The six-minute walk test (6MWT) is easily one of the most used evaluations in respiratory and cardiology clinical trials, most commonly in pulmonary hypertension (PAH), interstitial lung disease, pre-lung transplant evaluation or COPD.
The test, as the name would suggest, measures how far a patient is able to walk in six minutes. The current test monitors the patient’s resting heart rate and heart rate after the test. The investigator also measures the distance walked over the period, but it is suggested that these evaluations do not represent the full picture.
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As a result, Medidata is trialling a wearable sensor device which monitors heart rate and acceleration so investigators can evaluate more data from each patient than in the current test. Melissa Ceruolo, Medidata’s leader in technology development for medical device systems and innovator in data-driven health, adds that the sensor also allows patients to conduct the test at home to decentralise this evaluation which would also make it an attractive device to use in rare disease trials where the 6MWT is an endpoint.
Medidata is currently conducting research to evaluate the sensor in PAH and heart failure patients with Dr. Daniel Lachant, assistant professor in the department of medicine, pulmonary diseases and critical care at the University of Rochester Medical Center and Dr. Marvin Slepian, founder, and director of the Arizona Center for Accelerated Biomedical Innovation at the University of Arizona. The trial is evaluating how effective the sensor is in measuring this endpoint as well as the additional data it can collect.
If efficacious, given the number of trials which use the 6MWT as an endpoint, this could have a real impact on decentralising these trials.
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Abigail Beaney (AB): Tell me about how Medidata developed this new model for the 6MWT?
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By GlobalDataMelissa Ceruolo (MC): We are looking at how to monitor functional capacity and starting with the 6MWT made a lot of sense because it is widely used and people are so familiar with it and there’s a known limitation of repeatability of the test. We have been working with Dr Lachant, who during Covid-19 was trying to keep monitoring his patients, so he started in-home monitoring to avoid patients coming to the clinic. During this time, Dr Lachant expanded his novel biomarker called cardiac effort to the home setting and Medidata decided to continue his work and initiated a collaboration for a multi-year study in pulmonary hypertension and heart failure to expand biomarker development.
The starting point for this is being able to use sensors in the clinic during the 6MWT and get physiological data that you would typically not get in this test. This includes raw ECG data and accelerometer data. This data is giving us a great signal in the clinic, and now we are looking to move this assessment into the home by providing a kit to patients so that they can do their physical exertion test in the home. That’s really where we’re focused today — how to develop that right patient experience with the right data.
AB: How does using the sensor assist in showing more behind each patient’s response to the 6MWT than current methods?
MC: In the 6MWT, you can have these two patients who walk the same distance, but their physiology looks really different, with one having exerted much more effort than the other. The sensors detect two signals, the ECG signal for heartbeats, and the accelerometer. Those two signals are analysed together to provide granularity in functional capacity.
The ECG part of the sensor allows an investigator to monitor a patient’s stats while resting, as they start walking and after they have completed their walk, monitoring patients at every stage of the test to really evaluate the response. With the accelerometer, investigators can look not just at the distance moved but the quality of that movement. Examples of this would be that some patients may slow down as they turn or sway side to side which minimises their forward acceleration. They may pause or shuffle along which may not be picked up by clinicians. By bringing these two signals together, we can produce something informative, so we can really understand what’s going on inside the body.
AB: You mentioned the issue of lack of repeatability. What other issues are there with the standard 6MWT and how does the introduction of this sensor target these?
MC: For me, the biggest issue is the lack of information and specificity. If a patient goes outside and walks for six minutes, you can measure a variety of data. Typically, just the distance the patient covered during the test is being measured, as well as vitals before and after the test. We are going ahead a few steps and asking how the patient achieves that by looking at these two dimensions. In the traditional 6MWT, a patient is monitored before the walk and again afterwards. When you introduce your sensor, and you’re monitoring it in real-time, the investigator gets additional data points with information of how the patient is performing the test which you would not normally record.
AB: What does this look like from a patient’s perspective?
MC: We have spent a lot of time determining which devices are best appropriate for this application. This approach requires a really high-fidelity signal that has limited noise during emulation, so we are limited in what sensors we can use — for example we cannot use wristwatches. Currently, we are using VitalConnect’s Vital Patch in our study.
In the clinic, the investigator will provide patients with instructions on how to use the sensor before helping them apply it correctly. The patient will be verified using a phone signal to make sure the sensor is working before being asked to do a walk. The investigator will talk with the patient about the course they are going to use, making sure that it’s safe and making sure that a caregiver is present. The patient will then go home with their kit before setting up the course to complete the walk at home.
In the home, the patient wears the sensor and can go about their normal day-to-day activities. They use the sensor with an app which provides the patient with an explanation about the test. It also has a timer for patients to complete the walk with motivation at the time stamps in which an investigator would motivate the patient in the clinic. Once they have completed the walk, they are provided instructions of how to remove the sensor and package it back up.
AB: In clinical trials, standardisation of endpoints is essential. When conducting these kinds of tests at home, how can investigators ensure standardisation?
There are criteria for patients when selecting the course. It has to be flat; It can be inside or outside but it has to be a minimum of 30 feet long, using cones that are provided to determine the length of the course. Before the test, they do a walk from point A to point B once so that we know from the sensor data what the course length is before the test. The other criteria is that the first course must be the course used every time.
The variability of the courses between patients is not as critical as each patient doing the same course, each time. The other benefit is that patients wear the sensor for 48 hours so we can see the normal behaviour outside of the prescribed activity to assess the patient’s complete functional capacity.
AB: This sensor is being trialled in PAH and heart failure patients — what other indications could this be used in?
MC: The 6MWT is used in so many therapeutic areas. Medidata also has an initiative in central nervous system (CNS) diseases including multiple sclerosis, Parkinson’s disease and Huntington’s disease. We have 300,000 hours of sensor data and have been looking at verification testing and performance testing of this. We are also exploring and talking to our customers about building out new protocols and new development pipelines for other therapeutic areas that could really leverage the signals that we are getting with this 6MWT.
AB: What else does Medidata hope will come from using this sensor?
MC: We believe that with the addition of a chest sensor, we can get much richer data. This data could be helpful in recruitment and being able to identify patients who may better respond. It could predict outcome, so there is a lot of utility. We have had patients in the study that have not had great outcomes. We can look at the signals a month or two months before as predictive analytics. We are focusing on how we take this data and leverage it to bring the most value to the industry. Furthermore, we want people to see that and be able to use it and leverage everything that we’re doing.
