Rapid Diagnostic Algorithm for Faster Diagnosis of Acute Coronary Syndromes

I'm Rick Body. I'm a Professor of Emergency Medicine at the University of Manchester, and I work as a consultant in the emergency department at Manchester University, NHS Foundation Trust. When we're treating patients with suspected acute coronary syndromes here at Manchester Royal Infirmary, we see Troponin-Only Manchester Acute Coronary Syndrome Decision Aid or T-MACS, which was developed through our research here.

This uses high-sensitivity cardiac troponin concentrations measured when patients first arrive in the emergency department in combination with some of the details of patient's symptoms, their ECG interpretation, and it uses that information to calculate the probability that patients have an acute coronary syndrome based on that probability. We then stratify patients into different risk groups and decide what to do about further investigation and management.

At Manchester Royal Infirmary we've been using the T-MACS algorithm since June 2016. And we're currently in the process of rolling out this algorithm at 12 hospitals across Greater Manchester. The first thing that we had to do when we implemented the T-MACS algorithm was to get our informatics system up to scratch. We needed a software application that would allow us to calculate the probability of the acute coronary syndromes using our T-MACS algorithm.

Having done that, we then had to develop the environment. So previously, before we had our early rule-out strategy, we had to admit patients to inpatient beds. It meant they often stayed in hospital longer than they needed to, and it kind of over medicalized the situation. With our Ambulatory Care Unit, we began to care for patients with suspected acute coronary syndromes in reclining chairs.

They never hit a hospital bed. It's a very comfortable environment, we could do their serial troponin sampling. And almost always when those tests come back clear, the patients could go home very quickly and efficiently. Before we had this rapid diagnostic algorithm, we could see from the data that our patients were generally staying in hospital for on average more than two days.

That's despite the fact that only a small minority of those patients actually had an acute coronary syndrome. So, with better diagnostic algorithms, we really had great potential to reduce unnecessary hospitalization for our patients. Since we implemented T-MACS we can see that the length of stay has reduced particularly for low-risk patients where they almost invariably will go home on the same day, just a number of hours after they've arrived in the emergency department, which is very efficient and far better for patients.

At the moment, emergency departments are facing some really important challenges. The problem of crowding is getting worse year-on-year. And we need to make sure that we only keep patients in hospital when they really need to be there. We have a huge challenge with patients who have suspected acute coronary syndrome because we know the symptoms can be very atypical, very unusual.

We have to have a high index of suspicion for that diagnosis, which means the patients must undergo investigations. The problem is that with contemporary cardiac troponin assays, those investigations have to take place over a number of hours. And that means that patients are staying in crowded hospitals and emergency departments for much longer than they really need to, contributing to this problem of crowding.

With high-sensitivity troponin assays, we can achieve two things. So the assays have better precision than contemporary troponin assays, which means that if we were to retest the same sample several times the results will be closer together. And also the assays have better analytical sensitivity which means we can detect lower concentrations of cardiac troponin.

Those two benefits of the high-sensitivity cardiac troponin assays really unleash some great potential for us in the emergency department. The other benefit I mentioned is about the precision. So we know that we have much tighter results when we repeat the same test on the same sample. That has benefits because when patients do need two tests, we can reduce the time interval between those tests because we know that the result is not likely to vary very much, just as a result of the imprecision of the test.

So, a small change is much more likely to be genuine and that's why we can reduce the time interval, again unleashing benefits for our crowded emergency departments and allowing us to make faster decisions which is good for everybody.