[Music]
Welcome back to the St. Emlyns podcast, I'm Ian Beardsell and I'm Simon Carley and this is the second of a series of podcasts we're going to be doing talking about a key skill in the emergency department, that of making diagnosis and how we decide when patients either have or haven't got disease. So Simon, could you just take a couple of minutes just to remind us where we got to after the first podcast and some of the things we talked about?
Sure, I think there are a few things that we talked about last time and the first was what is a diagnosis and the diagnosis we decided was the label.
It's just a tag that we put on somebody and we say if you've got this diagnosis it's a tag that says you've got that label, this is what you have, we're going to do something about it and if we don't give somebody the label, we don't give them the diagnosis, we're saying, well I don't think you've got this so we're not going to treat you or we're not going to do anything about it but it's really just a label that we attach to something.
We also talked about as emergency physicians that in terms of diagnosis, the one we're really interested in is the one that's going to kill you. So we almost work backwards, we almost work from a perspective of deciding what somebody doesn't have rather than what they do have. That's our initial approach to many conditions and that means that diagnostically speaking we're most interested in tests which have a high sensitivity so these are
tests which will definitely pick up anybody who might have the disease. That's our usual first approach. Once we've been through that then we start thinking about other tests which are specific that will tell us whether somebody definitely does have a disease because there are consequences to treatment for many of the things that we do. And deciding when that tipping point is about when we're going to go forward and treat somebody depends on how consequential the therapy is.
So many of our therapies have got risks such as thrombolysis and we want to be fairly sure about that whereas other things we're putting somebody in our wrists blend for an example, then the consequences are pretty minimal so we don't have to be as sure. If you put all of those things together it really does put us into a situation where we have to accept that when we say you have a diagnosis or you don't we're really talking about a probability.
When we say you have it means we think it's pretty likely that you do and it's worth treating and when we say you don't we mean it's pretty unlikely and therefore by not giving you that label you'll probably be okay but it does mean we're probabilisticians not diagnosticians and that's a bit of a surprise to many people. So let's take that onto the ED shop floor and talk a little bit about how we might be able to use some of that in a patient who's presented to the ED. Let's say with
cardiac sounding chest pain. So we've got that idea we need to get to the stage where we can either say with a degree of certainty that they haven't got the disease or that we want to rule in the disease and start treating them for whatever it may be. So ACS type treatment. How do we get from a patient who pictures up with cardiac sounding chest pain to that end stage that we've just been talking about. We need to go through some sort of diagnostic process. Chest pains are great
ones to think about how diagnostic tests really work in practice. So let's take that group of patients. So you've got this group of patients who've got myocardial, potentially myocardial disease or symptoms which are suggestive. Let's go for the big wins early. Let's go for something which is really specific which will rule in the diagnosis so a spin and that would be an ECG. Your ECG if it's
positive you've got big ST segment changes and you're going to go off to the lab. They've almost certainly got the disease. Well certainly 95% certain and it's worth treating that group of patients. So we'll go for a big win early specific test ECG. It's positive. Get rid of them. Move them onto other things. They're an easy group of patients to deal with because it's all protocolised medicine. Similarly those who've got pretty dodgy looking ECGs they can go the same way really. You're not
going to be sending those patients home. So we're going for those big wins early. We're then left with a group of patients who in my experience they've got normal ECGs or very nearly normal ECGs and the symptoms are in that dramatic. They've not recently had an MI. They've not got chest pain at rest but it's potentially possible and those are patients who we don't want to send home without doing anything. But we need to make sure that we pick up those who have got myocardial
disease. And there are about 10% of that group of actually had a myocardial damage event. So we need something which is sensitive and that's when we start thinking about sensitive tests which will pick up anybody who might have the condition. So just to reiterate that then the ECG is a relative specific test because there's few false positives. So if we see an abnormality on an ECG it's likely that the patient may well have disease. It's still important to relate it to what the
patient looks like. We see some young patients with what might be described as high takeoff and I think having an idea of their age and a few other bits and pieces help us. But it's a quick quick way of ruling patients in because it's a specific test. So we've got those ones sorted. Then we want to now work on saying who hasn't got the disease. We must have an idea of prevalence I guess in our population and in this terms we can really equate prevalence to pre-test probability.
Is that can we use those terms relatively interchangeably at the start? Absolutely. I think it's sometimes confusing when people talk about them as if they're very different things. Prevalence in that population now is good and that's going to be our pre-test probability if we're going to do some testing. And for that group of patients, normally CGs, no particular things in the history, they're not in heart failure, no major features on examination. It's about 10%
of them have actually got underlying myocardial damage. So it's still quite high. So 10% is our pre-test probability. It's obviously not low enough to say they haven't got the disease because we've been missing quite a lot of patients, but it's also not high enough to start giving them the treatment because the treatment could have harm. So we need to take those patients now and move them further down that diagnostic pathway. We should really start with the history,
shouldn't we? So let's think how we can use some of those things we've just talked about but equate them to a patient in the ED. So I think we see lots of patients with chest pain and lots of patients with cardiac sounding chest pain. So why do we try and relate a little bit of what we've learned
so far into a real patient scenario? So a patient turns up with cardiac sounding chest pain. How do we get to the stage where we can either say you haven't got the disease, you're safe to go home, or you have got the disease, we think it's more probable that you have, we're going to start treatment on you. What's the best way to go about that? Well, great example. Chest pain is a beautiful one if we want to think about how diagnostic tests work in clinical practice. So you've got that
group of patients you said and they're cardiac sounding chest pain patients. Yeah, so these are the patients that we describe as having a cardiac sounding chest pain. So we want to get them through to be sure they haven't got an ACS type problem. Yeah, so we're thinking about things like my cardio in function and aortic dissection, that kind of thing and that's great. I think everybody will understand what we're talking about. So let's let's think about how we do our diagnostic test.
Let's go for some easy wins early. Let's use something which is really specific and which is pretty good and can rule in. So a spin test. Let's use an ECG. An ECG is quite a good test because it's specific. If you've got big ST segment changes there, that's good. If you've got lots of ST segment depression, then that's also good. It identifies a high risk group of patients who are either going to be definitely admitted and given an antibiotic medication or are going to go off
to the lab get PCI or thrombolysis. So use of an early specific test is fantastic. Move them out, dead easy to deal with. By specific, we're meaning there's few false positives. So it's more likely if we see these abnormalities that they're true. They're true positives and the patient has the problem. So we can use that test. It's still important, I guess, to have a bit of an idea about the patient
because there will be other things that we know about that will cause some of those changes. So having an eyeball of the patient from the end of the bed and having an idea of their age perhaps might give us a bit of a hint. But generally that's going to sort out one set of patients off they go, decision made, you're staying in, you're having your treatment. Okay. How do we go about the rest of
the patients who we've got with cardiac chest pain? Who aren't so obvious? Yeah, well, I think you're going to be left then with about, still about 50% of the patients are going to have a pretty normal looking ECG symptoms compatible with mycardal disease but no major examination findings and nothing particularly alarming in the history like the fact that they had a mycardal infarction last week. In that group of patients, you've still got about 10% of them are going to have underlying
mycardal disease if you look hard enough for it, which is still pretty hard. So 10%, this is now what we're describing as our prevalence or we could maybe even call that pre-test probability, that we can use those terms interchangeably, that's okay. Yeah, I think so. I think when we're talking about pre-test probability, it's quite good that we're talking about the specific group of patients who we've now filtered down to, whereas prevalence people might say, well, it's the prevalence of
people who turn up in your ED. But pre-test probability, I think that's really essential for an emergency physician to understand that's our group because the test performance is going to vary depending on your pre-test probability. And so this is the probability that that patient has disease in the group that we're talking about. So now we've got to a group who have a 10% probability of having a bad outcome or a disease that we want to diagnose. Now, we've said already that's not enough to say,
we're going to really not treat you anymore. We want to be less than 2% really for that. But because of the nature of the disease and the nature of the treatment, which can have harm, it's also not high enough to start doing treatment for those patients. So we need to start moving that 10% either up towards a level where we're happy to treat or down to a level where we're happy to say it isn't an ACS type picture. And how do we go about that? We've got to start with history, haven't we?
Well, to some extent, we've done some of that already. So we've got ourselves to the point where on history and examination, we're about 10%. We've done as much as we probably can. We've now got to move on and think about laboratory tests. And in our practice, we use components, I don't know what you use in cell. We're the same with troponin. We're about to move to a high sensitivity troponin. All right, OK. So 21st century stuff from
others. And I would say that because in Manchester, in Manchester, we've been doing high-sensitive troponin for a number of years. But that's because of the wonderful Ric Body, of course. Yeah, we're going to use some diagnostic tests. And what we're trying to do with that, what are we trying to do with the diagnosis is exactly as you describe. What we're trying to do is to say that the probability after we've done the test is either so low we can let you go home
or it's so high that we're going to come up. So we're trying to move the probability and we do that by a function of diagnostic tests, which you would call a likelihood ratio. So a likelihood ratio which is positive, so you get a positive result, it makes it more likely that you've then got the diagnosis. And it moves that probability higher to a certain degree? Yeah. And if it's a negative result, it moves the probability lower. And it's a function of both the sensitivity and
the specificity. And the likelihood ratio, the more positive it is, the more it will move you up in that direction. In effect, the better the test is for diagnosing. And if it's a negative likelihood ratio, it moves you more down towards that rule out criteria. So this is again, all about how good that test is at the diagnosis you're looking for. Yeah, and absolutely, you can have the same test that's got a really good positive ratio, like a ratio, and a really
rubbish negative one. Or you can have one which has got a really rubbish negative one and a really great positive one. So it does really depend on exactly how you're using a test. And trapezoid is a good one, high sense of trapezoid is a good one, because it's very good at ruling out, because it's super sensitive, it's not particularly good at ruling in, because it's not very specific.
And so that affects the function of the lose likelihood ratio. So if we have our patient with the 10% pre-test probability, and we've got to that via a combination of history and other things, although I think it's quite hard for physicians to really know what a pre-test probability is. But let's say it's about that 10% level, and we do the high sensitivity trapezoid in, in whichever
guys you're going to do that, how many tests you're going to do. That could well be enough to take us low enough to take us below that 2% threshold and say, we're happy to stop now. Yeah, that's if you're using high sensitivity ponials just as a yes no type thing. So if it's either above or below this level, and that is still a function, but that's still a little bit similar just using a sensitivity specificity, isn't it? It's still a bit like a yes no type question.
I guess the sensitivity in specificity all go to make up the likelihood ratio. So I guess the interlinked heart rate inexplicably sort of linked? Yeah, they are, but take that one step further. I take that one step further and say, well, okay, say our level of trapezoid in the ruling out would be 14. Okay. Nanograms, okay, that's fine. Why is it 14? It's because we've chosen that level because at that level it's particularly good
for ruling out. What if we change that level? What if we made it at 30? Well, it wouldn't be as good at ruling out, but would that be better at ruling in? And the answer is it would. So depending on where you put your level on a test like troponin, a continuous variable will affect how good that test is at performing. So you could even have the same test, but with a different value it would be great at ruling in, at a lower value, it'd be great at ruling out. Now we don't do that very often as
diagnosed issues, but that is the function of many, many tests that we use. It actually works for things like amylase. It actually works for things like white cell counting appendicitis. Many things which a lot of people would say that test doesn't work. Well, it does, I know, are very high levels or at very low levels in the middle ground. And I guess tests like
the troponin have been extensively investigated. And so we get somebody who's looked at that a long time to set that level for us, but as I'm going to call myself without hesitation a box standard emergency physician, I guess I have to develop my own level with the white cell count or those other tests you talked about to say where I'm happy about, oh, do you know what, a white cell count
of 18 in this context? I don't think that's important. So it's more difficult with those other less binary tests if you like about how we're going to use them at the end, those continuous variables. Yeah, but I think you do it all the time already. I put it to you that if somebody comes in and they've got a troponin of 6000, although most of the time we're told to use, or most people using high-sensitive troponins as as rollouts, somebody comes through with a troponin of 6000, I think that's
a bit of a rule in, isn't it? I guess it is. I guess it is. So we've got our patient with the pre-test probability of 10%, and we're going to do a troponin, a high-sensitive, a troponin on them. Now, it's a bit like the D-dimer, isn't it? It's a sensitive test. We're just looking for a negative test because it's going to take us, we're just looking for a sensitive test because it's going to take us
to that level where we can rule it out, same as with the D-dimer. What happens with that if we actually don't get a negative high-sensitive opponent? So it comes back positive. We're no better off, are we? Well, as I say, it depends on the level. If it comes back very, very high, you can use these tests because they continue as variables to roll in a diagnosis, but a lot of the time it'll come back sort of slightly raised, in which case it's not particularly helpful. D-dimer is perhaps a better
example. A D-dimer, which is negative, so below the investigation level, you can use that to roll out in low probability patients. If it comes back as positive, it just means you're going to have to go on and do further testing. That might be further serial biochemical testing. To look for eyes and falls, we do a troponins, quick interlude here. We don't do serial testing for D-dimer, of course. We do VQ scans or further imaging to define whether or not the patient has a disease. Or it might be
an alternative test entirely, such as a VQ scan or a CTPA for PE. So all the time, we're taking a pre-test probability, applying a test and getting a post-test probability. Until that post-test probability is satisfactory to roll in or roll out, we keep going. We keep going, and that may be as part of the history. So we said at the very beginning, we start off with that group of patients where we apply the test. The test is the ECG. That has a good positive likelihood ratio. If it shows
changes, it's likely the patient has disease. That takes us our post-test probability high enough to roll in. But in this case, we've got to the stage where troponin, that pre-test probability is 10%, we've done a test, it's not changed enough. We haven't got to the threshold of rolling in or rolling out. We need another test. And that's how we think all the time, I guess. We do that with everything we see, whether that's the patient who pitches up with a headache. We operate a diagnostic
test that might be asking some questions about the headache. And each question is in itself a diagnostic test. Each time striving to get to the stage where we can say, "Do you know what? I'm satisfied that in all likelihood you don't have that illness. I can move on and do something else with you." Yeah, you're quite right. Every little note, every little point where you're making a decision or asking a question is a diagnostic test in itself. And that will change your pre-test probability
to a post-test probability. So Simon, I think we've taken everyone a bit further down that line of working out whether a patient has disease or doesn't have a disease. Using diagnostic testing to take our pre-test probabilities through to post-test probabilities and just adding a little bit of sensitivity, specifically likelihood ratios into the mix to try and give that mathematical bent to think about these probabilities. That's probably enough for certainly my brain today,
maybe not for yours, but it might be for listeners as well. Why don't we come back again in our next episode to take this further and work out what it means to decide to give a patient treatment and how we decide if that treatment is going to be effective and whether even that treatment might harm as well as do good. So please think about what we've been talking about. We'd love to hear from you getting touched via all of those methods. You could Twitter via the website. We'd love to hear
anything you've got to say. And we'll look forward to speaking to you very soon. Take care. Hey, and just for you. Yeah, last time I did ask you what your favourite test was. You were very fond of your old Sam machine, if you're in a family. I like technology. What's your least favourite test? You see, you always just do this. I think we're about to finish and I'm all just chilled out. I'm about to pop it to the fridge, get myself a beer, pack myself
on the back and you just land me with a question. And so I'm going to answer for you today. Professor Simon Carly, I'm going to say the White Cell count. Oh, what's that? I don't know. I just think everyone puts a load of emphasis on the White Count as being important and almost never can I think of a time where it changes my pre-test probability significantly enough about whether I'm going to treat a patient or send them home.
Because I've already made the decision based on everything else. And apart from the occasional patient I'm happened to pick up via a screening type test, do I have some awful leukemia, which has happened about once in my career? I can never think of a time it's been helpful, apart from convincing a surgeon that a patient might be poorly. Yeah, I think I said, do you know what my favourite is? I most certainly do.
Yeah, it's CRP. Yeah, CRP is always that one that emergency physicians like to beat up. And we manage to stop people doing CRPs, I think, which is great. So, no one needs to do now, I've stopped them doing White Cell counts and both of us can be happy. Yeah, my Mrs. of Owl though. Anyway, see you another time. Take care. You needed to say the word vowel a bit clearer. Yeah. [Music] [Music]