Pleasure to come back to Oxford always.
Although I'm a cardiologist, I do, and in many ways allowed myself more with primary care and community care.
So. And I think I'll tell you a bit later on, I've got strong links with your evidence based medicine department, head of centre.
So I've got some conflicts to tell you. The biggest conflict is you should know that I spend my time, do clinical work.
I'm not a full time clinician and neither I full time could have controlled my time teaching, researching,
and I've been at advisory boards for these companies, but I also have research funding from these other funders.
I'm a trustee of the South Asian Health Foundation and my interests quite varied,
so ranging from digital health and its evaluation to where informatics sits in medical education and evidence based medicine.
So this evening I'm going to take up half an hour of your time and do three things.
First of all, I'm going to tell you about three patients that I'm going to tell you about three problems which those patients highlight.
And then three frameworks. We're focusing on this.
So heart failure is what I'm specialised to and treat and diagnose to look at in my work, my cardiologist.
And heart failure is an unusual disease in that it's a it's a syndrome that is really at the endpoint of quite a lot of processes,
from high blood pressure to congenital heart disease,
from heart attacks to lots of rarer conditions like cobalt and high levels of cobalt can be associated with heart failure, for example.
So so there's a real ragbag of stuff that can be linked with heart failure.
It can eventually end up being seen by me. And the idea is we want to pick you up at that.
And as in any disease, you want to be pre disease or normal if you can to stop the disease from happening.
And there's an asymptomatic phase where people might not be presenting breathlessness, which is the most common thing that happens.
But most of my work is when people say, I can't walk to the door, don't.
I can't go up a flight of stairs. They're breathless. And there's a variable course, which at worst can be can be fatal,
and it can lead to lots of different treatments, the mainstay of which is drug therapy.
But there's, you know, a spectrum on which heart transplant, heart transplantation is there as well.
But what I'm here to talk to you about is big data.
This term that means everything and nothing.
Now at the moment, I think we're up to seven bees.
When I was studying here doing my PhD, our first heard about big data and I think it was only previous at that time.
So every time you had to be, you can get a paper and remain in the light.
So now we're up to velocity, volume, veracity, variety, variability, visualisation, value.
Um, but the, the theme really is that there's more of the data coming at a quicker rate and can we do something more with it.
So this is my topic to talk about whether we can in heart failure.
And in this this kind of jargon, bingo of big data epidemiology, these are the terms that you will come across.
So precision medicine, the idea that we can make more precise diagnoses and more precise courses of treatment,
personalise the treatment to the individual, which is slightly different to the to the previous one and new or old data analytics.
There are definitely a community.
There are some analytics that aren't new, but a lot of what is being done with big data is by statistics and epidemiology.
This is not new, but the main theme is if you look across that spectrum, I show you for heart failure.
What we're trying to do as researchers or clinicians is trying to predict the risk of being at that point,
whether it's developing heart failure, whether it's developing the outcome of heart failure.
And the fact of the matter is, most of the risk scores that I have at my fingertips as a clinician are not good enough.
When people ask me questions, I can't answer them accurately enough with the risk scores that I have at my disposal.
And, you know, there's ways which are going to later of how we assess the accuracy of these risk goals.
But really, there is there is a tried and tested method of how we evaluate risk prediction,
how we evaluate risk scores in the same way as we evaluate new treatments.
And at the moment, there's many, many of them in heart failure, which are not working very well.
So my first patient, Imran, was 43 years old when I met him and he had a relatively rare condition and amyloidosis,
which is an incurable condition, you could say it's an it affects lots of different organs.
But his in his case, it was affecting his heart very badly as previously.
Well, a man who used to be a very vigorous swimmer and he had fluid around his heart,
which meant that he was he couldn't walk for me, that wasn't really affecting the other organs of the stage.
He was from the south coast and he was transferred to us at Barts.
He had you don't need to know all those, but he had a host of scans and various evaluations and pre transplant.
Because what happens is you have these these tests and investigations to see whether you'd be you survive a transplant,
how we can prioritise you for transplant. But also this underlying condition,
amyloidosis has a treatment regime which affects the immune system that was that was stunted because actually in this period,
while he was being investigated, his heart got worse. So I'll come back to.
But Dejection fraction is the term we use for how much of your heart output is pumped out with each minute it should be 55 60%.
This gentleman it was 5%. So within six, seven days of seeing me, he got listed for urgent transplant.
And actually within three or four days of being listed, he had a transplant.
So he was pretty damn lucky because.
In the UK, you'll see in the Premier League of Europe of heart transplantation.
We're not doing that well in terms of the rate of transplant per per million population.
And that's that's been like that for several years. And moreover, if you look at different organs,
so this is the the rate of transplantation of different organs in people have been certified as brainstem dead.
The proportion of organs that are actually available and eligible to be used that actually get used.
So at the top there, there's kidneys and livers, which is 80 plus percent.
But heart transplantation, we're using less than a fifth of those organs.
And that's just to give you a flavour that the pool of people who can get an organ is small.
There's problems with the organs, the pipeline to get the organs in the first place.
And so Emerald did incredibly well to get an organ.
And not much has been changing this old data from when I was a registrar in Birmingham,
which is a transplant, a big transplant unit, but it's still about 250 to 300 transplants per year.
That's not growing. So the thing is, we've got to get really good at picking the horses to back who you can give the heart to.
And so there's a waiting list on that side and there's post-op care and survival on this side.
And we do our best to predict who's going to survive on the waiting list, because it might be up to a year or 18 months on the waiting list.
And we want to back the people who are going to survive as long as they can.
And there's various factors, patient level, clinical factors.
System level at each stage.
And we have various risk scores that are either developed and derived from the US databases mostly, but also some European ones as well,
that are generally used in all of the research, but they use quite a few small number of features.
They don't personalise those risk factors to the individual and they're not very good.
So this is some work that we did with some colleagues at the Alan Turing Institute to to look at how we could do better using machine learning,
whether we could to to predict how people do after heart transplantation.
Do we have data like that in the UK? The answer is that number of transplants per year over the last ten years of registries,
it's not a big dataset and it's not well curated, so you're lucky if you can get three or 4000 people in that dataset.
So we looked at the US where there's publicly available data, you just send an email and get access to the data.
It's wonderful and that's whatever organ transplantation that you want to look at.
So this is called open access.
We looked at those those between those dates and say there were 60,000 patients who had transplants.
There were 36,000 people who were on the waiting list.
And the amount of people who who were followed.
So those scores that I listed and really just just focus on the bold these are all
the risk scores that I listed at the top of those top three in the bottom three.
And the numbers never get much above 0.6 for pre transplant survival.
And when you get to post-transplant survival, you're in crossing a coin territory.
You're 0.50.6 area under the curve for these scores, predicting survival even at three months.
And I'm trying to see whether this person can live for ten years.
So what is machine learning? I'm sure you will know this.
It's a branch of artificial intelligence.
And in the cardiology literature, there's been a ballooning of the use of the term artificial intelligence.
And it's yeah, there's lots of people joking about whether the intelligence of the people writing about people is actually artificial or not.
And so it does borrow from models of of statistics because there's various algorithms that are off the shelf.
And there's also people who are trying to marry up different algorithms to see if they do better.
But Mihaela Bhandarkar, who's our computer scientist and this in this piece of work,
what she was trying to do was to cluster features together and personalise the risk prediction in a way that traditional risk scores look at
population characteristics to see whether the regression could be done in clusters of individual characteristics to try and personalise the pitch.
And she calls this model trees of predictors. So in your kind of Phoebe format, this was my peak.
We're looking at, you know, databases will look at this new algorithm. Does it do better than off the shelf?
Machine learning tools and regression predicting survival of the hot competition.
Those schools that I showed you earlier, they all have a few component features.
One of them has seven things. They're 13, 14, and they range from ejection fraction, which I mentioned earlier, to things like age or blood pressure.
And there's this idea that permeates the machine learning literature that bigger is better,
that more is better, the more features they have is better.
So we used everything that we could in this database, 15 features, and I won't go into it because we compute so much more about this.
But in short, the algorithm breaks down on the basis of various features at each level of
this tree and sees whether with a limited number of features at each level,
you can predict, rather than using the same algorithm, the same risk prediction tool and everybody.
And what we found was that in the red box that all of the numbers got better.
The ones post-traumatic transplant survival less.
So there's still less than point seven. But at the top, particularly in the short term, they were good.
But at ten years, you're in the 76 territory, but not that much better than existing machine learning.
But significantly better. For what it's worth.
But definitely better than. So we improved with survival.
This got a bit of coverage but the press and because this idea of personalising risk prediction it was precision medicine with risk
prediction and it and it also shows that you can improve risk prediction over various time Verizon's but it's very far from perfect.
We need to do things prospectively that's looking in the rear-view mirror.
We need to do a forward looking study with real patients going forward, and we need to validate in different populations,
possibly in a trial setting, and check whether patients and clinicians actually want to use this.
So that's something that we're doing at the moment.
We've got an online tool that transplant cardiologists can use with their patients, and we're gathering data on that.
So that was the first problem. Risk prediction at the end stage of heart failure.
This is Richard, the next patient, 61 years old.
We had a heart attack eight years ago and has had heart failure since his ejection fraction.
This which is kind of moderately fast and he is breathless, going up two flights of stairs.
He's very avidly looking up stuff on the Internet.
He's one of those people who's bringing sheets of paper to the consultations and this risk calculator that said it use machine learning.
It says he's got less than 20% chance of survival in next ten years.
So his question is, when we see his grandson graduate,
which is 15 years now and know I don't know anything about his grandson's academic ability, I'm just interested in debiting.
And so is this machine learning any good in heart failure showed off the top of the title.
So this is a large study from Sweden.
So the Scandinavians who write and analyse data better than us, and they do it for the whole the whole of the country.
And in Sweden, they looked at nearly 50,000 heart failure patients.
And they were looking at whether you could predict mortality using traditional methods and using machine learning.
And in short, what we're taught at medical school and in cardiology training, this thing ejection fraction, how well your heart pumps.
That is the best indicator of whether you're going to survive or how long you're going to survive.
In actual fact, that was really tossing a coin that wasn't very good at all predicting in a national data set in Sweden.
But they're using using over 40 variables in machine learning.
They managed to increase the C statistic, but this was, again, a retrospective study.
There's lots going on in the literature about machine learning.
I'm only going to tell you about in the cardiovascular space, but these are three diseases heart failure,
acute coronary syndromes, atrial fibrillation, common, and they often overlap.
And we're constantly thinking that we can do better at the definition, better at the diagnosis, and therefore better at prevention.
And so machine learning is has been used a lot.
There's lots of papers being written about it, but nothing is being used in actual clinical practice.
So we did a systematic review. The problem here is I don't know how to tell this patient how good is this machine learning
algorithm or how good is the literature about machine learning for risk prediction?
So we've done this.
We've looked in the last 18 years or so and we've looked at any study that's looking at risk prediction, machine learning, clustering.
And we've also done a scathing review of non cardiovascular disease to see where heart disease is compared to other diseases.
After filtering through, we found about 70 articles.
So I should give credit to Nick Cheng, who's a postdoc in data science, and my group has been doing most of this work and clustering.
For those of you who may not know is looking at whether the data actually clusters,
whether if you if you took 50,000 heart failure patients, do they naturally settle into different clusters.
And there are three papers that we found that are looking at clustering.
They're all the mean is about 2000 patients.
Some are very small. The largest was that's a Swedish study that I just showed you.
Most of them are in heart failure, some in the other diseases, most of them are single diseases.
And almost two thirds of them are in North America and less than 1000 individuals.
And people talk about machine learning using lots and lots of variables, but actually it's about 26 at the moment in the UK.
There's a variety of methods used, but only three or four are commonly used.
Most do not use several methods like we used. They used one by machine learning methods, and clustering is uniformly positive.
Nobody's ever written a paper that says we didn't find any clusters. If they found two plus two and four plus two equals three positive.
So it's always positive and they don't always validate their findings in the risk prediction literature.
So this is machine learning to improve the heart transplantation risk prediction, for example,
there are a few studies, there's some which are very small, again, mostly North America.
People are using many more covariance, over 100.
They're using more sophisticated machine learning approaches maybe.
But again, all of them are showing that machine learning improves risk prediction.
So what can we say? We've got a focus on North America.
We've got some very small studies. We've also got a lack of validation and we've got a probable publication bias.
And in our scoping review, it looks like whether you're looking at clustering in rheumatoid or clustering in chronic lung disease,
the same problems are happening in their literature as well. My third patient, Francesca, 57.
She has had hypertension for donkey's years. She has recently been picked up with diabetes.
She has a friend who has recently got restless and has been diagnosed with heart failure.
What's my chance of having failed in her question? Because she's had hypertension for ages 15 years plus, and she's she's diabetic and she's 67.
So primary prevention in heart failure is really difficult.
There's no consensus guideline about primary prevention in heart failure.
There's a primary prevention of cardiovascular disease,
which mentions a couple of lines about heart failure and other than stopping smoking and a couple of drugs and treating blood pressure.
But we haven't made much advance in primary prevention of stopping it happening in the first place.
So our European Society of Cardiology has a table drawn by a group, a group of August, predominantly men in the U.S.,
who sit around a table and decide that these are the 8 to 9 factors that are ecologically associated with heart failure.
So they have a level one, which is the yellow disease myocardium.
Abnormal loading conditions. Level two, level three, level four.
And so you've got everything there, as I said, early, from hypertension to cobalt, from pregnancy to amyloidosis.
So lots of causes that 89 in all. So what we thought is there's been no study that's ever looked at all of these causes in a systematic way.
People have said, let's look at heart failure caused by May or not.
Well, let's look at blood pressure related, heart failure or not.
They've developed all of these together, and neither will they be able to say.
One of the things that can be done is an electronic health record.
You might be able to look at the incident heart failure patients, 170,000 of them in CPT,
which is a primary care database that I'm sure most of you have seen.
It's linked between primary and secondary care and whether we can look at all of these risk factors.
So we we tell you that each one of those 89 causes or etiologic factors in the electronic health record, and we did that in 170,000 patients.
We as I say, we were using CPI linked with health data, hospital episode statistics.
So read codes from the GP, ICD codes in the hospital statistics and that that's the number of codes.
So this is a it's a reasonable piece of work to just get to the stage where you can make these codes mean something.
And then each of those levels, level one, level two.
As I said, we've looked at when somebody in the last five years before their heart failure had a code that matched.
And so, for example, the communist causes disease, myocardium,
ischaemic heart disease is probably the commonest reason for having heart failure or having an association with heart failure.
And each of those levels we've looked it's hard to do this in traditional data set without bigger data, bigger data about causes.
Right. And what you do is you end up in a situation where you can present all of the causes ever in the in the life history of heart failure.
And see, for example, that you've got hypertension here.
You've got coronary disease, obesity, cancer, diabetes.
So it's mostly the things that we think. But there's also some rarer things that we you know, this is this is pacemaker related.
Issues that are related to heart failure. And then we can look at whether these are age and sex adjusted.
We can look at whether the causal factor is related to the prognosis.
This is at five years, but you can look at ten years.
And people who don't have heart failure but don't have any recording of any of those risk factors have much better survival.
Unsurprising, you might say, but you can you can look as a clinician who might be focusing on,
how am I going to tell this lady what is her risk of developing heart failure?
Maybe this might push us further along. The problem is, as I told, you know, of relatively few treatments for primary prevention of heart failure.
What we've got data for is treating heart attacks, treating blood pressure.
And also there's a group of drugs that have just come off the pipeline called T two inhibitors,
which are for treating diabetes, which also prevent heart failure to some extent.
So if you look at the proportion of people who have those three conditions,
it's actually quite large among people who have not one, two, three, four, five risk factors out of those 89.
Hopefully, there's potentially a big chunk of people, even why we've got three treatable conditions who might be treatable for better treatment.
So we think this is the first study that looked across all causal factors heart failure in a population based nature,
the communist risk factor disease. But they're mostly things that we expected adding cancer, obesity and anaemia and individual and population level.
We probably should be looking at better treatment of hypertension, diabetes.
That would be wrong of me to come to Oxford and not talk about evidence based medicine and to talk about those that get some famous definition.
And I always go back to this patient values, relevant scientific evidence, clinical judgement.
And I also always remember this story of the blind men and the elephant, which you're well aware of.
But it's you know, there's lots of countries that claim it.
My ancestors in India say that it comes from the Chinese.
They come from there. But the story is the same.
The three blind men are introduced to different parts of the elephant's anatomy and have different ideas about what they're actually discovering here.
And the truth is, we're still in evidence based medicine, not at a place where people are looking at where those circles are overlapping.
Most people are neglecting the patient values and preferences, but there's probably much more of this going on.
And that's that's definitely in my experience of evidence based practice been been an issue.
And that's why, you know, these these are patient based scenarios which are genuine from my from my practice.
And most of the literature is not designed to answer that question.
Excuse me. This is a new fangled lexicon from the big decks of era learning health system.
It's always three things. This is science, evidence and care.
And this came from the Institute of Medicine report in the US in 2006,
where the big emphasis was on wastage that most of the research doesn't reach guidelines in evidence.
Most of the evidence doesn't get ready for clinicians like me. It doesn't get applied by health systems.
And so the care is suboptimal and it leads to poor outcomes and probably patient safety issues and maybe death.
So if you can have data circulating around,
then the system as a whole can learn and science has to be more informed by data coming back from the clinical space.
So this is a model that's not replacing at all evidence based health care, but just thinking about it and phrasing it in a slightly different way.
Maybe that reflects. But again, patients in the middle.
But again, there's a problem in the data science space, which is where I am at today, where we're looking at different parts of the ocean.
At the moment, you've got the informatics people who are pushing the science and what we do with
electronic health records and the latest newfangled machine learning methods.
You've got people who are interested in doing guidelines and you've got a different
agenda of how we use it in the health care setting and actually provide care.
The bigger issue still that I think is that there are these three different paradigms,
these three different frameworks which often people seem to think are competing.
So, you know, are you an evidence based person or your informatics person?
Are you a quality improvement person? Or are you an evidence based person in your big day for personal?
Your focus here, you know, is at the end of the day, it should be about are we trying to do this to make things better for patients?
Full stop. And adding to that big data machine learning, hey, I'm serving medicine, so on.
This is one that, again, one of my colleagues, Rob Aldridge, in my department, is writing a lot about that.
Public health is also not to be left behind in the big data era.
It's not just the people who are looking at genes and proteins.
It's also the public health. People are going to marry with the computer scientists and the epidemiologists via statistics
to make sure we use the data better across disciplines to understand and prevent disease.
So to do that, the public population level.
And the difference that I want to put to you now is that we are moving to an era where, you know,
having one person or a set of investigators, having a cohort of people with heart failure, that's going to get developed quite soon.
We should be using our routine data much better at the individual level, for the patient level.
In general practice, we've been far ahead in this country and hospitals are slowly catching up.
But partly where we've gone wrong is that elephant issue.
But even in the hospital, we have to been thinking about how to make the data actually improve things for patients.
But people are thinking about this much more at scale and and doing it across clinical and research space.
But. The issue is that the data is still problem.
So the people who are likely to use new technologies or have new technologies,
a new computer science and machine learning or whatever used upon them are the people who are
socioeconomically better off than the people in higher income countries and the data divide.
So we know this slide. So this is genomic studies to date, but 2016, it's not that much better today in 2019.
In 2009, 4% of the people in genomic studies worldwide with non-European.
19% non-European in 2000. 64/5 of the population of the world is non-European.
So should I be excited about what precision medicine is going to do for people when the data doesn't represent the population data divide?
Who owns the data? Is it publicly owned? Is it commercially?
What's the quality of the data? And what are the standards in its collection and its use?
So so before you get to making heart failure treatment better for patients,
there's this backdrop where we need to before we say the machine learning is going to tell you more about heart failure.
It's the same problem as standard statistics and epidemiology is is the is the population representative of the patient sitting in front of you.
Is there external validity research? So I'm coming towards the end.
There's no framework. There's no data. There's no digital tech that is bigger than the patient that's learning at the lower end.
And even now, people are evangelical about different parts of the elephant.
We must remember the patient. And that's where the exciting opportunities are in big data, in heart failure.
There are gaps I've actually shown you in both machine learning and in my own work and in the way we're looking at causes and risk prediction.
We need to be scientific and we need to talk to each other and leave you with a quote from Yuval Harari.
So we read from his every day and he he's big on data science, actually.
He's he's saying that this the century, the last train ever take station called Homo sapien is leaving.
Those who missed the train will never get a second chance in order to get a seat on it.
You need to understand 21st century technology and in particular, the powers of biotechnology and computer algorithms.
And he does actually talk about how there's this data divide and how everybody
needs to understand how the algorithms and the science that's being pushed.
We need to all make sure that there is religious legitimacy in that from that data.
And so I commend that book to you if you haven't read it.
And I will stop that quickly.