Monthly Archives: March 2013

Are we getting safe medicines?

Do you read the small print about side effects?  Does your doctor tell you?  What are the chances that taking a medicine will kill you or make you ill in an entirely new way?

This last question is one reason why trials are run before a medicine is approved for use.  However, there is an inherent flaw in the system.  No matter how many people are in a trial there is a chance that a side effect will be missed.  Consider this: Imagine choosing one school class out of all the classes in the country to check the prevalence of albinism.  Given only about 1 in 17,000 people have albinism then you can imagine that it is unlikely that you will find an albino person.  However, you may find a red haired child because the prevalence is much higher.  If, though, you check a whole school you may still not find albinism.  Can you, then, conclude there is none?  No, because even if the school has 2000 pupils there is only a small chance of finding the condition.  Quite simply, the rarer the condition the more pupils need to be checked.  In terms of drugs, the rarer the side effect the more people need testing.

However, the difference between new drugs and our analogy is that trials are looking for unknown side effects.  What this means, is that a statistician can turn things around and say that for a trial of a given size if a side-effect is not seen what is the maximum prevalence of that side effect.  If, for example, you only cared if the drug increased the risk of a side-effect by more than 5 times (Relative Risk = 5) compared with those not taking the drug, and the event was relatively rare (say 1 in 5000), then you would need to have a trial with 14,707 people taking the drug and another 14,707 in a control group (e.g. taking a drug already used to treat the disease) in order to be reasonably certain that the new drug did not increase the risk by more than 5 times (see the table).  The side-effect you are interested in may be serious (eg kidney cancer), but if the drug is saving many lives in the first place (eg a drug that suppresses the immune system and allows transplants to take place), then it may be an “acceptable” risk.  The point is that trials must be of sufficient size to measure an “acceptable risk.”

SampleSizeIn a recent article published in PLOS Medicine (see here) researchers looked at the number of participants in trials of drugs approved by the European Medicines Agency over the last decade.  Quite shocking is that for medicines intended for long term use (Chronic diseases) nearly 20% were approved even though they did not meet the Agency’s own criteria for numbers of patients in studies, and these were very very low numbers indeed (300 over 6 months)!  Only about 10% of studies had sufficient participants to pick up on a risk of greater than 5 fold with an incidence of more than 1 in 1000.  This highlights why it is absolutely imperative that there are further ongoing studies monitoring side effects of drugs once the drug has been approved.  Anyone who has read Ben Goldacre’s book “Bad Pharma” will know such studies are often poorly done if done at all. How do we ensure such studies are done?  Do we legislate that drug companies do them (potential bias here), or do we make sure we have a well trained, adequately funded independent group of scientists able to do this?  If you think the latter, let your MP know!  In the meantime, let the medicated beware.

Love this info graphic


World Kidney Day — March 14 this year — is a worldwide awareness campaign that aims to draw attention to kidney disease. Yet another awareness campaign? Yes, we’re afraid so. But this one isn’t pointless. Kidney disease affects a lot of people throughout the globe, and around 10 percent of US adults.

Because the symptoms are initially so vague, many have no idea they suffer from kidney disease until it has reached a serious stage and complications can no longer be prevented. Could you be one of them? Check this infographic out, and ask yourself some serious questions.

The symptoms of a kidney disease are initially so vague, many have no idea they suffer from it until it has reached a very serious stage. Are you one of them? Take a look at this infographic and ask yourself some serious questions!

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Happy WKD

I love living in NZ, it enables me to be the first in the world to wish everyone a happy World Kidney Day.  May your kidneys never lack oxygen, be always filtering, and ever distant from the nephrologists biopsy needle!

Let me remind you:

 If it weren’t for your kidneys where would you be

You’d be in the hospital or mortuary

If you didn’t have functioning kidneys

(with apologies to John Clarke)

Better, take a look at this video too (from

This year’s theme for World Kidney Day is “Kidneys for Life: Stop Kidney Attack.”  If you’ve not caught up with my myriad of other posts, Kidney Attack (aka Acute Kidney Injury) is the rapid loss of kidney function and/or structural damage brought about by toxic damage to the kidneys or temporary loss of blood to the kidneys.

This week I published a blank post entitled “A list of effective treatments for Kidney Attack.”  There is no known treatment – merely acute dialysis, a support for the kidneys, not a treatment. There is no treatment because detection is delayed and difficult and because not enough research has been done.

The good news is that I and many others around the world are engaged in finding new ways of detecting this disease.  Before I list some of the good news I want you all to repeat after me “30,000 kidney attacks a year in New Zealand, 1300 deaths.”  If you live out of New Zealand you may say “Two million die of Kidney Attack each year.”  Now tell someone else … anyone … the next person you see (not your boss if you read this at work).  Well done, thank you.

So, for some good news:

Hooray – we have for the first time means of measuring structural damage to the kidneys.  For us, this is the X-ray moment.  Imagine life before the X-ray – all that could be said is that you could no longer bowl a bouncer (throw a curve ball), play the piano, or dance a jig (whatever that is).  In other words, all that could be said was function was lost.  With the X-ray actual injury to the bone could be observed.  Importantly, it could be observed before function was lost permanently.  The measurement of various molecules we make in the urine are to us like the X-ray – they are measures of injury to the kidney (we call them biomarkers).

We are busy investigating how best to use these biomarkers and have been discovering:

  • which are best after Cardiac surgery, Contrast procedures or in the ICU (all risk factors for Kidney Attack),
  • what the optimal timing is for measurement of each biomarker,
  • how to use the biomarkers in Randomised Controlled Trials aimed at testing new treatments,
  • which biomarkers are best for detecting Kidney Attack when someone has additional co-morbidities like sepsis, and
  • which biomarkers add the most value to what we already know and enable the best assessment of risk of poor outcomes.

In the meantime, some of my work has shown how we can better utilise the information we already have with urine output and the mainstay of nephrology, the plasma creatinine measure:

  • the discovery that even when creatinine does not change after Cardiac Arrest there is likely to be Kidney Attack (it had been thought that it was only when creatinine was elevated there was a problem),
  • a combined measurement of plasma & urine creatinine and urine flow rate (called creatinine clearance) over a short period of time in the ICU helps identify Kidney Attack patients otherwise missed,
  • how best to estimate someone’s “normal renal function” so that a judgment can be made if it has recently changed, and
  • how best to utilise creatinine in Randomised Controlled Trials to tell if an intervention is improving kidney function.

All these add up to progress.  My own and my group’s work over the last 6 years has received funding from a number of funders (see logos attached) some of which originate with your tax dollar – hence my commitment to keep the tax payers informed. I am indebted to my boss, Professor Zoltan Endre, not only did her hire me (I think he mistook Physicist to mean Physician!), he has taught me heaps and consequently we have formed a strong collaboration. Our work has also depended on the good staff of Dunedin and Christchurch Hospital ICU’s, Christchurch Emergency Department, and the Canterbury Health Laboratories.  Without the commitment to research these people make, progress would not have been made.  Most important are the patients or their families who have consented for us to take extra samples or enroll them in a trial. The decision to participate is often made at a difficult time – families wrestling with issues of possible death or long term health issues of their loved ones.  I salute them.  I thank them.  New hope, new medicines, new tests, and new procedures are built on the courage and generosity of the patients and families who participate in research.

Sponsors who have provided grants (top row), or run assays (middle row), or provided free accommodation (me!) for the Christchurch Kidney Research Group, University of Otago.

Sponsors who have provided grants (top row), or run assays (middle row), or provided free accommodation (me!) for the Christchurch Kidney Research Group, University of Otago.

Diabetes in NZ – new scary data

If this doesn’t scare you, you are an Ostrich.  Otago University researcher Dr Kirsten Coppell has released new data on the prevalence of diabetes in New Zealand.  See here for the press release.

Basic data:

  • 7% of New Zealanders over the age of 15 have diabetes
  • 18.6% have pre-diabetes which typically leads to Type II diabetes (therefore the prevalence is likely to go higher than 7%).
  • The pre-diabetes prevalence increases with age – it was 45% in 55-64 year age group.

For those interested in reading the research, it can be found in the NZ Medical Journal.  NZMJ 1 March 2013, Vol 126 No 1370; ISSN 1175 8716  URL:  Dr Coppell kindly sent me a copy (*I’ve made a few more observations about the details of the study for those who are interested below).

In the meantime, this is rightly hitting the headlines.  We should be afraid, very afraid.  Our politicians must stop arguing over that which is petty (like selling less than half of a small fraction of our assets) and get focussed on what matters.  Next year is election year – we should demand a comprehensive diabetes policy from each political party.  Below is a letter I wrote to the Christchurch Press prior to the last election – not much has changed.  As for you – you can stop attacking the sugar – you don’t need it and it may kill you.  Beware of “fat free” food which substitutes sugar instead.  Get some advice – see your doctor.  Don’t become a statistic in the next survey.

As for the link with my work (Kidney Attack a.k.a. Acute Kidney Injury), the little diagram explains.Diabetes AKI



*  The study was a representative sample of New Zealanders.  The study size was large (for an NZ study) – 4,721.

From the results

Overall the prevalence of diabetes was 7.0% (95% CI: 6.0, 8.0). Diabetes was more common among men (8.3%; 95% CI: 6.4, 10.1) compared with women (5.8%; 95% CI: 4.7, 7.0). The prevalence of diagnosed diabetes was 6.0% (95% CI: 4.5, 7.5) among men and 4.0% (95% CI: 3.1, 4.8) among women, and the prevalence of undiagnosed diabetes was 2.1% (95% CI: 1.2, 3.0) among men and 1.5% (95% CI: 1.0, 2.0) among women.

Scary for me is the percentage of undiagnosed diabetes.  This represents tens of thousands of New Zealanders!

Tables in the paper show how the prevalence increases with age and body mass index and that there are marked differences according to ethnicity.  One third of Pacific people over the age of 45 had diabetes, yet about 40% of this was undiagnosed diabetes!

By the way – 95% CI with two numbers following means a that the 95% confidence interval for the prevalence is between the two numbers.  What this means is that there is a 95% chance that confidence interval contains the true prevalence (which can only be known if everyone is measured).  Eg There is a 95% chance that the 6% to 8% confidence interval contains the true prevalence of diabetes (note – 7% should be thought of as an estimate).