Tag Archives: health

A vision of kiwi kidneys

Sick of writing boring text reports.  Take a leaf out of Christchurch nephrologist Dr Suetonia Palmer’s (@SuetoniaPalmer) book and make a visual abstract report.  Here are two she has created recently based on data collected about organ donation and end stage renal failure by ANZDATA (@ANZDATARegistry). Enjoy.

Suetonia C-18RfJXUAApRcU

Suetonia C-16lBZXsAERoeM

ps. The featured image is of the Kidney Brothers.  Check out the great educational resources at The OrganWiseGuys.

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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

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.

CMRF_launch

HRC success in Christchurch

The Health Research Council announced Programme and Project grant recipients.  Here’s the list from the Christchurch campus of the University of Otago in which I get a brief mention :).  If others have abstracts of successful grants they’d like posted on this blog, then please let me know.

*****Update: It’s come to my attention that this announcement sent to Uni Otago staff left off the investigator lists investigators who were not current University staff.  I’ve added a few I know about below, but here may be others left out of the list, sorry.  ****

Monday, 9 June 2014.

University of Otago, Christchurch researchers have been awarded more than $8 million of Health Research Council 2014 funding. The results were announced by Minister Steven Joyce at 11.30am today.

The funded projects are:

  • HRC Programme Grant to Professor Mark Richards: Heart Failure: markers and management ($4,980,858).
  • HRC Project Grant to Professor David Murdoch: Legionnaires’ disease in New Zealand: improving diagnostics and treatment ($999,467).
  • HRC Project Grant to Dr Ben Hudson: A randomised controlled trial of nortriptyline in knee osteoarthritis ($1,190,921).
  • HRC Project Grant to Professor Tim Anderson Genetics, brain imaging, and cognitive decline in Parkinson’s disease ($1,178,804).
  • Emerging Researcher First Grant to Dr Tracy Melzer: Imaging markers of imminent cognitive decline in Parkinson’s disease ($149,943).

A summary of each project follows:

HRC Programme Grant to Professor Mark Richards ($4,980,858)

Heart Failure: markers and management

Heart failure (HF) will affect 20% of people now aged 40 years and confers high rates of early readmission and death.  Professor Richards and his team will implement an integrated programme addressing unmet needs in HF including: (1) The IMPERATIVE-HF controlled trial of intensified immediate post-discharge management using special blood tests to individually grade risk and guide intervention with rapid adjustments to treatment to improve outcomes. (2) Testing of candidate kidney damage markers for early warning of this frequent and dangerous complication of HF. (3) Establishing correct sampling times for novel markers for best prediction of early and long term outcomes in HF. (4) Testing our newly discovered markers for early warning of pneumonia complicating HF. (5) Clarification of diagnoses and testing management plans for patients in the Emergency Department with breathlessness or chest pain who do not have clear-cut HF or heart attacks but who nevertheless have elevated blood biomarkers and a poor outlook.

Other investigators are: Prof Vicky Cameron, Prof Richard Troughton, A/Prof Chris Pemberton, A/Prof Miriam Rademaker, A/Prof Chris Frampton, Prof Chris Charles, Dr Leigh Ellmers, Medicine, A/Prof John Pickering, Dr Anna Pilbrow (all University of Otago). Professor Zoltan Endre (University of New South Wales), Dr Martin Than (ED, Christchurch District Health Board), Prof Robert Doughty (University of Auckland), Dr James Pemberton (Cardiology, Auckland District Health Board)

HRC Project Grant to Professor David Murdoch ($999,467)

Legionnaires’ disease in New Zealand: improving diagnostics and treatment

Legionnaires’ disease is a severe type of pneumonia that is under-diagnosed in New Zealand. Special tests are required to make a diagnosis of legionnaires’ disease, but there are no clear guidelines about which patients to test. An enhanced testing system for legionnaires’ disease was developed in Canterbury and has been used there since 2010. The system involves targeted use of the current best test for legionnaires’ disease: PCR(polymerase chain reaction), which detects bacterial DNA. This approach has uncovered many cases of legionnaires’ disease that would have otherwise gone undetected. This study will roll out this same testing strategy across New Zealand for one year in order to measure the national burden of legionnaires’ disease, toimprove patient treatment, to identify cost-effective ways to test for legionnaires’ disease in the future, and to create better guidelines for the treatment of pneumonia.

Other investigators: A/Prof Patricia Priest, Prof Stephen Chambers, Dr Ian Sheerin.

HRC Project Grant to Dr Ben Hudson ($1,190,921)

A randomised controlled trial of nortriptyline in knee osteoarthritis

Osteoarthritis (OA) is a very common and painful condition.  Medicines currently available for treating OA pain are not ideal: they are either inadequately effective or cause unpleasant or dangerous side effects. Recent research has shown how the brain processes pain in OA and this has opened up the possibility of using different types of medicines for OA pain.  Nortriptyline (an antidepressant) has been used to treat persistent pain in other conditions, and other antidepressants may reduce pain in knee OA.  It is not known whether nortriptyline is useful in this condition.  We plan to test this effect by randomly allocating participants to treatment with nortriptyline or placebo and to measure changes in their pain before and after a period on the medication.  We hope that this will tell us whether nortriptyline will be helpful.  If it is, then we believe that many people may benefit from taking this medicine.

Other investigators: Prof Les Toop, Prof Lisa Stamp, Dr Jonathan Williman, Prof Gary Hooper, A/Prof Dee Mangin, Ms Bronwyn Thompson

HRC Project Grant to Professor Tim Anderson ($1,178,804)

Genetics, brain imaging, and cognitive decline in Parkinson’s disease

Many people with Parkinson’s are at risk of dementia but scientists and clinicians have been unable to predict when that will occur. Professor Tim Anderson and his team will do advanced brain scans (MRI and PET) gene testing and clinical evaluations in 85 Parkinson’s patients who have mild cognitive impairments, who are known to be at higher risk, and then determine whether they progress to dementia over the subsequent three years. By identifying characteristics present in the scans and genetic tests of those who develop dementia, compared to those who do not, Professor Anderson and his team can advance understanding of this important issue and establish a useful and reliable tool for researchers and clinicians. It is critical to do this so that preventative treatments to protect against dementia can be targeted at the most appropriate patients when that treatment becomes available and also to select the right ‘at risk’ Parkinson’s patients for trials of new treatments.

Other investigators are: Prof Martin Kennedy, Dr Tracy Melzer, Dr John Pearson.  Prof. John Dalrymple-Alford (University of Canterbury), Dr Ross Keenan (CDHB, Christchurch Radiology Group), Prof. David Miller (University College London)

HRC Emerging Researcher First Grant to Dr Tracy Melzer ($149,943)

Imaging markers of imminent cognitive decline in Parkinson’s disease.

Most Parkinson’s disease (PD) patients eventually develop dementia, which is the most burdensome aspect of this progressively worsening condition.  Mild cognitive impairments often indicate imminent dementia, but the two to 20 year time course poses a major problem for medical interventions, as brain changes associated with dementia in PD are still poorly understood.  Recent evidence suggests that neurodegenerative diseases such as PD progress along discrete brain networks.  One important network, known as the ‘default mode network’ appears particularly susceptible to neurodegeneration. Dr Melzer and his team will examine this network to determine if its disruption can specify which PD patients are vulnerable to progression to dementia within the next two years. A sophisticated but readily available brain imaging technique, called resting state functional imaging, will be used. These measures will assist in the selection of the most suitable patients for new treatments that may delay or prevent subsequent dementia in this vulnerable population.

The other investigator is: Prof Tim Anderson. Prof. John Dalrymple-Alford (University of Canterbury), Dr Ross Keenan (CDHB, Christchurch Radiology Group), Dr Daniel Myell (NZ Brain Research Institute)

 

A letter for all District Health Board Candidates

Dear District Health Board Candidates

Soon I and thousands like me will cast our votes to choose our District Health Boards.  Given the huge budgets of DHBs and the huge potential to influence health outcomes I want more information from you than a couple of paragraphs I received with the voting packs.  Below are two questions I think are important.  As this is an open letter on a blog site, I invite others to submit their questions too.  I also invite you, the candidates, to state your name, the DHB you are running for and your response to my or other posted questions (ie not just the blurb from your pamphlets).

My questions:

1. What single health intervention do you want to see implemented and what evidence do you have that it would be efficacious?

2. What plans have you for increasing patient participation in research?

Regards

Dr John Pickering

Significantly p’d

I may be a pee scientist, but today is brought to you by the letter “P” not the product.  “P” is something all journalists, all lay readers of science articles, teachers, medical practitioners, and all scientists should know about.  Alas, in my experience many don’t and as a consequence “P” is abused. Hence this post.  Even more abused is the word “significant” often associated with P; more about that later.

P is short for probability.  Stop! – don’t stop reading just because statistics was a bit boring at school; understanding maybe the difference between saving lives and losing them.  If nothing so dramatic, it may save you from making a fool of yourself.

P is a probability.  It is normally reported as a fraction (eg 0.03) rather than a percentage (3%).  You will be familiar with it when tossing a coin.  You know there is a 50% or one half or 0.5 chance of obtaining a heads with any one toss.  If you work out all the possible combinations of two tosses then you will see that there are four possibilities, one of which is two heads in a row.  So the prior (to tossing) probability of two heads in a row is 1 out 4 or P=0.25. You will see P in press releases from research institutes, blog posts, abstracts, and research articles, this from today:

“..there was significant improvement in sexual desire among those on  testosterone (P=0.05)” [link]

So, P is easy, but interpreting P depends on the context.  This is hugely important.  What I am going to concentrate on is the typical medical study that is reported.  There is also a lesson for a classroom.

One kind of study reporting a P value is a trial where one group of patients are compared with another.  Usually one group of patients has received an intervention (eg a new drug) and the other receives regular treatment or a placebo (eg a sugar pill).  If the study is done properly a primary outcome should have been decided before hand.  The primary outcome must measure something – perhaps the number of deaths in a one year period, or the mean change in concentration of a particular protein in the blood.  The primary outcome is how these what is measured differs between the group getting the new intervention and the group not getting it.  Associated with it is a P value, eg:

“CoQ10 treated patients had significantly lower cardiovascular mortality (p=0.02)” [link]

To interpret the P we must first understand what the study was about and, in particularly, understand the “null hypothesis.”  The null hypothesis is simply the idea the study was trying to test (the hypothesis) expressed in a particular way.  In this case, the idea is that CoQ10 may reduce the risk of cardiovascular mortality.  Expressed as a null hypothesis we don’t assume that it could only decrease rates, but we allow for the possibility that it may increase as well (this does happen with some trials!).  So, we express the hypothesis in a neutral fashion.  Here that would be something like that the risk of cardiovascular death is the same in the population of patients who take CoQ10 and in the population which does not take CoQ10.  If we think about it for a minute, then if the proportion of patients who died of a cardiovascular event was exactly the same in the two groups then the risk ratio (the CoQ10 group proportion divided by the non CoQ10 group proportion) would be exactly 1.  The P value, then answers the question:

If the risk of cardiovascular death was the same in both groups (the null hypothesis) was true what is the probability (ie P) that the difference in the actual risk ratio measured from 1 is as large as was observed simply by chance?

The “by chance” is because when the patients were selected for the trial there is a chance that they don’t fairly represent the true population of every patient in the world (with whatever condition is being studied) either in their basic characteristics or their reaction to the treatment. Because not every patient in the population can be studied, a sample must be taken.  We hope that it is “random” and representative, but it is not always.  For teachers, you may like to do the lesson at the bottom of the page to explain this to children.  Back to our example, some numbers may help.

If we have 1000 patients receiving Drug X, and 2000 receiving a placebo.  If, say, 100 patients in the Drug X group die in 1 year, then the risk of dying in 1 year we say is 100/1000 or 0.1 (or 10%).  If in the placebo group, 500 patients die in 1 year, then the risk is 500/2000 or 0.25 (25%).  The risk ratio is 0.1/0.25 = 0.4.  The difference between this and 1 is 0.6.  What is the probability that we arrived at 0.6 simply by chance?  I did the calculation and got a number of p<0.0001.  This means there is less than a 1 in 10,000 chance that this difference was arrived at by chance.  Another way of thinking of this is that if we did the study 10,000 times, and the null hypothesis were true, we’d expect to see the result we saw about one time.  What is crucial to realise is that the P value depends on the number of subjects in each group.  If instead of 1000 and 2000 we had 10 and 20, and instead of 100 and 500 deaths we had 1 and 5, then the risks and risk ratio would be the same, but the P value is 0.63 which is very high (a 63% chance of observing the difference we observed).  Another way of thinking about this is what is the probability that we will state there is a difference of at least the size we see, when there is really no difference at all. If studies are reported without P values then at best take them with a grain of salt.  Better, ignore them totally.

It is also important to realise that within any one study that if they measure lots of things and compare them between two groups then simply because of random sampling (by chance) some of the P values will be low.  This leads me to my next point…

The myth of significance

You will often see the word “significant” used with respect to studies, for example:

“Researchers found there was a significant increase in brain activity while talking on a hands-free device compared with the control condition.” [Link]

This is a wrong interpretation:  “The increase in brain activity while talking on a hands-free device is important.” or  “The increase in brain activity while talking on a hands-free device is meaningful.”

“Significant” does not equal “Meaningful” in this context.  All it means is that the P value of the null hypothesis is less than 0.05.   If I had it my way I’d ban the word significant.  It is simply a lazy habit of researchers to use this short hand for p<0.05.  It has come about simply because someone somewhere started to do it (and call it “significance testing”) and the sheep have followed.  As I say to my students, “Simply state the P value, that has meaning.”*

sig

_____________________________________________________________

For the teachers

Materials needed:

  • Coins
  • Paper
  • The ability to count and divide

Ask the children what the chances of getting a “Heads” are.  Have a discussion and try and get them to think that there are two possible outcomes each equally probable.

Get each child to toss their coin 4 times and get them to write down whether they got a head or tail each time.

Collate the number of heads in a table like.

#heads             #children getting this number of heads

0                      ?

1                      ?

2                      ?

3                      ?

4                      ?

If your classroom size is 24 or larger then you may well have someone with 4 heads or 0 (4 tails).

Ask the children if they think this is amazing or accidental?

Then, get the children to continue tossing their coins until they get either 4 heads or 4 tails in a row.  Perhaps make it a competition to see how fast they can get there.  They need to continue to write down each head and tail.

You may then get them to add all their heads and all their tails.  By now the proportions (get them to divide the number of heads by the number of tails).  If you like, go one step further and collate all the data.  The probability of a head should be approaching 0.5.

Discuss the idea that getting 4 heads or 4 tails in a row was simply due to chance (randomness).

For more advanced classes, you may talk about statistics in medicine and in the media.  You may want to use some specific examples about one off trials that appeared to show a difference, but when repeated later it was found to be accidental.

_____________________________________________________________

*For the pedantic.  In a controlled trial the numbers in the trial are selected on the basis of pre-specifying a (hopefully) meaningful difference in the outcome between the case and control arms and a probability of Type I (alpha) and Type II (beta)  errors.  The alpha is often 0.05.  In this specific situation if the P<0.05 then it may be reasonable to talk about a significant difference because the alpha was pre-specified and used to calculate the number of participants in the study.

The Face of Kidney Attack Part III

He didn’t die, quite.  But later thought he may well of.  Steve Gurney’s episode of Acute Kidney Injury (see Part II) didn’t finish him after he was discharged from his third hospital (one each in Malaysia, Singapore and New Zealand) – 4 weeks after the event. While media outlets clamoured to hear the story of this amazing athlete’s brush with death, he had a $92,000 medical bill and was so weak he could barely walk.  He couldn’t return to his own home because it was on a hill and he couldn’t make it up the steep track.

Steve did all the right things.  He began exercising by walking to the letterbox and gradually increased it from there.  He lived on fruit, vegetables, nuts, legumes and meat – nothing pre-processed.  While his body began to be restored, it was the mental anguish – so often hidden from others – that really shook him up.  This from his book “Lucky Legs”:

“I’d gone from top dog in my sport to lowly turtle.  My aim to compete as a mountain biker in the Olympics had disappeared down a mud puddle.  I’d lost 15 kilograms, mostly muscle, there was a possibility of permanent kidney damage and my career as a pro athlete was in question.  My fuzzy mind reasoned that the ‘mat of my expertise’ had been jerked from under my feet now that I had been robbed of my fitness, too.  It was like the bottom had fallen out of my world and I was falling, out of control, with nothing to ground me.  ….The depression went on for six months … death seemed like a realistic solution  … But there was a tiny spark that said, ‘Don’t jump. … hang in there … like a long endurance race …”

Steve’s story of recovery is one of endurance and it is one of reaching out for help.  Some of the help Steve got was from practices which scientifically speaking don’t have a leg to stand on, yet the process of reaching out and talking with people concerned and willing to help was, and is to anyone in similar situations, so very important.  Steve didn’t go for homeopathy, but I’ve been told be someone who acknowledges it is a load of nonsense that they think it valuable to have in the community because of the power of the placebo affect.  She may well be right (needs a study).

Steve wins again

Steve wins again

The story continues and is one of anguish and triumph.  The two time winner of the Coast to Coast returned to it three years after his brush with death and won again, and then won another six years in a row.  Steve’s experiences had strengthened him mentally and focussed him on the things that mattered most to him.  As he said, “Contracting leptospirosis … was a good thing.”

There is an ancient Hebrew concept of health called “shalom.”  Often translated simply as “peace” it is actually much broader than that.  Unlike the common idea of health being merely an absence of illness, it encompasses the notion of being in right relationships – spiritually, physically, environmentally, and communally.  Those of us working in medical science do well to be reminded of shalom.