Tag Archives: Biomarkers

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.

Cheesecake files: A stadium full

As we’ve been enjoying the World Cup and the Commonwealth Games my latest cheesecake appeared in print online. The topic once more is Kidney Attack biomarkers – those pesky little proteins in the urine that appear when your kidney is injured.  This time I have been getting stuck into some math (sorry) to try and understand what it is that affects when these biomarkers appear in the urine after injury.  I call this a biomarker time-course.  A “Pee Profile” may be a better term but it would never get past the editor.  What I care about is whether the type of biomarker and/or extent of injury, affects the pee profiles.

There are three basic types of biomarkers.  First are those that are filtered from the blood by the two million odd filters in the kidney.  Often they are then reabsorbed back into the blood in the little tubules where the pee is produced – that is, they don’t appear in the urine.  Think of it like a stadium with many entrances.  People (biomarkers) come in and sit down (are reabsorbed).  If, though, a section of the stadium has been fenced off because of broken seating from the previous game (the injury), then some of those entering the stadium may end up exiting it again (the pee biomarkers).  The numbers being reabsorbed and exiting will also depend on whether all the entrances are open – if some are closed then this will have a flow on affect on the rate of people leaving the stadium.

The second are preformed biomarkers.  If we change the analogy slightly, imagine these as people already in the stadium (if the analogy was accurate they would have been born there!).  If some terrible injury happens (like the 4th, 5th, 6th and 7th goals of a now famous football match) some of those people would get up and exit quickly.  The overall rate of exit would reflect on the extent of the injury.

The third, are induced biomarkers.  These are ones that don’t already exist, but are produced in response to an “injury.”  Instead of being biomarkers, let us think of the spectators as produces of these biomarkers and let noise be the biomarker.  There is some background noise of course, but when an “injury” (goal, gold medal performance etc) occurs there is a sudden increase in noise which slowly dies down.  Depending on the team and the number of supporters this will be softer or loader and will carry on for shorter or longer periods (Goooooooooooaaaaaaaaaaaa……lllllllllllll).

The upshot of it all were many coloured graphs and a step towards understanding how we may better make use of the various types of novel biomarkers of kidney injury that have been recently discovered.

PlosOneFigs

_____________

Pickering, J. W., & Endre, Z. H. (2014). Acute kidney injury urinary biomarker time-courses. PloS One. doi:10.1371/journal.pone.0101288

 

 

A new entity is born: CDaR

Have you ever been told the blood test is positive and the disease in question is shocking – Cancer, an STD (but you don’t sleep around!), MS?  Have you every wondered why it is that some drugs get withdrawn years after, and millions of prescriptions after, they were first approved?  Surely, you’ve read a headline that coffee is good for you and chocolate bad, or was that chocolate good and coffee bad or were they both good, or both bad? Probably you’ve read all those headlines.  What does it all mean?  Am I sick or not (I heard some tests falsely give positive results)? Does it matter if I’ve been taking that drug or drinking three cups a day?  The answer to all those questions depends on one thing – clinical data research.  That is, it depends on how we collect the numbers, and what story those numbers are telling us.  Today, I am thrilled to announce that I have had my department’s (Department of Medicine, University of Otago Christchurch) endorsement to establish a new group, Clinical Data Research (CDaR), which will focus on the stories numbers in medicine tell us.

Source: Pickering et al http://ccforum.com/content/17/1/R7

Source: Pickering et al http://ccforum.com/content/17/1/R7

My recent expertise, as readers of this blog may have picked up, is in Kidney Attack (or Acute Kidney Injury). My contribution, as someone with a physics background, has been in data analysis and mathematical modeling.  It has been a privilege to have been involved with many discoveries and helping bring to light the stories of the biomarkers of that disease and the results of a unique randomised controlled trial.  Kidney Attack is a notoriously difficult to detect, and, partly because of that, one that has no effective treatment.  I’m currently working on the story of the association of Kidney Attack with death following surgery with cardiopulmonary bypass.  I am now looking to take those skills and work with other researchers in other medical specialties who generate data and are looking to tell its story (although I will still work on the kidney data!).  I’m particularly keen to engage with more students and pass on some of the data analysis skills I have acquired.  Moves towards open data as well as collection of data in large databases is providing more opportunities to assess the efficacy of health interventions and detect disease risk factors. The prospect of personalised medicine is one of both hope and hype. To sort fact from fantasy in all these areas will require development of new analytical techniques and careful assessment of evidence. This is what I wish to devote the rest of my career to, and to inspire others along the way. John Ioannidis, a highly respected biostatistician, once wrote an essay entitled “Why most research findings are false”  It is a scary thought that many interventions and diagnostic techniques in medicine may be based on biased studies (usually inadvertently biased!). More data will help reduce the bias, if it is treated nicely.  I promise to do my best to treat my data nicely, after all it is your and my health that is at stake.

I posted a few weeks ago my ten commandments of a data culture.  This is the ethos of CDaR.  Below is the lay summary of the new entity.

Group Name:            Clinical Data Research (CDaR)

Department:             Department of Medicine

Institution:                University of Otago Christchurch

Aim: To provide transparent evidence, with the lowest possible risk of bias, of the utility of biomarkers and efficacy of treatments in health or disease.

Lay summary of our aim: We aim to save lives and reduce the burden of disease by applying new ways to collect and analyse clinical data to better diagnose diseases, to predict the course and outcomes of diseases, and to assess how well treatments work.  We do this because we all want the best possible health outcomes for our communities, our families, and ourselves, with the least possible harm done along the way.  We are excited by the new ways scientists, including those at the University of Otago Christchurch, have come up with to measure disease, disease risk, and treatment outcomes. We are also living in an age of unprecedented data generation. To discover both benefits and harm in all this data and to make those discoveries available to all those making clinical decisions requires people dedicated to analysing this data in a transparent and open fashion that exposes both the good and the bad. That is who we want to be and who we want our students to become.

Definition:  A biomarker is any measureable quantity related to disease risk or diagnosis, or disease or health outcomes.

Cheesecake files: Ponting’s last innings

“Only as good as your last match” goes the cliché.  This is true for Ricky Ponting and here is why. I recently published an article1 (Open Access :)) on some new techniques being used in medical research which determine if making an additional measurement improves what we call “risk stratification.”  In other words – does measuring substance X help us to rule in or rule out if someone had a disease or not.  I got a bit board with talking about “biomarkers” and medical stuff, so when it came to presenting this at the Australian New Zealand Society of Nephrology’s annual conference I looked to answer the very important question: “Does Ricky Ponting’s last inning’s matter?”, or in Australian cricket jargon “Ponting, humph, he’s only as good as his last innings, mate.”

How did I do it?

  1. I chose Australia winning a one-day international when chasing runs as an outcome (Win or Loss).
  2. Using data available from Cricinfo I determined which of the following on its own predicts if Australia will win (ie which predicts the outcome better than just flipping a coin): (1) Who won the toss, (2) whether it is a day or night match, (3) whether it is a home or away match, (4) how many runs the opposition scored.
  3. As it turned out if Australia lost the toss they were more likely to win (!), and, not surprisingly, the fewer runs the opposition scored the more likely they were to win.  I then built a mathematical model.  All this means is that I came up with an equation where the inputs were the winning or losing of the toss and the number of runs and the output was the probability of winning.  This is called a “reference model.”
  4.  I added to this model Ricky Ponting’s last innings score and recalculatd the probability of Australia winning.
  5. I then could calculate some numbers which told me that by adding Ricky Ponting’s last innings to the model I improved the model’s ability to predict a win and to predict a loss.  Below is a graph which I came up with to illustrate this.  I call this a Risk Assessment Plot.

So, when the shrimp hit the barbie, the beers are in the esky, and your mate sends down a flipper you can smack him over the fence for you now know that when Ricky Ponting scored well in his last innings, Australia are more likely to win.

The middle bit is the Risk Assessment Plot. The dotted lines tell us about the reference model. The solid lines tell us about the reference model + Ricky Ponting. The further apart the red and blue lines are the better. The red lines are derived from when Australia won, the blue lines from when the lost. If you follow the black lines with arrows you can see that by adding in Rick Ponting’s last innings the model the predicted probability (risk) of a win increases when Australia went on to win (a perfect model would have all these predictions equal to 1). Similarly the predicted probability of a loss gets smaller when Australia did lose (ideally all these predictions would equal 0).

  1. Pickering JW, Endre ZH. New Metrics for Assessing Diagnostic Potential of Candidate Biomarkers. Clin J Am Soc Nephro 2012;7:1355–64.

The Hunting of the SNARF

Some of you may know Lewis Carroll’s classic nonsense poem “The hunting of the Snark”.  Eight men set off with a blank map to find the mythical Snark.

 And the Banker, inspired with a courage so new
          It was matter for general remark,
     Rushed madly ahead and was lost to their view
          In his zeal to discover the Snark

Snarks were dangerous creatures, however

 “For, although common Snarks do no manner of harm,
          Yet, I feel it my duty to say,
     Some are Boojums—”

I dwell in a world where inspired by the new many have rushed on ahead to discover the SNARF (SigNals of Acute Renal Failure).  The hunting of the SNARF has followed contours familiarly trodden and graphically illustrated by a Hype cycle(1).

The Hunting of the SNARF

The Hunting of the SNARF: A Hype Cycle of the hunt for the perfect biomarker of Acute Kidney Injury

It was kickstarted by new technologies called proteomics and genomics which gave the hope that soon would be discovered a rapid, accurate, and, most importantly, early biomarker of Acute Renal Failure (later renamed Acute Kidney Injury, AKI).  This was the beginning of the hype that was driven in no small part by some fantastic early results.  A paper published in the Lancet in 2005 was an important driver in the hype that followed(2).  As with many early studies this involved children and cardiac surgery.  Importantly the biomarker involved almost perfectly distinguished between those who had the disease and those who didn’t (ie not false negatives or false positives).  As the field progressed and more and more studies were investigated across a more diverse range of patient groups and potential AKI causes the ability to discriminate between those with and without the disease became much more modest.  It became apparent that one biomarker to rule them all was not going to be the solution – rather a panel of biomarkers whereby the clinician would choose which biomarkers, if any, to use according to the timing and suspected etiology of the renal injury, the baseline renal function and specific illness of the patient.  We do not yet have such a panel, nor have we conducted sufficient investigations to find if an AKI biomarker(s) adds value to what the clinician can already deduce.  That is partly my job and these are the greater challenges that must drive us up the slope of enlightenment to reach the plateau of productivity where finally we may capture the SNARF.

(1)    Jackie Fenn, “When to Leap on the Hype Cycle,” Gartner Group, January 1, 1995

(2)   Mishra J, Dent CL, Tarabishi R, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. Lancet 2005;365(9466):1231–8.