about longitudinal cough monitoring and the clinical significance of the data it provides?

We use several vital signs and different markers on a longitudinal basis to care for many diseases. In the end, cough could become one of these metrics—something clinicians use to ensure their patient is fine, the treatment is working, and there is no exacerbation. The challenge in transitioning to clinical use is to increase our understanding of what meaningful clinical signals are in cough trends, because it’s a lot of data. We have good tools, we can generate reliable data, and we see value in long-term clinical management, but we need to find the meaningful and usable signals in that dataset so the clinical team knows what to do with the information.

Do you think wearables such as smartwatches could address some of the shortcomings of phones when monitoring cough, thereby impacting the quality of data?

Wearables could be a better option in some situations. They might be more comfortable to wear continuously compared to phones, which some users might be hesitant to keep with them at all times due to security reasons. Wearables offer practical advantages and can fill gaps in data collection, making monitoring more consistent. However, it's about having various tools for different contexts rather than one superior device.

Do you think cough monitoring should develop beyond tracking frequency, and if so, what else could we measure?

There are two components here: improving the quality of data and enhancing the prediction potential of the model. To improve data quality, we could use an accelerometer to ensure the patient is wearing the device. This would help us determine if the absence of data is due to the absence of cough or the device not being used.

Additionally, what other biomarkers could we include in the models? The field of remote patient monitoring is rapidly evolving, with metrics like temperature, breathing rate, and step count being used. We haven't made significant progress in combining these with cough data. This is an area we’d like to explore, but I don’t have data to confirm that cough should be combined with specific metrics for particular clinical applications. There’s certainly room to grow in this area.

How about cough-specific variables? Do you think tracking them along with frequency is beneficial?

The true answer is, I don’t know. As clinicians, we ask patients questions like, “Is your cough productive? Is it increasing or decreasing?” but we don’t have a differential way of analyzing these answers in clinical practice. When we use these data points as patient-reported, they don’t lead to a stratified approach to managing patients. Even if we could get this information more reliably, I’m not sure a clinician would make a different call based on it.

For longitudinal monitoring, such as in chronic conditions like COPD, having data on whether a patient’s cough is becoming more productive could indicate an exacerbation requiring early intervention. In these cases, measuring these additional data points objectively and longitudinally might help us act earlier and achieve better patient outcomes.

Looking ahead, how do you envision the development of cough monitoring?

I see cough monitoring reaching a crossroad. Are we going to keep pushing and improving the technology to get more reliable data without addressing the other component: what we do with the data? If we provide healthcare workers with comprehensive, 24/7 cough time series for a year, but it leads to no action, it’s not changing patient management. I'd like to see more evidence of actionable information from the cough signal that can have a clinical impact. We need to start measuring impact in terms of how it benefits the patient, rather than just improving the quality of the data we generate.

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