Category Archives: my work

Cheesecake Files: Machine learning heart attacks

“Machine learning” rates very high on the buzz-word scale, right up there with “nano-technology” and “blockchain”. Like most buzz it is more noise than substance. However, every now and again it looks like there might be something in the noise that bites. This episode of the Cheesecake Files1 is about testing an algorithm (another buzz word) developed through a machine learning technique for the early detection of heart attacks (strictly – myocardial infarction).

The buzz

Before I begin my story in earnest a couple of words about the buzz words. When I say “algorithm” think “recipe”. In the context of emergency medicine this is simply a series of steps which assist the medical team in their decision making. For example – if the presenting complaint is “chest pain” the triage nurse will connect up a device (ECG) to measure the electrical activity of the heart and will draw some blood and send it off the the lab with specific instructions to measure the concentration of a molecule called troponin. Several years ago we introduced in all New Zealand emergency departments more detailed pathways (ie an algorithm) which included guidance on which other data to obtain from the patient, when to repeat blood measurements and how all the data goes together to risk stratify the patient. The principal aim being to ensure that as quickly as possible, and as safely as possible, physicians could rule out the presence of a heart attack. This is important because patients presenting with possible heart attacks are one of the most common presentations to the ED and so if they remain in the ED a long time this can affect the whole service. However, only about 10-15% (in NZ) are actually having a heart attack. Many of those who aren’t can now be reassured early that they are not. Please note – if you’ve sudden onset chest pain then the ED is the right place for you. Just because most who attend are not having a heart attack doesn’t mean that you might not be.

The other buzz word is “machine learning.” This term is usually used to mean a computational technique which involves giving a computer some data and some basic instructions how to look at it. Then asking the computer to make a prediction of an outcome (in our case, whether a patient is having a heart attack or not). The prediction is compared to the actual outcomes and information on how well the computer performs is feedback into the machine to tweak some of the algorithm. Think of this as tasting the soup and then adding a few more spices. The process is repeated many times until the soup is as good as it can possibly be. Some recipes we know and can follow ourselves. Some happen behind closed doors as a team of chefs puts together a meal. A characteristic of machine learning algorithms is that they are often not easily understood (a “black-box”), but the proof of the pudding is in the eating. This leads me to the story that is the current cheesecake.

The story

Nearly three years ago we were asked to test if an algorithm called MI3 works to risk stratify people who appear in the emergency department with symptoms suggestive of a heart attack. The algorithm had been developed by a US based diagnostic company called Abbott Diagnostics. We were given access to the black box and could input variables from real patients and observe the predicted outcome. In this case the algorithm was producing a number that very closely corresponded to the probability of a patient having a heart attack. There were very few variables required to make this prediction – sex, age, two measures of troponin and the time between the two measures. The latter is important because how troponin concentrations change over time informs us about the possible heart attack.

A collaboration of research groups from Scotland, Switzerland, Germany, United States of America, Australia and New Zealand came together to provide sufficient data to test MI3. This group was lead by Christchurch ED physician Dr Martin Than, and Scottish cardiologist, Prof Nicholas Mills. I was charged with pulling together all the data and conducting the statistical analysis of the performance of MI3.

There were about 8000 patients in our testing data set with 10.6% of them having a heart attack. Importantly, the first thing I noted is that the values output by the algorithm corresponded to the true rate of heart attacks. ie when the MI3 value was 5 about 5% of those with this value were having a heart attack, when it was 90 about 90% of people were having a heart attack. In other words, the algorithm was well calibrated – this can give physicians confidence. The second thing was to see if we could find MI3 values below which we could say that almost everyone is not having a heart attack (it’s impossible to be 100% certain – we aim for about 99% or better). We were able to find such a value and show that it identified an impressive 69% of people as low-risk. The full results are available in the cardiology journal Circulation – here.

The application

So, how may this be used? The difference with this algorithm compared with others is three-fold (i) it does not require blood samples to be taken an specific set intervals, (ii) it does not require information about patient history or detailed signs and symptoms to be gathered and incorporated, (iii) and the output is a probability rather than simply stratifying patients to a low, intermediate or high risk category. In other words, the inputs are simple and objective, and the output is easily interpretable. In practice, the physician may receive the MI3 value from the labs along with the troponin results. This may aid discussions with the patient through the use of icon arrays or similar (see the figure).

A concept of how a tool displaying the result of the algorithm may be used to display risk to physician and patient.

1 Once upon a time, a long long time ago, I received a cheesecake for every publication. Sadly, those days are gone now. But I live in hope.

Disclaimer: I have acted as a consultant statistician for Abbott Diagnostics. I have no shares or intellectual property associated with MI3. Abbott was not involved in the testing of the algorithm.

Cheesecake files: A new test to rule out heart attacks in just a few minutes.

Your chest hurts, you go to the hospital (good move), you get rushed through and a nurse takes some blood and measures the electrical activity of your heart.  A doctor asks you some questions.  While she does so, the blood is being tested – the results are back already! Yeah, they are negative and everything else is OK, it’s not a heart attack – you can go home.  This is the likely scenario in the near future thanks to new blood test technology which we, in Christchurch hospital’s Emergency Department, have been fortunate to be the first in the world to trial in patients. The results of our pilot study have now been published ( in a Journal of the American Medical Association (JAMA Cardiology).

About 65,000 patients a year are investigated for heart attacks in New Zealand emergency departments, yet only about 15% of them are actually having a heart attack.  New Zealand leads the world in having become the first country in the world in which all patients are assessed by an accelerated diagnostic pathway that enables rapid evaluation of the patients and can send people home after two blood tests taken two to three hours apart (see here for more).  This means many patients who once-upon-a-time would have been admitted to hospital overnight, are now able to be reassured after 4-6 hours that they are not having a heart attack and can go home.  Nevertheless, there are enormous advantages for both patient and health system to being able to come to the conclusion that the pain isn’t life threatening earlier. The cork in the bottle preventing this happening is the time it takes for a blood sample to be analysed for signs of damage to the heart. These blood tests typically take 1 to 2 hours from the time of sampling (within ~15 minutes of arrival in the ED) until the results are available for the doctor to review.  Because doctors are dealing with multiple patients at a time, their review and decisions around whether to allow the patient to go home, or to be admitted for more investigation, are further delayed.  A point-of-care test is one that happens with a small machine near the bedside and can produce results available to the doctor even while they are still examining the patient.  Until now, though, the precision of these machines has not been good enough to be used in emergency departments.  When one manufacturer told us that their new technology may now have sufficient precision we were keen to test it,  so we, in a first-in-the-world study, undertook a study in patients entering the emergency department of Christchurch hospital whom the attending doctor was investigating for a possible heart attack.

Thanks to the volunteer patients (I love volunteers) who gave some extra blood we measured the troponin concentration by this new point-of-care test (called the next generation point of care troponin I: TnI-Nx). Troponin comes from the heart muscle and is released into the blood during a heart attack. When the troponin concentrations in the blood are very very low we can be confident that the source of the patient’s discomfort is not a heart attack.  Low concentrations require a very precise measurement test. Often, a very low concentration means the patient can safely go home. In 354 volunteers we measured troponin with the TnI-Nx assay when they first came to the emergency department.  Their treatment didn’t change, and all clinical decisions were based on the normal laboratory based troponin (measured on entry to the emergency department and again 2 hours later). From the blood samples we collected and measurements we made, we could work out what could have happened if we had used the TnI-Nx results instead.

In our study the TnI-Nx troponin measurement was as good as, and possibly slightly better, than the laboratory based troponin measurement at ruling-out a heart attack. We found 57% of the patients being investigated had troponin concentrations measured with TnI-Nx below a threshold at which we could be confident that they were not having a heart-attack.  All 57 patients who were actually having a heart attack had higher concentrations.

When implemented our results may mean that instead of waiting 3-6 hours for a results, half of patients being investigated could know within about 30 minutes of arriving at the ED whether they are having a heart attack or not.  This early reassurance would be a relief to many, as well as reducing over-crowding in the emergency department and freeing up staff for other tasks.  But before we implement the new test, we must validate it in more patients – this is a study we are carrying out now.  Validation will enable us to more precisely determine a threshold concentration for TnI-Nx for clinical use which we can, with a very high degree of certainty, safely use to rule-out a heart attack.

The test also should allow people living in rural areas to get just as good care as in emergency departments because it could be deployed in rural hospital and general practices.  This would save many lengthy, worrying, and expensive trips for people to an urban emergency department.

This study was carried out by the Christchurch Emergency Department research group (director and senior author Dr Martin Than) in conjunction with the Christchurch Heart Institute (University of Otago Christchurch).  My colleague, Dr Joanna Young did much of the hard yards, and we thank our clinical research nurses and assistant for all they did to take blood samples, collect data, and lend a hand around the ED.  The manufacturer of the blood test, Abbott Point-of-care, provided the tests free of charge, but they were blinded to the results and all analysis was conducted independent of them.

How we envisage TnI-Nx may be used in the future to allow very early rule out of heart attacks

Please note – patients experiencing sudden onset chest-pain should always seek immediate medical attention.

I am fortunate to hold a Senior Research Fellowship in Acute Care sponsored by the Canterbury Medical Research Foundation, the Emergency Care Foundation, and the Canterbury District Health Board which enables me to participate in these studies.

ps.  You’ll have to read some of my older posts if you want to know why “Cheesecake files”

 

Cheesecake Files: The ICare-Acute Coronary Syndrome (heart attack) study

Hundreds of nurses, Emergency Department doctors, Cardiologists and other specialists, laboratory staff, administrators and managers from every hospital in New Zealand with an emergency department have come together to implement new, effective, and safe pathways for patients who think they may be having a heart attack.  Today, Dr Martin Than (CDHB, Emergency Department) presented to the American Heart Association results of our research into the national implementation of clinical pathways that incorporate an accelerated diagnostic protocol (ADP) for patients with possible heart attacks.  Simultaneously, a paper detailing that research is appearing in the academic journal Circulation.

The headlines, are that in the 7 hospitals we monitored (representing about 1/3rd of all ED admissions in NZ a year), there was a more than two fold increase in the numbers of patients who were safely discharged from the ED within 6 hours of arrival and told “It’s OK, you are not having a heart attack”.

Improving Care processes for patients with a possible heart attack.

Why is this important?

About 65,000 of the 1 million presentations to EDs each year in New Zealand are for patients whom the attending doctors think may be having a heart attack.  However, only 10-15% of those 65,000 are actually having a heart attack.  The traditional approach to assessment is long, drawn out, involves many resources, and means thousands of people are admitted into a hospital ward even thought it turns out they are not having a heart attack.  Of course, this means that they and their families have a very uncomfortable 24 hours or so wondering what is going on.  So, any method that safely helps to reassure and return home early some of those patients is a good thing.

What is a clinical pathway?

A clinical pathway is a written document based on best practice guidelines that is used by physicians to manage the course of care and treatment of patients with a particular condition or possible condition.  It is intended to standardise and set out the time frame for investigation and treatment within a particular health care setting – so it must take into account the resources available for a particular hospital.   For example, each hospital must document how a patient is assessed and if, for example, they are assessed within the ED as having a high-risk of a heart attack, where they must go.  In a large metropolitan hospital, this may mean simply passing them into the care of the cardiology department.  In a smaller setting like Taupo, where there is  no cardiology department, it may mean documenting when and how they are transported to Rotorua or Waikato hospital.

What is an accelerated diagnostic protocol?

An accelerated diagnostic protocol (ADP) is a component of the clinical pathway that enables the ED doctors to more rapidly and consistently make decisions about where to send the patient.  In all cases in New Zealand the ADPs for evaluating suspected heart attacks have 3 main components: (i) an immediate measurement of the electrical activity of the heart (an ECG), (ii) an immediate blood sample to look for the concentration of a marker of heart muscle damage called troponin, and a second sample 2 or 3 hours later, and (iii) a risk score based on demographics, prior history or heart conditions, smoking etc., and the nature of the pain (ie where it hurts and does it hurt when someone pushes on the chest, or when the patient takes deep breaths etc).   Importantly, these components enable a more rapid assessment of patients than traditionally and, in-particularly, enable patients to be rapidly risk stratified into low-risk, intermediate risk, and high-risk groups.  Usually the low-risk patients can be sent home.

What was done?

The Ministry of Health asked every ED to put in place a pathway.  Over an ~18 month period, a series of meetings were held at each hospital which were led by Dr Than, the clinical lead physician for the project.  Critically, at each meeting there were multiple members of the ED (doctors and nurses), cardiology, general wards, laboratory staff, and hospital administrators.  The evidence for different ADPs was presented.  Each hospital had to assess this evidence themselves and decide on the particularly ADP they would use.  Potential barriers to implementation and possible solutions were discussed.  Critically, champions for different aspects of the pathway implementation process were identified in each hospital.  These people led the process internally.

Oversight of the implementation was an adhoc advisory board put together by the Ministry of Health and with MoH officials, Dr Than, Cardiologists, and myself.

The Improving Care processes for patients with suspected Acute Coronary Syndrome (ICare-ACS) study was a Health Research Council sponsored study with co-sponsorship of staff time by participating hospitals.  Its goal was to measure any changes in each hospital to the proportions of patients who were being discharged home from ED early and to check whether they were being discharged safely (ie to check that there were not people with heart attacks were being sent home).  Dr Than and I co-led this project, but there were many involved who not only set up the pathways in each of the 7 participating study hospitals, but who also helped with attaining the data for me to crunch.

What were the study results?

In the pre-clinical pathway implementation phase (6 months for each hospital) there were 11,529 patients assessed for possible heart attack. Overall, 8.3% of them were sent home within 6 hours of arrival (we used 6 hours because this is a national target for having patients leave the ED).  The proportions of patients sent home varied considerably between hospitals – from 2.7% to 37.7%.  Of those sent home early, a very small proportion (0.52%) had what we call a major adverse event (eg a heart attack, a cardiac arrest, or death for any reason) within 30 days.  This is actually a very good number (it is practically impossible to be 0%).

We monitored each hospital for at least 5 months after pathway implementation and a median of 10.6 months.  Of the 19,803 patients, 18.4% were sent home within 6 hours of arrival.  ie the pathway more than doubled the number of patients who were sent home early.  Importantly, all 7 of the hospitals sent more patients home earlier.  The actual percentages sent home in each hospital still varied, showing there are more further improvements to be made in some hospital than others.  Very importantly, the rate of major adverse events in those sent home remained very low (0.44%).  Indeed, when we looked in detail at the few adverse events, in most cases there was a deviation from the local clinical pathway.  This suggests that some ongoing education and “embedding in” of the pathways may improve safety even more.

The study also showed that amongst all patients without a heart attack the implementation of the pathway reduced the median length of stay in hospital by nearly 3 hours.  Using crude numbers for the cost of an acute event in a hospital I estimate that this is a saving to the health system of $9.5Million per year.  These types are calculations are difficult and full of assumptions, nevertheless, I can be confident that the true savings are in the millions (pst… Government… I wouldn’t mind a fraction of this saving to carry on research please).

How did this come about?

This study and the pathway implementation is the result of a decade long series of studies in Christchurch hospital and some international studies, particularly with colleagues in Brisbane.  These studies have involved ED staff, cardiologists, research nurses, University of Otago academics (particularly those in the Christchurch Heart Institute) and many others.  They began with an international onbservational study which measured troponin concentrations at earlier than normal time points to see whether they gave information that would enable earlier discharge of some patients.  This was followed by the world’s first randomised trial of an ADP verse standard (then) practice.  That showed that the ADP resulted in more patients being safely sent home.  It was immediately adopted as standard practice in Christchurch.  The ADP was refined with a more “fit for purpose” risk assessment tool (called EDACS – developed locally and with collaboration of colleagues in Brisbane).  The EDACS protocol was then compared to the previous protocol (called ADAPT) in a second randomised trial.  It was at least as good with potential for discharging safely even more patients.  It is currently standard practice in Christchurch.

As a consequence of the Christchurch work, the Ministry of Health said, effectively,  ‘great, we want all of New Zealand to adopt a similar approach’, and the rest, as they say, is history.  Now, all EDs have a clinical pathway in place, all use an evidence based ADP – two use the ADAPT and all the rest use EDACS with one exception which uses a more ‘troponin centric’ approach (still evidence based) which I won’t go into here.  Meanwhile, all of Queensland has adopted the ADAPT approach and we know of many individual hospitals in Australia, Europe and Iran (yes) which have adopted EDACS.

Other help

As mentioned already, the Health Research Council and the Ministry of Health along with all those medical professionals were integral to getting to where we are today.  Also integral, were all those patients who in the randomised trials agreed to participate.  Medical research is build on the generosity of the patient volunteer.  Behind the scenes is our research manager, Alieke, who ensures doctors run on time.  Finally, I am very fortunate to be the recipient of a research fellowship that enables me to do what I do.  I thank my sponsors, the Emergency Care Foundation, Canterbury Medical Research Foundation, and Canterbury District Health Board.  Some of the earlier work has also been done in part with my University of Otago Christchurch hat on.  Thank you all.

An even quicker way to rule out heart attacks

The majority of New Zealand emergency departments look for heart muscle damage by taking a sample of blood and looking for a particular molecule called a high-sensitivity troponin T (hsTnT).  We have now confirmed that rather than two measurements over several hours just one measurement on arrival in the ED could be used to rule out heart attacks in about 30% of patients.

What did we do?

We think this is a big deal. We’ve timed this post to meet the Annas of Internal Medicine timing for when our work appears on their website – here.  What we did was to search the literature to find where research groups may have measured hsTnT in the right group of people – namely people appearing in an emergency room whom the attending physician thinks they may be having a heart attack. We also required that the diagnosis of a heart attack, or not, was made not by just one physician, but by at least two independently.  In this way we made sure we were accessing the best quality data.

Next I approached the authors of the studies as asked them to share some data with us – namely the number of people who had detectable and undetectable hsTnT (every blood test has a minimum level below which it is said to be “undetectable” in hsTnT’s case that is just 5 billionths of a gram per litre, or 5ng/L).  We also asked them to check in these patients if the electrical activity of the heart (measured by an electrocardiogram or “ECG”) looked like there may or may not be damage to the heart (a helpful test, but not used on its own to diagnose this kind of heart attack).  Finally, we asked the authors to identify which patients truly did and did not have a heart attack.

What did we find?

In the end research groups in Europe, UK, Australia, NZ, and the US participated with a total of 11 studies and more than 9000 patients.  I did some fancy statistics to show that overall about 30% of patients had undetectable hsTnT with the first blood test and negative ECGs.  Of all those who were identifiable as potentially “excludable” or “low-risk” only about 1 in 200 had a heart attack diagnosed (we’d like it to be zero, but this just isn’t possible, especially given the diagnosis is not exact).

VisualAbstract AnnalsIM 170411

Pickering, J. W.*, Than, M. P.*, Cullen, L. A., Aldous, S., Avest, ter, E., Body, R., et al. (2017). Rapid Rule-out of Myocardial Infarction With a High-Sensitivity CardiacTroponin T Measurement Below the Limit of Detection: A Collaborative Meta-analysis. Annals of Internal Medicine, 166(10). http://doi.org/10.7326/M16-2562 *joint first authors.

What did we conclude?

There is huge potential for ruling out a heart attack with just one blood test.  In New Zealand this could mean many thousands of people a year can be reassured even more swiftly that they are not having a heart attack. By excluding the possibility of a heart attack early, physicians can put more effort into looking for other causes of chest-pain or simply send the patient happily home.   While not every hospital performed had the same great performance, overall the results were good.  By the commonly accepted standards, it is safe.  However, we caution that local audits at each hospital that decides to implement this “single blood measurement” strategy are made to double check its safety and efficacy.


Acknowledgment: This was a massive undertaking that required the collaboration of dozens of people from all around the world – their patience and willingness to participate is much appreciated. My clinical colleague and co-first author, Dr Martin Than provided a lot of the energy as well as intelligence for this project. As always, I am deeply appreciative of my sponsors: the Emergency Care Foundation, Canterbury Medical Research Foundation, Canterbury District Health Board, and University of Otago Christchurch. There will be readers who have contributed financially to the first two (charities) – I thank you – your generosity made this possible, and there will be readers who have volunteered for clinical studies – you are my heroes.

Sponsors

 

 

Big data + Big science = Big health

Big data and big science are buzz phrases in health research at the moment.  It is not at all apparent what the exact definition of these are or should be and whether they will be short lived in our lexicon, but I think it reasonable to assume that where there is buzz there is honey.

I think of big data in health as information routinely collected by our interaction with health systems, both formal (eg GPs or hospitals) and informal (eg networked devices that continuously monitor our heart beat).  Through ever improving connectivity such data may become available (anonymously) for the health researcher and policy maker.  The statistical tools needed to analyse this volume of data without producing spurious correlations are still being developed and there are some genuine ethical concerns that must be addressed.  Within New Zealand we have a unique alpha-numeric identifier for anyone who has encountered our formal health system.  This is very unusual internationally and puts us in a good position to pull data together from multiple sources and to monitor change over time.  Recently I have used this system to assess the performance of new emergency department chest-pain pathways at multiple hospitals throughout the country.  These pathways had been developed in research programs in Christchurch and Brisbane. Following a Ministry of Health initiative for each emergency department to adopt such a pathway, and with the financial support of a Health Research Council grant (and my personal sponsors), we were able to establish efficacy and safety parameters of the change in practice.  If we had used a traditional model of employing research staff at each hospital the costs would have run into many millions and would simply not have been possible given how health research is financed in this country.  This model of monitoring changes made to how health care is delivered is both pragmatic and affordable.  It is also necessary if we are to be reassured that change is really improving practice. We expect to see more big data used in this way.

Big science is often thought of in terms of hundreds or thousands of researchers in facilities like CERN costing hundreds of millions of dollars. I think big science need not be so large or expensive.  Rather it is large international collaborations whereby sufficient good quality clinical research data is gathered to answer important clinical questions.  The key is “sufficient”.  Because of the prevalence of a disease or the size of a population base any one research group may not be able, in a reasonable time frame, to collect sufficient data to answer the important questions.   Over the past two years I have been involved in several international studies where we have pooled data, some of which our group has led, some of which are led by colleagues overseas.  We are now formalising a “consortium” to further ensure data is well and appropriately used and collected.  This move had been particularly important as even million dollar studies of a thousand patients do not have sufficient data to answer some of the key safety questions around the diagnosis of heart attacks (my current focus).  A criticism of much academic clinical research is that it is just not useful1.  This is in large part because the studies are too small to give results that would change practice.  They are also often not pragmatic enough (eg by excluding significant portions of patients likely to be assessed or treated by the intervention under study).  Recognition that it is through large collaborative studies that useful practical change can occur will lead to more such collaborations.  They require people to be involved with a slightly different skill set than those whose research is purely local – in particular the “people” skills required to form productive and lasting cross-cultural relationships.  They also require flexibility in funding which may lead to how rules for some grants change (eg by allowing some portion of funding to be spent offshore).

The era of Big data and Big science for Big health is both daunting and exciting.  While there will no doubt be blind alleys and false starts as with any research or new venture, there will also be practical and meaningful evidence based changes to health delivery. Something to look forward to.

  1. Ioannidis, J. P. A. (2016). Why Most Clinical Research Is Not Useful. Plos Medicine, 13(6), e1002049. http://doi.org/10.1371/journal.pmed.1002049.t003

Cheesecake files: A little something for World Kidney Day

Today is World Kidney Day, so I shall let you in on a little secret. There is a new tool for predicting if a transplant is going to be problematic to get working properly.

Nephrologist call a transplant a “graft” and when the new kidney is not really filtering as well as hoped after a week they call it “Delayed Graft Function.”  Rather than waiting a week, the nephrologist would like to know in the first few hours after the transplant if the new kidney is going to be one of these “problematic” transplants or not.  A lot of money has been spent on developing some fancy new biomarkers (urinary) and they may well have their place.  At this stage none are terribly good at predicting delayed graft function.

A while ago I helped develop a new tool – simply the ratio of  a measurement of the rate at which a particular substance is being peed out of the body  to an estimate how much the body is is producing in the first place.  If the ratio is 1 then the kidney is in a steady state. If not, then either the kidneys are not performing well (ie not keeping up with the production), or they have improved enough after a problem and are getting rid of the “excess” of the substance from the body.  This ratio is simple and easy to calculate and doesn’t require extra expense or specialist equipment.

A few months ago, I persuaded a colleague in Australia to check if this ratio could be used soon after transplant to predict delayed graft function. As it turns out in the small study we ran that it can, and that it adds value to a risk prediction model based on the normal stuff nephrologists measure! I’m quite chuffed about this.  Sometimes, the simple works.  Maybe something will become of it and ultimately some transplants will work better and others will not fail.  Anyway, it’s nice to bring a measure of hope on World Kidney Day.

This was published a couple of weeks ago in the journal Nephron.

 

Cheesecake files: A world second for heart attacks

Going to the Emergency Department with chest pain no longer means an almost certain night in hospital.  Friday saw the publication online of our randomised controlled trial comparing two different strategies to rapidly rule-out heart attacks in people who present with chest pain to hospitals.  Here’s a précis:

What’s the problem?

  • Chest pain is common – 10% or so of presentations to ED are for chest pain.
  • Heart attacks are not so common – only ~10-15% in NZ (and less overseas*) actually have a heart attack.
  • It is devilishly difficult for most chest pain to rapidly rule out the possibility of a heart attack.
  • Consequently, most people get admitted to hospital (in 2007 93% of those presenting with chest pain).

But – led by Dr Martin Than in Christchurch and an international group including Dr Louise Cullen in Brisbane – a series of observational studies and one randomised control trial have resulted in a gradual increase in the proportion discharged.  The trial was the first of its kind, it compared standard practice at assessing chest pain to a purpose built accelerated diagnostic pathway (ADP), which we called ADAPT.   In that study 11% of patients in the standard practice (control) arm and 19.3% in the ADAPT ADP arm (experimental arm) were discharged home from ED within 6 hours.  A great improvement which led to that ADP being adopted in Christchurch hospital.

So why another study?

Two reasons: First, 19% still means that there are many patients being admitted who potentially don’t need to be in hospital.  Second, the ADP was based around a risk assessment tool designed to rule-in heart attacks rather than rule-out.  In the meantime, the team had constructed a purpose build risk assessment tool that in observational studies looked like it could rule out 40-50% of patients.

What is the study just published?

The world’s second randomised controlled trial of assessment of chest pain compared the ADAPT ADP in use (now the control arm) with a new ADP based on the new Emergency Department Assessment of Chest pain Score (EDACS)[the experimental arm].  The only difference between the two arms of the study was the risk assessment tool used. The tool gave a risk score. Patients with a low score, no unusual electrical activity in the heart, and no elevated heart muscle injury proteins in either of two blood samples measured 2 hours apart, were considered low risk.

An important aspect of the study was that it was pragmatic.  This means that the doctors didn’t have to follow the ADP and could decide to send a patient home, or not send them home, based on any factors they thought clinically relevant.  This makes it very tough to run a trial, but it makes the trial more “real life.”

What were the results?

558 patients were recruited.  They all volunteered and are marvellous people.  I love volunteers.

The primary outcome was the proportion of patients safely discharged home within 6 hours.  We assessed safety by looking at all medical events that happened to a patients over 30 days to check to see if any patients discharged home had a major cardiac event that could potentially have been picked up in the ED.

34% of the control arm and 32% of the experimental arm were discharged within 6 hours.  In other words, there was no difference in early discharge rates between the two arms.  The surprising feature of this is that between 2012/3 (when the first trial was run) and 2014/15 the proportion of patients the first ADP ruled out increased from 19% to 34%.  This was unexpected, but pleasing. There were no safety concerns with any patients.

The secondary outcome was simply the proportion each arm of the study classified as low risk (ie not considering whether this led to early discharge or not).  The control (ADAPT ADP) classified 31% and the experimental (EDACS ADP) 42%.  This was a real and meaningful difference which suggests that there is “room for improvement” in early discharge rates as the clinicians become more familiar with the EDACS ADP.

Since 2007 in Christchurch hospital over three times more patients who present with chest pain can be reassured from within the ED that they are not having a heart attack and discharged home (see the infographic).EDACS infographic v2

What was your role?

My role: I managed aspects of the data collection for the later 2/3rds of the patients recruited, did the statistical analysis and co-wrote the manuscript.  In reality, there were a lot of people involved, not least of whom were the wonderful research nurses and database manager who did a lot of the “grunt work”.

What now?

Over the last year all EDs in New Zealand have implemented or in the process of implementing an accelerated diagnostic pathway.  The majority have chosen to use the EDACS pathway.  I am part of a team nationwide helping implement these pathways and monitor their efficacy and safety.

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This study was funded by the Health Research Committee of New Zealand. The work was carried out with the collaboration or the University of Otago Christchurch, Christchurch Heart Institute, and the Canterbury District Health Board Emergency Department, Cardiology Department, General Medicine, and Canterbury Health Laboratories. My salary is provided through a Senior Research Fellowship in Acute Care funded Canterbury Medical Research Foundation, Canterbury District Health Board and the Emergency Care Foundation.

*Not because we have more heart attacks, just an efficient and well funded primary care sector that keeps the very low risk patients out of the ED.

**Featured Image: Creative Commons Share-Alike 3.0 http://tcsmoking.wikispaces.com/heart%20attack

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