Category Archives: my work

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). *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.




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.

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.


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


Christchurch Hospital’s latest study: IDENTAKIT-HF

If it weren’t for your kidney’s, where would you be? You’d be in the hospital or infirmary (with apologies to Fred Dagg). The heart and kidneys are not just linked by a pipe, but the health of one is very much dependent on the health of the other. Acute Kidney Injury (AKI) is a phenomenon whereby there is a sudden loss of all or some of the kidneys’ filtration ability. This can have dire immediate consequences with a greater increased risk of mortality & longer hospital stays. It can also increase the risk of developing a chronic kidney disease or even later cardiac problems. Unfortunately, AKI is devilishly difficult to detect, and therefore there are no early treatments. It is also very common – some 4-5% of all hospital patients. Those with heart failure are particularly vulnerable.

IDENTAKIT-HF is a new project all about identifying AKI biomarkers inheart failure. Two weeks ago it enrolled its first patient. It is a collaborative project involving myself, the Christchurch Heart Institute, and a biomarker laboratory in Prince of Wales Hospital, Sydney headed by former Christchurch nephrologist Professor Zoltan Endre. Not only are blood samples being taken from patients with heart failure and potential AKI, but also urine samples. This is because various novel protein markers in the urine appear to respond much more quickly to AKI than markers in the blood. It is now recognised, that not one marker, but a panel of markers is needed to identify AKI and provide information about how to target any treatments. IDENTAKIT-HF will identify the likely members of such a panel and then test if they really do identify the disease and predict its course. This will form the platform for future intervention trials to develop treatments and improve patient outcomes.

Cantabrians, this is your life

There is little more precious than our health and that of those we love. “Research saves lives” is  Canterbury Medical Research Foundation’s (CMRF) proudly held motto. The CMRF has been supporting the people of Canterbury for 55 years thanks to the generosity of Cantabrians. In that time they have funded more than $22 million in grants.  Yesterday I attended the launch of their new logo and branding.  The logo depicts a medical cross and the four avenues of Christchurch.  This new logo is intended to signal CMRF’s intention to be fresh and more external facing with a broader appeal to the Canterbury donating community and a bigger emphasis on  partnerships with other funding organisations to leverage money to best effect.  My own fellowship, jointly funded by the CMRF, the Emergency Care Foundation, and the Canterbury District Health Board is an example of that.  CMRF are also expanding the breadth of research they will fund and are now working to expand their influence in the translational, population health and health education spaces. Their vision is to be giving $2 million in grants per annum within 5 years.  What a great boost that will be to Canterbury. A key partner largely funded through CMRF is the NZ Brain Research Institute – their logo has also changed to mirror that of CMRF.


Cheesecake files: Just how deadly is it?

Everyone said it did, but how did they know and by how much?  Statements like

“The development of AKI [Acute Kidney Injury] after CPB [Cardiopulmonary Bypass Surgery] is associated with a significant increase in infectious complications, an increase in length of hospital stay, and greater mortality.” (Kumar & Suneja, Anaesthesiology 2011 14(4):964)

are common place in the acute kidney injury literature.  When I started to look at the references for such statements I realised that they were all to individual, normally single centre, studies and that the estimates of the increased risk associated with AKI after CPB varied considerably.  Furthermore, the way AKI is defined in these studies is quite varied. This lead to two questions?

  1. Just how deadly is getting AKI after CPB?
  2. Does it matter how we define AKI in this case?

These questions are important as the answer to them helps a surgeon and patient to better assess the risk associated with choosing to have cardiopulmonary bypass surgery and what the importance is in monitoring kidney function after such a surgery.  To answer these questions required a meta-analysis the results of which I have just published (a.k.a earned a cheesecake).  A meta-analysis involves systematically searching through the literature, a sentence which takes seconds to write but months to serve, for all articles reporting an association between AKI and mortality after CPB.  Then there is learning how to put all the, sometimes disparate, data together (I had to learn a lot of R for this one) and to report on it.  As this was my first meta-analysis, I was fortunate to have the assistance of two highly competent scientists & nephrologists with meta-analysis experience, namely Dr’s Matt James of Calgary, and Suetonia Palmer of my own department in the University of Otago Christchurch.

So – what did we find?

  1. If you get AKI after CPB you about 4 time more likely to die compared to if you do not get AKI after CPB even after accounting for things like age, diabetes, and other risk factors.
  2. Somewhere between 37 and 118 lives per 10,000 CPB operations could be saved if we could find a way to eliminate AKI.
  3. How AKI was measured did not make any difference to the results.
  4. AKI after CPB was also associated with increased risk of stroke.
Figure 1 from Pickering et al, AJKD 2014

A teaser of a figure from Pickering et al, AJKD 2014

Pickering, J. W., James, M. T., & Palmer, S. C. (2014). Acute Kidney Injury and Prognosis after Cardiopulmonary Bypass: A Meta-analysis of Cohort Studies. American Journal of Kidney Diseases : the Official Journal of the National Kidney Foundation. doi:10.1053/j.ajkd.2014.09.008

ps. Sorry about the paywall folks, but as I’ve said before, if we want to put this data in front of the people it is most relevant to we haven’t the budget to always make them Open Access.