A day in the life: using pacemaker data to predict risk

05:30

I have a two-year-old son and one-year-old daughter who get me up early. Most mornings we share some porridge before I drop them at nursery.

08:15

My work day begins with a coffee at my desk at the University of Manchester. After two maternity leaves, and three months at the beginning of the pandemic when I paused my PhD to work on Covid hospital wards, I’m back to research.

Typically, the first part of my morning is spent on admin for the studies I’m working on, or training for my PhD.

10:00

I walk to the heart centre at Manchester Royal Infirmary, where I spend the rest of the morning recruiting people for a study called PATTErn. For this, we’re using data from 183 people over 60, with pacemakers or ICDs (implantable cardioverter defibrillators), to see if we can identify older patients at particularly high risk of being hospitalised. Most of the people in this study have cardiac resynchronisation therapy (CRT) pacemakers. These are used to treat some people with heart failure, to help their heart pump more efficiently.

We can get all the information we need in one go, while they have their regular pacemaker check. We download data from their device for the last year. For the last decade, these devices have been able to measure heart rate, rhythm, any fluid build-up in the lungs and physical activity. Previously this information was used to help the devices work better. Now doctors are using this data to monitor the health of the patient.

For PATTErn, I’m mostly focusing on physical activity. Pacemakers contain accelerometers that detect your body moving (the same kind of technology that’s inside smart phones or wristbands that shows you how far you’ve walked). We combine this data with questionnaires about their physical ability and do assessments of their grip strength and walking speed.

12:00

After a sandwich for lunch, I’ll start phoning patients who are part of our second study, TriageHF. It’s also looking at data from pacemakers, to see if we can use this information in real time as a remote alert system, so health professionals can act earlier to prevent patients getting really ill and being hospitalised.

Ten years ago, people had to come into hospital and have a receiver put onto their chest to get data from their cardiac device. Now they can be in their own home and if they’re within five metres (or yards) of the plug-in unit, data is streamed to us daily.

Patients can be in their own home and stream pacemaker data to us.

There’s a traffic light system on my computer: green, amber, red. Red means the patient is at high risk of being hospitalised, perhaps because they have a build-up of too much fluid in their lungs, or their heart has gone into an abnormal rhythm. Someone from the heart team phones to assess them. Sometimes we’ll change the medication they’re on, or call them in for tests, or liaise with their GPs.

The average heart failure patient might come in for checks every six months, but their condition can decline rapidly between appointments. In the past, it’s been so frustrating – when someone is hospitalised, you know they’ve probably already been declining for days or weeks. And you think, we could have turned things around if we’d known earlier. With remote monitoring of pacemakers, hopefully we can intervene before things get too serious.

14:00

I spend the rest of the afternoon doing data analysis. With PATTErn, I’m trying to find patterns in the physical activity data to separate out which groups of people are most at risk of being hospitalised. We might find it’s people whose physical activity is decreasing, or just that their activity is very low. When we know who’s at most risk, we can see who needs closer remote monitoring.

I am trying to find patterns in the data to find out who is most at risk.

There’s already been so much progress in cardiac devices over the last decade. With remote monitoring, patients could see even more improvement in managing their condition.

16:00

I leave work to pick up the kids. At their bedtime, my other half and I ‘divide and conquer’ – we’ll each take one child. Then, if I’m lucky, I’ll get to spend the evening on the sofa with a good book.

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