I told her. But today I thank you and thank you, Emily and Margaret, for the invitation.
Can everybody hear me? Great. So, yeah, really glad to be here.
Really interesting how big data has taken off since I started do my stay over ten years ago now.
So I'd like to spend the next 45 minutes just talking to you about what I've learned over that time about heart failure in primary care,
in the context of those big data studies that we've done as part of our group.
So as Emily said, I'm a GP, so I'm a GP in the Midlands.
In a suburban practice we look after about 10,000 patients in our parts.
But I'm also a researcher as well at the University of Oxford.
So just around the corner in the Nuffield Department of Primary Care Health Sciences,
I'm also a member of the nice Chronic Heart Failure Guideline Committee and the Quality Standards Committee.
So that guideline was updated in 2018 and the quality standard is being updated at the moment.
So that gives me a slight angle on policy as well as my research and clinical work.
And hopefully I'll bring that all together in the talk to show how big data can be very powerful for us to improve patient care.
So my research focuses on big data as well as other methodologies.
As Emily mentioned, I think it's important to put that into context.
So big data, what do we mean by that? Well, when I'm in surgery as a GP, I'll be inputting clinical codes about my patient, about diagnosis.
I'll put treatments are prescribed treatments through the electronic medical record.
If a patient has a test that will appear in their electronic medical record.
So there's lots of data that we input as part of routine clinical care.
And as researchers, we're very fortunate to be able to access this data in large primary care datasets.
So things like the clinical practice research, datalink key research or Kids, the Health Improvement Network,
these are all examples of large primary care datasets that we can use for research purposes.
Our research group has predominantly over the years, and the three examples I give today are all from CPR.
But as a GP I see patients, I see real people.
So the research databases are anonymized. We don't know who these people are.
It's at a population level. We see the numbers.
But when I'm in practice, in a clinic in in the town where I live, I see patients, all types of patients.
So we serve people from young babies through to very elderly and people reaching the end of their lives.
So we provide holistic care to the community which we serve.
And as part of that, we see patients with heart failure,
people with newly diagnosed heart failure, people that don't know they've got heart failure yet,
people that have been living with heart failure for years and people that are dying of heart failure.
So that gives me a real insight into what it's like being diagnosed and living with the condition, and that really helps to inform my research.
So let's talk a bit about heart failure. So just the basics.
Heart failure occurs when the heart is struggling to pump enough blood around the body to meet the metabolic needs of the organs.
And that manifests for the patients in fluid building up in the lungs, causing them to feel really breathless and often increasingly breathless.
Over time, they can also get really swollen legs and they can get fatigue, almost exhaustion, because their organs aren't being perfused properly.
And there the symptoms that patients get, they can have one of those symptoms, they can have all of those symptoms.
But there's around a million people in the UK living with heart failure at the moment and there'll be around 200,000
people diagnosed each year with a new diagnosis of the condition it accounts for a large proportion of NHS expenditure.
So 3 to 4% of the total budget goes on heart failure and that's largely due to hospital costs.
So A&E attendances heart failure cuts around 5% of emergency hospital admissions.
Patients tend to have a longer stay if they've got heart failure compared to other conditions, and they're frequently readmitted to hospital.
And we find that many patients get their first diagnosis of heart failure on an emergency admission.
So they may have had a gradual onset of symptoms up to that point.
But many patients actually get admitted through a prior to their first diagnosis.
And that's something I'm particularly interested in and I'll talk more about in the in the lecture.
And these patients do have a poor prognosis.
So the outlook for people with heart failure is similar to the outlook for people with with common types of cancer.
But it's not all doom and gloom. It's a very treatable and increasingly treatable condition.
We have a large number of drugs which we know can improve quality of life for patients,
control their symptoms, prevent them from going into hospital and improve their survival.
And this new drugs just come on the market, which are really improving outlook for these patients.
So getting a diagnosis of heart failure is absolutely key to accessing these evidence based treatments,
which we know can improve both quantity and quality of life.
So that's the burden of heart failure for the population in the health care system.
What about the patient and their family? Well,
I did some qualitative work a couple of years ago interviewing people with a new diagnosis and ask them just to describe their diagnostic journey.
And they found the term heart failure incredibly frightening. It sounds frightening, doesn't it?
Heart failure. A lot of people felt it was a sort of imminent demise and this one patient just really hadn't
realised what his symptoms meant or recognised that there was something seriously wrong.
He said, I just thought it was probably my age.
I don't think I thought it was the heart to start with. No, I just thought it was because I was walking a long distance and needed to stop.
So we often find this in the qualitative research we've done.
These patients normalise their symptoms and don't necessarily recognise heart
failure in a way they might recognise other conditions where awareness is better.
So during the talk I'd like to give three examples from our work.
So I should say I work with a fantastic group in the department.
So I co-lead the heart failure research team with Professor Richard Hobbs,
and we've worked together for 15 years looking at the primary care aspects of heart failure in different ways.
And I'd just like to give three examples where we use big data to answer important clinical questions and show how it can translate
from these millions of numbers that you're seeing your spreadsheet to actually answering important questions for patients.
And the first one I'd like to start with this survival. So survival is tricky.
When somebody is diagnosed with a condition, it can be challenging to know what they want to know, what they don't want to know.
But it's important that we have answers to questions patients might ask us.
And as a heart failure group, we have a fantastic peer group, so patient and public involvement.
They're a group of people that have got experience of heart failure and they give us
honest answers when we ask them about the types of research we're thinking about doing.
And then we're involved in throughout the research process through to dissemination of our findings at the end.
And it's really fascinating, actually, our patients felt that they did want to have discussions around survival.
They felt the discussions they had had in their own experience were often inadequate.
One said, I like doctors to have the facts, and I felt that they just hadn't.
And also this idea of this frightening term and imminent demise needed to be addressed by the clinician rather than ignored.
So we decided to do a study called Survive H.S.
To answer these specific type of questions.
So a patient might ask me as a GP if I tell them they've got heart failure or the specialist has told them and they come back to me,
So what is that and what does that mean? So how long will I survive?
What will I die from? Or is outlook for people like me improving?
Now, not every patient will want to know the answers to these questions, but some people might.
And as a GP sitting in front of that patient, I want to know the answers.
And you might think, Well, don't we know this already?
There's a lot of work in cancer epidemiology where these these answers are very well known, but not in heart failure so much.
So a lot of the data on survival comes from large screening studies, often from the US, which is a different population to the UK or from hospitals,
patients and people in hospital with heart failure are quite different to people living in the community with heart failure.
So we felt we were in a good place to answer this question using big data and this was the end result.
So this was a paper published in the BMJ on Valentine's Day in 2019, which seemed very appropriate and timely.
I think it was just coincidence,
but we looked at trends in survival after a diagnosis of heart failure in the United Kingdom and over a long time period.
So from the millennium, up until the most recent data when we started the study in 2017.
So this was a population based cohort study using the clinical practice research datalink which you'll, you'll have heard of already on the module.
So a large anonymized primary care dataset and we used a long time period.
So we went from the 1st of January 2000 up until the end of December 2017, and we were doing the data in 2018.
So we went all the way to the end of the previous year.
The data completeness. One of the values of big data is that we can link to other datasets.
So we linked to hospital episode statistics.
So that gives us an idea of what happens to these patients in hospital when they go in, what they go in for, how long they're there.
And we also had linkages to the Office for National Statistics Mortality Registry.
So that tells us when patients have died and also what they've died of.
And that information comes from the death certificate completed by the doctor who is last involved with their care.
So these are quite powerful combinations because we can really get an insight into what's happening with these patients.
So in survive. Tess We had 395 practices records for over 2 million people,
so no cohort study or screening study where you actively recruit patients who would achieve anywhere like this number of people.
You just wouldn't have the funding to do it. We chose people over the age of 45 because they're people that are likely to
develop heart failure and those who get heart failure when they're younger,
it tends to be a congenital cause. And that's a different condition with with different treatment options.
So within this 2.4 million people, we found maybe 56,000 people with a new diagnosis of heart failure.
So that's the new diagnostic code within their primary care records.
Average age of diagnosis was 77, which is similar to what you find in other epidemiologic studies in heart failure.
And we looked with the linkage with hospital less episodes statistics what
proportion of people had been admitted to hospital around the time of diagnosis?
And we found it was around 43% in our cohort, although some studies have reported that to be even higher.
So we did survival analysis looking at age, sex and practice, match controls.
So we had the people with heart failure diagnostic code and then we matched them by age,
sex and practice to people that just didn't have a code that just to see the difference in survival and they saw a kaplan-meier curve.
So the blue line is those without heart failure in their records and the dotted yellow line is those with heart failure.
And as you can see, there's a dramatic difference. So X-axis, this time y axis is survival probability,
and there's a real reduction in survival in those with heart failure compared to their peers who don't have heart failure.
And just looking at that risk table, I think that's also the power of big data is just the sheer numbers.
So each time point to five years, at ten years, even at 15 years, we've got lots of people in the cohort to look at and compare.
So we can really see what's happening within that population and what the the trend looks like.
And we also dig a little bit deeper. So if we want to answer the question, what is is the survival rate for people at one year, five, ten years?
We can calculate that from the survival analysis, but we can also look at it by gender, but also by age.
And as you can see at a younger age, your chance of surviving a year at a 90% if you're between 45 and 54.
But as you go down in the older age groups, when you get to 75 to 84, it's it's 76.5%.
So if you're talking to the patient in front of you, it's helpful not only to have an overall idea of survival rates in the general population,
but it's also important to put it into context of the patient.
Demographics and particularly age is very influential on survival, as you would expect, and I think that's important for us to report in our papers.
When the BMJ paper came out, there was a lot of fear, I think, because overall the survival did look pretty bad.
But actually if you look at younger patients,
the survival wasn't as bad as the overall cohort because there are plenty of treatments available to improve prognosis.
So we also looked at cause of death and we had over 30,000 deaths within the cohort.
So a large proportion of the cohort died during the follow up.
And this allowed us to look at what the causes were.
So patients with a diagnosis of heart failure, what do they actually die from?
Do they die from heart failure or do they die of something else?
And as you can see on the so on a death certificate, you will put the primary cause of death.
So the main thing that has led to death in that person and then you would put secondary
causes which are sort of contributing factors but not the main thing they died from.
So heart failure was the main cause in 7.2%, but it was actually influential in around half of patients.
So that's important to know. But also that means 50% of people with heart failure actually died of something else.
And that's important as a GP to bear in mind because these patients are usually living with
several other long term conditions and we don't want to just focus on the heart failure,
we want to focus on managing all of those conditions to give them the best quality of life and the best survival chances.
So we also looked at the top three causes.
So severe cardiovascular disease as high as you would expect, but then the next one down, respiratory and also cancer.
So useful information for us to to have.
And we also looked at trends over time. So what we want to see is improvement.
So medical science improving survival rates for people with conditions.
And we've seen this in cancer say cancer research. UK say that survival rates have doubled in cancer overall over the past 40 years.
Now, unfortunately, we just don't see the same improvement in heart failure.
So as you can see on the x axis, this the the year of diagnosis and then the Y axis is survival percentage.
And there's a very slight increase in one year, five year and ten year survival rates over that time period.
But if we look at the the differences from the start of the study to the end, it's really quite small numbers.
So between six and 7% improvements in the one, five and ten year survival rates.
Whereas as I said, with cancer, there's been a doubling. So 200% improvement in a 40 year period.
So we still have a lot of work to do to improve patient's chances once they're diagnosed.
And with the linkage we were also able to look at hospitalisations.
So patients hospitalised around the time of diagnosis compared to those who didn't end up going into hospital around the time of diagnosis.
And you can see the yellow line are those that weren't admitted and the pink cast on those were admitted.
So patients admitted to hospital around the time of diagnosis had a worse survival than those who weren't.
Now that that seems to make sense, but that might be important for us in primary care,
because if we can actually keep people out of hospital by diagnosing and treating them a bit earlier,
that we might actually improve the survival chances potentially.
So there's a definite different scene with the data.
And we also looked at both hospitalised and non-hospitalized patients in terms of the improvements on the left.
It's those that were in the community, not in hospital and their survival rates.
There has been an improvement compared to those who were hospitalised.
So we really haven't seen much improvement at all in people that get hospitalised around that time of diagnosis,
whereas people in the community there has been more improvement than looking at the the cohort as a whole.
So there's strengths and limitations with big datasets.
You get a large number of people and that is a very powerful thing for research.
So we can look at trends over time. We can compare survival in those that do or don't have the condition.
We can calculate survival rates based on age, based on gender.
The downside of datasets, as I'm sure you've covered already, is that, as I said at the beginning,
we use the primary care record predominantly to record our clinical care.
So when I'm sitting in my surgery, seeing patients every 10 minutes at the forefront of my mind,
isn't that this clinical code might be used by a researcher in Oxford at some point in the future.
So I need to get it right. I mean, it does it does make me very aware of the need to code properly.
But this is a snapshot of what's happening in real life clinical practice.
Now, CPR data very good at looking at the quality of coding within their practices.
So they do make sure that the practices are up to standard.
So what we're seeing is reflective of real life primary care, but it is predominantly for routine clinical care.
I think that is a strength as well because it does show real life recruiting patients into research studies.
Does involved involve a certain type of bias, you know, who is willing to participate,
etc., whereas this is showing what is happening every day in the community.
A million patients a day see their GP.
Clinical coding is something that we acknowledge is a limitation in the discussion of all our papers, particularly in heart failure.
It's relevant for the type of heart failure.
So two types of heart failure, heart failure with reduced ejection fraction and heart failure with preserved ejection fraction,
and the treatments for those are quite different. So it's important at diagnosis to differentiate between the two,
but that may not necessarily be recorded in the primary care record and we see in these codes increasingly use more recently.
But if we go back a few years, they weren't really being used before.
So we can't see what type of heart failure it is that we're looking at, unfortunately.
But in the future we are likely to be able to see that.
The other thing which review is sometimes say to us is around the reliability of death reporting in the UK because we don't do many post-mortems,
it relies on the doctor looking after the patient during their last illness and what they put on the death certificate.
But as a reporting system, it is one of the most robust in the world.
And we have to go on what is written on the death certificate because there is no other way and
the fact that it's actively recorded by S and then we can link it with the primary care record.
That's that's quite a powerful linkage that isn't available in many other countries.
So we always push back on that one because I think this is particularly looking at the cause of deaths.
I think is is really important just to give us an idea of what people are dying from and when.
So that was our paper. In 2019, we decided to publish the gender statistics in slightly more detail in the European Journal of Heart Failure.
So we reported national trends in heart failure, survival for men and women.
And and that was published the following year.
And I think, again, because the dataset so big, you get very robust estimates for survival rates, splitting it by by gender.
And what we found was that actually patients so female patients compared to male patients are diagnosed on average five years later.
But then if we adjust, right, their survival is their mortality is actually lower.
So their survival is better than men.
These are infographics which we've started to produce for all of our papers because most people don't read a whole paper,
unfortunately, cover to cover. But what we do is a visual representation of the study.
So aim at the top, the key characteristics, the numbers that we're looking at, and then the key results in bright colours.
And that just helps to get the message out from our research to a wider audience.
So we put these on our website, we tweet them, we put them on Facebook, and we do blogs and things on on the mic part of the website,
which is around diagnostics, just to try and increase the dissemination of our work.
Because if we're going to spend years doing all this research,
we want people to actually engage with it and see what we're doing because hopefully it's relevant to the community population.
So what does this mean for my practice? So as I said, I work in a sort of suburban primary care.
General practice is ATP, so we cover about 10,000 patients.
So that means statistically we'll have about 20 patients newly diagnosed with heart
failure each year and about 100 people on our list living with heart failure.
So in that 20, that will be newly diagnosed.
What our research shows is that around four of them, so 20% will die within the first year.
And their patients that we need to identify early, we need to have them on our palliative care registers and gold standards framework,
which is how we assess end of life care and identify people that are more likely to need end of life care because often these patients do get missed.
Most of our registries focus on cancer diagnoses and cancer deaths,
but actually people with heart failure also need high quality palliative care and end of life care.
So at that 24, we're losing a year, but ten will survive the next five years and actually six will still be alive ten years later.
So those six are people whose heart failure is potentially treatable, manageable, and it may be something else that causes them a problem in the end.
So that's the difficulty in general practice.
But also the joy of it is that you look at the patient overall, you don't just look at the one diagnosis,
you look at what is wrong with them and how we can manage that individual optimally to optimise their pace,
their quality of life as as well as their survival.
So that's looking at our survival analysis and how we use big data for that.
And I'd just like to spend the second half of the talk looking at diagnosis, which is what we're focusing our research on at the moment.
So what does Big Data tell us about diagnosis?
So just a little bit about the heart failure pathway.
So as I said, patients with heart failure develop shortness of breath, ankles swelling, leg swelling, and they get incredibly fatigued and exhausted.
And the diagnostic pathway relies on patients recognising the symptoms and seeking help for them.
And as I said from my qualitative research, patients are often quite stoical at the beginning and didn't necessarily always
appreciate that their symptoms could be linked to something like heart failure.
So patients would say to me that they had put their breathlessness perhaps down to another condition.
If they got lung disease, they put it down to that or almost normalise the symptom saying,
Oh, it's just getting older, or put it down to all the medication that they were on.
So there is in most of my interviews, there was a gap between patients getting symptomatic and actually seeing their GP.
And I think there's an issue there about awareness and we need to raise awareness of heart failure more generally amongst the public.
So when a patient comes to see the GP, it's then our responsibility to think of heart failure.
So as you said, these patients often have several long term conditions or on lots of medications.
So heart failure may be one of many things going through our minds when we're looking at why is this patient breathless?
And we know from some research there is a delay between patients coming to see their GP and actually getting a diagnosis of heart failure because
we can go down other diagnostic routes before we think about heart failure and then when we've considered heart failure is a possibility,
we do a blood test which shows if the heart is under strain or not.
And I'll talk about that in a second. And if that blood test is raised, then we need to refer the patient to get a definitive diagnosis.
So heart failure is the diagnosis we make in primary care. If we think somebody's got heart failure, we need to refer for further imaging.
So a heart scan called an echocardiogram and also a specialist assessment, and it's usually a cardiologist that would make the final diagnosis.
So this is the nice guidance on testing for heart failure.
So there's a blood test which goes up when the heart is under strain called not genetic peptide.
There's two types this NT, Probnp and BNP.
There are two parts of the same molecule and they're both not terrific peptides.
The one that tends to be used more commonly now is anti probnp.
And in the nice guideline, there's clear guidance for what we should do depending on the results of this blood test.
So it's a simple blood test that we take along with blood tests we would do for kidney function,
blood count, etc. if the level of treated peptides very high,
so above 2025 with where to refer urgently for a patient to have a heart scan and a specialist assessment within two weeks.
So similar to the two week wait cancer pathways.
If it's between 402,000, we need especially assessment and echo within six weeks.
But if it's below 400, then that means heart failure is unlikely and we should think about alternative conditions.
So it can be a very helpful test to rule out heart failure if if you just want to make sure it's not that and it's probably something else.
So not sure. I think peptides are released by the ventricles in response to pressure or fluid overload.
They relax vascular smooth muscle and also induce a decrease.
So they act like a lot of the drugs we use to treat heart failure.
And the two types of not drastic peptide, a equally reliable intake gnosis and T Pro is slightly more stable.
So if the blood test is sitting in a general practice for hours before they go off to the lab, that won't affect the anti pro results.
And the thresholds are different though, so you need to know which one you're doing.
So BNP less than 200 or anti pro less than 400 makes heart failure unlikely, but it doesn't differentiate between the two types of heart failure.
So you need an echo for that.
So this is a study where we looked at the association between the naturalistic peptide level at diagnosis and subsequently what happens to patients.
So how likely they are to be hospitalised and how likely they are to die.
And this was published in the journal Heart last year.
So that's a journal that's read predominantly by cardiologists in the UK.
So as we've said, heart failure is a malignant condition requiring urgent treatment, and the guidelines recommend that we do not treat it.
Peptide Testing to prioritise referral for diagnosis.
And we use CPR again between 2004 and 2018.
And we had over 40,000 people with a new diagnosis of heart failure,
half of whom were men and slightly older, 78 and a half was the main age at diagnosis.
Again, we linked to the hospital episode statistics dataset and also to the OS civil mortality data to get a holistic view of what was happening,
and particularly to answer this research question about who's going into hospital and who's dying of heart failure.
And we looked at one year hospitalisation. So from a diagnostic code, how likely are people to go into hospital during that time?
And also the one, five and ten year survival rates as we had with survival, but specifically in relation to the AMP level at diagnosis.
So. As you can see from the survival curves, we've got time on the x axis, survival probability on the Y axis.
And we looked at the different categories. So as we said, less than 400, it's unlikely to be heart failure.
I think something else, 400, 2000 is what we call moderately raised.
And then above 2000 is is a high level and they're the people that need referral and see within two weeks.
And this survival curve just sort of confirms that the guidance is correct in
that the sort of dotted blue line is those people that have an anti pro level
above 2000 at diagnosis and they're the people that are much more likely to die
of the condition than those who have a moderate or a normal anti probnp level.
So overall in the cohort of 40,000 we found over half were hospitalised in the year following diagnosis, a huge burden for the health service.
If you've got 50% chance of going into hospital a year after diagnosis and it was those people who had a very high anti pro BNP,
so above 2000 that were more likely to have a heart failure related hospitalisation compared to those with a moderate natural tick peptide.
So these are very high risk people and we need to get them seen,
diagnosed and treated very quickly in order to prevent them going into hospital or having worse outcome.
So we also looked at overall mortality rates between those with high or moderate MP.
And as you can see, again, the mortality rates at one, one year,
five years and ten years are all higher in that high anti probnp group compared to the moderate and TPO BNP group.
We did a competing risk model to look at the risk of heart failure related deaths.
So are people dying from heart failure or something else?
And those with a high anti pro BNP had a 50% higher rate of heart failure related deaths compared to those with moderate.
So these are people going into hospital with heart failure, dying of that heart failure.
And interestingly, the median time between anti so natural peptide test and heart failure diagnosis was over 100 days.
So nice recommend that patients following their test should be seen and diagnosed
within two weeks if it's both 2000 but within six weeks for everyone else.
And this is way outside of that. So patients are waiting a long time and I think that's an area where we need to shine
a light and that's an area that requires improvement because if in primary care,
I see a patient that might have heart failure. I do. And that genetic peptide test and it's high.
If it's at both 2000, I need that patient to have the echo and see a specialist quickly because otherwise and what I have seen in clinical
practice is patients have had their MP test and while they're waiting to get their scan and their right patient appointment,
they're admitted to hospital because they're too unwell to to be in the community.
So they go into hospital, have intravenous diuretics, and that's when they get their diagnosis.
If we could make that diagnosis sooner and initiate treatment more quickly, we can maybe have prevented that hospital admission.
And I think that's what they see this data show, is that this is what's going on and this is where we need some improvements.
It's particularly important when hospitals are under such strain from COVID and trying to catch up after COVID.
We need to keep people out of hospital unless they need to be there.
And I think what we need is more capacity in cardiac imaging.
Get in an echo, takes weeks and weeks where I work.
So there is a real shortage of echo technicians to be able to do these specialist scans.
So there's several points in the pathway where we need increased capacity in the system in order for the diagnostic process to work properly.
And I think that's what these big data can show us and that's what we can take to policymakers to try and effect change.
And my final example is our most recent paper looking at long term trends in nutritive peptide testing.
So we've seen survival for heart failure is pretty poor 50%, five year survival rates that haven't really improved over time.
And we've seen from the nice guidance that transit peptide testing is absolutely key to getting a diagnosis
and getting on these effective treatments that we know can improve quality and quantity of life.
So in this study, we wanted to look at what's happening with naturalistic peptide testing and what's what's the sort of trend with it over time.
Because certainly when I qualified, we weren't using not tested peptide testing in the community.
I didn't have access to it. And now we are.
So the overall aim was to report the trends in testing and look at subsequent heart failure diagnosis over time.
Again, we chose the same data as the previous study.
It was all part of a CPD data code to answer three different questions.
And the third question I won't talk about today, but will be at BGP in January.
So that's our last bit to this, this big piece of work.
But again, 14 year period, long period of time linked to the hospital episode statistics and OS mortality data.
We had a bigger dataset this time, so 7.2 million patients we looked for not just peptide testing codes within their primary care records,
and we had over a thousand practices in this. So a significant proportion of the UK population, probably 10 to 15% covered within this study.
And what what has been happening with that peptide testing?
So as you can see from 2004, we weren't really doing much testing because most of us didn't have access to it.
I qualified in 2009. We got access in my practice in about 2011, and that really represents the national picture.
So there's this sudden uplift in 2010, real increase in testing, both BNP, which is the blue line and anti pro BNP, which is the red line.
But as you can see, and Tipos become much more the dominant test.
So it depends what your lab offer as to what you can order, but anti pro is more stable over time.
It's not metabolised by the newer drugs as well.
So nice now recommending anti pro BNP testing if available, but obviously if your hospital lab is set up for BNP then that's fine too.
And 2010 is the important year because that's when nice updated guidance previously.
So I was on the 2018 update committee, but the previous update had been in 2010.
And in that document they really strengthen the role of not treating peptide testing in the diagnostic pathway.
And it's interesting to see this uplift in testing since then.
We also looked at men and women again and in different age groups.
So Blue is the oldest age group and this is appropriate.
So it's by age. So there's more testing in older people and that's what you'd expect because the mean age at diagnosis is 76, 77.
We should be testing more in older people because they're more likely to have the condition.
And similarly, we looked at ethnicity. So not much difference between ethnic groups and deprivation.
So it's good to say that in the most deprived group, that's where the most testing is.
And heart failure prevalence is higher in deprived populations.
But sometimes you see health inequalities where actually the least deprived, they're getting the most testing.
But actually, from this big data, that wasn't the case. And we are testing the people that need to be tested, which is good.
And we we get a number. So the natural peptide level within the big data so we can calculate the median level at
time of heart failure diagnosis and we can look at that for each year of the 14 years,
15 years in the study. And as you can see, it's pretty consistent.
So the top graph, so those with BNP tests with and with that heart failure and the bottom is
anti pro with heart failure without and there hasn't really been much change.
So the median level of anti pro BNP at diagnosis is around 1200, which is actually quite high because the threshold for referral is only 400.
So that means that we are still getting people that have more advanced heart failure then being close to that threshold.
So that was an interesting finding because perhaps we need to be testing people a bit earlier and we need
to think about how we might be able to do that in order to catch people sooner in the disease process,
to be able to treat them before they land open. And because they're too breathless and they need intravenous diuretics to to make them feel better.
So these were the main findings that we concluded. So there has been this real increase in testing.
So we are using more natural peptide tests in primary care.
We using them in appropriate populations. So older people, more deprived people are getting tested.
We found the heart fake fairly detection rate was around constant, so around 10%.
So one in ten people who have enough to take a test will get a diagnosis of heart failure.
And that hadn't really changed. Whereas in cancer we're testing a lot more people.
So the detection rates are much, much lower because most people tested on having a diagnosis of cancer,
whereas in Hertfordshire that has stayed constant. So perhaps we need to be thinking about testing more people.
And surprisingly, in the last period of the study, still up to 2017, so pre-pandemic,
but recently only one in four people had a natural peptide test prior to that heart failure diagnosis.
So three quarters of people who were diagnosed with heart failure hadn't had a electrolytic peptide test beforehand.
So even though there has been this really big increase in testing,
we probably need to look at more testing in primary care to try and facilitate a
diagnosis at an earlier and more treatable stage before people end up in hospital.
So overall, my lessons from Big Data, I think as I've said, it's a really powerful research tool to be used.
I reflects better than any other methodology,
really real life practice and what is happening in real life in the million consultations in general practice each day in the UK,
I think the downsides are, as I've said, we use it for routine clinical care.
It's not collected in the same way as we would for robust or qualitative work.
It's just not using the same research methods, but coding standards are looked at and.
ROPER So I think it's still a reliable data source, but we need to acknowledge the limitations whenever we report this work.
I think the really key thing for me is always bringing the question back to the patient, because these data sets are so big,
you can look at so many things and get so many numbers, but actually what what's important and what matters.
So all three examples I gave the research question idea came from patients that I had talked to, either in our peer group or in my clinical practice.
And that's what then drove the big data analysis and the write up and the reporting of the results.
And I think without that focus we can get a bit lost in the big data.