Tag Archives: cheesecake

Cheesecake files: Of bathtubs and kidneys

Sitting in the bathtub you notice that there is a slow leak around the plug.  You adjust the taps to maintain a flow of water that exactly counteracts the loss due to the leak; the water level stays constant.  This is called a steady state and the same thing happens with out kidneys and the molecule used to assess their function.  Our bodies generate creatinine at a constant rate which finds its way into the blood.  Under normal circumstances our kidneys excrete that creatinine into the urine at the same constant

rate.  The creatinine concentration in the blood, therefore, stays constant.  When our kidneys get injured (as they very often do in hospitalised patients) this is like plugging the leak.  Just as the water level in the bathtub would rise slowly – undetectable at first – so too does the creatinine concentration rise slowly.  It normally takes a couple of days to be noticed.  Most of my work has been about trying to detect this injury to the kidney early.  However, if the kidneys start to recover then excess creatinine is only slowly cleared from the blood by the kidney – a process that similarly can take a day or two before it is detected.  Just as not knowing if the kidneys have been harmed makes treatment and drug dosing difficult for the nephrologists and intensivists, so too is not knowing if they have recovered.  My latest publication (aka a cheesecake file) that has appeared in press presents a simple tool for the physicians to try and determine if kidney function has recovered after having been compromised.

This particular piece of work began when a St Louis Nephrologists (a kidney doc), Dr John Mellas, contacted me to say that although a manuscript of his had been rejected by reviewers, he thought there was merit and could I help him (he found me through a search of the literature).  I confessed to being one of the reviewers who had rejected the manuscript!  Fortunately, John was forgiving.  His problem was that he was called in to the intensive care unit to look at a patient with high blood creatinine concentration.  Should he put the patient on dialysis or should he wait?  If he knew if the kidney was already recovering, then he would be less likely to put on dialysis. We talked about the issue for a while and eventually settled on a possible tool which we could test by looking at the behaviour of creatinine over time in abut 500 patients in the ICU.  The tool is quite simple.  It is the ratio of the creatinine that is excreted to the creatinine that is generated.  If more creatinine is being generated than excreted then probably the kidney function is still below normal, however, if more is excreted than generated then probably the kidney is recovering.  The difficulty is that there is no way to measure in an individual what the creatinine generation is.  We ended up using equations based on age, sex, and weight to estimate creatinine generation.  This is a bit like using an equation which takes into account pipe diameter, mains water pressure, and how many turns of the screw the tap has had to determine the rate of water flow.  Creatinine excretion, though, can be easily measured by recording total urine production over several hours (we suggest 4h) and multiplying this by the concentration of creatinine in the urine.

We discovered that by using the ratio between estimated creatinine generation and creatinine excretion we were able to tell in most patients if the kidney was recovering or not.  My hope is that physicians will test this out for themselves.  The good thing is that it requires only minimal additional measurements (and costs) beyond what are already made in ICUs, yet may save many from expensive and invasive dialysis.

Pickering, J. W., & Mellas, J. (2014). A Simple Method to Detect Recovery of Glomerular Filtration Rate following Acute Kidney Injury. BioMed Research International, 2014. doi:10.1155/2014/542069

 

Cheesecake files: Too little pee

This week’s post is really about the coloured stuff & why too little of it is dangerous.  Note, I say coloured stuff because it aint just yellow – check out this herald article if you don’t believe me (or just admire this beautiful photo).

 A rainbow of urine from a hospital lab. Credit:  laboratory scientist Heather West.

A rainbow of urine from a hospital lab.
Credit: laboratory scientist Heather West.

Story time

A long time ago, when Greeks wore togas, and not because they couldn’t afford shirts, a chap named Galen* noted that if you didn’t pee you’re in big trouble.  It took 1800 more years before the nephrologists and critical care physicians got together to try and decide just how much pee was too little.  This was at some exotic location in 2003 where these medics sat around for a few days talking and drinking (I’m guessing at the latter, but I have good reason to believe…) until they came up with the first consensus definition for Kidney Attack (then called Acute Renal Failure, now called Acute Kidney Injury)1.  It was a brilliant start and has revolutionised our understanding of just how prevalent Kidney Attack is.  It was, though, a consensus rather than strictly evidence based (that is not to say people didn’t have some evidence for their opinions, but the evidence was not based on systematic scientific discovery).  Since then various research has built up the evidence for or against the definitions they came up with (including some of mine which pointed out a mathematical error2 and the failings of a recommendation of what to do when you don’t have information about the patient before they enter hospital3).  One way they came up with to define Kidney Attack was to define it as too little pee.  Too little pee was defined as a urine flow rate of less than half a millilitre per kiliogram of body weight per hour over six hours (< 0.5ml/kg/h over 6h).  Our groups latest contribution to the literature shows that this is too liberal a definition.

The story of our research is that as part of a PhD program Dr Azrina Md Ralib (an anaesthesist from Malaysia) conduct an audit of pee of all patients entering Christchurch’s ICU for a year.  She did an absolutely fantastic job because this meant collecting information on how much every patient peed for every hour during the first 48 hours as well as lots of demographic data etc etc etc. Probably 60-80,000 data points in all!  She then began to analyse the data.  We decided to compare the urine output data against  meaningful clinical outcomes – namely death or need for emergency dialysis.  We discovered that if patients had a flow rate of between 0.3 to 0.5 ml/kg/h for six hours it made no difference to the rates of death or dialysis compared to those with a flow rate greater than 0.5.  Less than 0.3, though, was associated with greater mortality (see figure).  For the clinician this means they can relax a little if the urine output is at 0.4 ml/kg/h.  Importantly, they may not give as much fluid to patients. Given that in recent times a phenomenon called “fluid overload” has been associated with poor outcomes, this is good news.

The full paper can be read for free here.

Proportion of mortality or dialysis in each group. Error bars represent 95% confidence intervals.From Ralib et al Crit Care 2012.

Proportion of mortality or dialysis in each group. Error bars represent 95% confidence intervals.From Ralib et al Crit Care 2013.

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*Galen 131-201 CE.  He came up with one of the best quotes ever: “All who drink of this remedy recover in a short time, except those whom it does not help, who all die.”

1.     Bellomo R, Ronco C, Kellum JA, Mehta RL, Palevsky PM, Acute Dialysis Quality Initiative workgroup. Acute renal failure – definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care 2004;8(4):R204–12.

2.     Pickering JW, Endre ZH. GFR shot by RIFLE: errors in staging acute kidney injury. Lancet 2009;373(9672):1318–9.

3.     Pickering JW, Endre ZH. Back-calculating baseline creatinine with MDRD misclassifies acute kidney injury in the intensive care unit. Clin J Am Soc Nephro 2010;5(7):1165–73.

Cheesecake files: Injury, function, and death

When they say your tests are positive for a disease just what do they mean?  If it is a simple blood or urine test often they mean that the concentration measured is outside (above or below) some  reference range.  In my field of Kidney Attack (a.k.a. acute kidney injury: AKI) two tests of the same substance (plasma/serum creatinine) are needed a day or two apart . The difference in the concentrations is what is important.  If the creatinine concentration has increased by >0.3 mg/dl within 48 hours or by more than 50% within a week then the diagnosis of AKI is made.  What happens, though, when someone comes along with a new test?  How do we know it is any better (or worse) than the original test? In my view what is required is that both the old and the new tests should be compared to a third, clinically relevant, variable.  For example, a new prostate cancer test may be compared to the present (poor) PSA test  by referencing both to the more definitive biopsy results.

In AKI the reason the creatinine threshold of 0.3 mg/dl was included as diagnostic was because research(1) had shown this level of increase to be associated with a four fold increase in the likelihood of premature death. If you’ve seen any of my previous posts on my research you will know that I am interested in new biomarkers (plasma and urine proteins mainly) that could be used to diagnose AKI earlier than creatinine.  While creatinine is a marker of changes in kidney filtration function, most of these new biomarkers reflect structural injury itself.  An analogy is that movement of a finger hurt in a rugby tackle tells us if the finger is functioning, whereas an x-ray is needed to tell us if it is broken or not.

Sam Whitelock damaged his finger during a game.  It had enough function to let him continue to play.  X-rays later showed it was broken. Picture: TV3

Sam Whitelock damaged his finger during a game. It had enough function to let him continue to play. X-rays later showed it was broken.
Picture: TV3

 

My latest publication(2) describes a method to determine appropriate biomarker thresholds.  It is quite simple.  First, I determine the sensitivity of the creatinine threshold to predict a meaningful clinical outcome – the need for dialysis or death within 30 days. The sensitivity is simply the proportion of all those who end up having the outcome who had a measure above the threshold.  I then take that sensitivity and work out what the biomarker threshold needs to be in order to yield that same sensitivity.

An early sketch of mine as I worked out how to determine structural biomarker thresholds

An early sketch of mine as I worked out how to determine structural biomarker thresholds

(1) Chertow, G. M., Burdick, E., Honour, M., Bonventre, J. V., & Bates, D. (2005). Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. Journal of the American Society of Nephrology : JASN, 16, 3365–3370. doi:10.1681/ASN.2004090740

(2) Pickering, J. W., & Endre, Z. H. (2013). Linking Injury to Outcome in Acute Kidney Injury: A Matter of Sensitivity. PloS one, 8(4), e62691. doi:10.1371/journal.pone.0062691.t001

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.

Cheesecake files: Public or Perish

Today’s stories: The death of a definition, Diluted pee, and No trials for kids. I kill three birds with one blog to catch up with my “2012 publications” and keep my promise to be public about what I research.  If you get through these, I’d appreciate any feedback on whether or not I have achieved the goal of “plain language”?   If you’ve not read about what I do before you may want to check out my “I am a pee scientist” post on acute kidney injury (AKI).

The death of a definition

 Nejat M, Pickering JW, Devarajan P, et al. Some acute kidney injury biomarkers are increased in pre-renal AKI. Kidney Int 2012; 81(12); 1254-1262. Open Access: http://www.nature.com/ki/journal/vaop/ncurrent/full/ki201223a.html

We think this is so important we paid US$3000 for this to be Open Acess (available to anyone).  So, what’s so important?  For decades there has been a syndrome that goes by various names notably “pre-renal renal failure” [ugly name!] and “pre-renal azotaemia.”  It is characterized by a short duration increase in our surrogate marker for acute kidney injury (a surrogate marker “stands in the place” of a direct measurement which can not be performed) and the preservation of the kidney’s handling of sodium (important to keep our cells from either swelling or shrinking).  Importantly, it has been assumed that no real damage is done.

In this paper we show that there really is injury in this syndrome.  We did this by looking at 6 molecules that appear in the urine when there is damage to the kidney.  We concluded that “pre-renal renal failure” is merely a mild, though clinically significant, form of AKI. We invested in making this open access as our investigation has the potential to change the paradigm and clinical practice.  As these biomarkers enter clinical practice even a small injury will be recognized as needing to be taken notice of by the attending doctors.  Just how they will do this is another issue.

Diluted pee

Ralib AM, Pickering JW, Shaw GM, et al. Test Characteristics of Urinary Biomarkers Depend on Quantitation Method in Acute Kidney Injury. J Am Soc Nephrol 2012;23(2):322–33. Abstract http://jasn.asnjournals.org/content/23/2/322.abstract

How do you know if they have watered down your beer? Some of you won’t because you’re too sloshed to notice or you drink Budweiser anyway (and nothing is more watered down than that!).  For the rest of you it is a vitally important question.  Drinking beer also waters down your pee (I do hope you noticed!).  So does drinking coffee.  Doing lots of exercise has the opposite effect.  When it comes to measuring the little proteins that get excreted into our urine we need to know just how much watering down has gone on.  We measure the concentration of these blighters and if there is lots of water present the concentration is lower than it otherwise may have been (and vice versa) which will affect just how well these proteins detect AKI.

In this article we tested two methods of accounting for the dilution effect and compared them against not bothering to account for it.  One method involved measuring the concentration of another protein, which is supposedly proportional to the water concentration, and dividing all our results by that concentrate. The other involved measuring the urine output volume over 4 hours and working out an excretion rate (amount per minute) for each of our protein biomarkers.

Suprisingly, the best way to use the biomarker as a tool to diagnose AKI was to use just the concentration and not to try and account for water at all!  However, the best way to use it to try and predict those who may need dialysis or who may die was to use the concentration divided by the urinary creatinine concentration.  The paper goes on to offer an explanation as to why we think this is the case.

We also came up with a means to estimate the total biomarker excreted (total mass) over a time period (in our case 24 hours) and found that this was associated with how long people are likely to stay in the intensive care and likelihood of dying in the first week.  This suggests that we may use this total excretion as an outcome measure for intervention trials aimed at reducing the injury to the kidney.

No trials for kids

Endre ZH, Pickering JW. Acute kidney injury clinical trial design: old problems, new strategies. Pediatr Nephrol [Internet] 2012; On line ahead of print. Abstract http://www.springerlink.com/content/j876512550442u94/

This appeared online just last week.  Professor Endre and I do a lot of work together.  He’s a Nephrologist and Scientist and I do the numbers.  In this case we had been asked to write a review for the journal Pediatric Nephrology.  We searched for clinical trials of acute kidney injury which compared a proposed new treatment to either an existing treatment or a placebo.  We included only those trials where there were more than 30 patients and where they had been assigned randomly to either the treatment or placebo group (so called Randomised Controlled Trials).  We found only 49 trials – believe me, this is a very very small number.  Only 1 of those trials was in children!  Some of those trials tried to prevent AKI from happening by giving the patients something before the likely insult to the kidney (eg by giving them a drug or placebo before they underwent heart bypass surgery), some gave them the drug or placebo only a day or two after the insult when it was detected by the slow (normal) method of detection and a few gave it when injury was either anticipated as having just occurred, as in one trial (our own) there was an early measure of injury.   Very few of the trials reported a positive outcome and none were outstanding.  The trials used a wide variety of measuring just what a positive outcome meant – this is a long standing problem in nephrology with definitions of AKI and one we have been trying to work on.  We went on to make several recommendations for future trial design.

Cheesecake files: Keeping a promise 1

I believe in open access and the right of the public to know what I am doing.  Putting my money where my mouth is, is another story.  When I started this blog in January I promised myself to write something every time an article of mine appeared in print. That’s happened three times already this year and I’ve yet to fulfill that promise…so this is the first of several posts (promise).

Ideally all my research would be freely available online as soon as it has been through the peer review process.  Unfortunately, that costs a lot of money which few research budgets can meet (in the journals I publish it typically costs an extra US$3000 above and beyond normal page charges of around $70 per page and $500 per colour figure).  Nevertheless, this year I have managed to make two articles “Open Access” and another is on the way.   The one I have chosen today is my first book chapter in the field of Acute Kidney Injury.  I received an invite to contribute to a book  and responded positively for a change – for a reasonable cost (US$1000) it was an opportunity to produce a longer treatise on an important area of my work and to make it freely available to anyone and everyone.

 Pickering JW, Endre ZH. The Metamorphosis of Acute Renal Failure to Acute Kidney Injury [Internet]. In: Sahay M, editor. Basic Nephrology and Acute Kidney Injury. Rijeka: InTech; 2012. p. 125–49.

Freely downloadable from: http://www.intechopen.com/books/basic-nephrology-and-acute-kidney-injury/the-metamorphosis-of-acute-renal-failure-to-acute-kidney-injury

The story begins with a lament as to the repeated failure of clinical trials to discover any effective therapy for Acute Kidney Injury (AKI).  We then discuss the history of how the thinking has changed from Acute Renal Failure – the idea that the kidney filtration function is suddenly reduced – to Acute Kidney Injury – the idea that the kidney tissue is injured which often results in a reduction in function.  For the mathematically minded there is a section on how to determine the function of the kidney on the basis of the concentrations of a marker (plasma creatinine) in the blood.  Those who prefer words to symbols, though, can skip this.  We discuss the current definitions of Acute Kidney Injury (still based on function!  – That is soon to change…watch this space), then I introduce three things important to clinical trials.

  1. Although AKI is associated with higher mortality rates it is financially ruinous to run a trial with mortality as an outcome because of the very high numbers of patients needed.  For that reason, a surrogate for kidney function is used.  Often this has been a definition of Acute Kidney Injury that is categorical – ie the plasma creatinine concentration increases by more than 50% you have AKI, if it doesn’t, you don’t.  A trial will then compare the proportions of patients in the placebo and treatment groups with AKI.  A couple of years ago I published an article in which I demonstrated that such a categorical trial outcome was not the best idea – better was to use a continuous measure of the change of creatinine that takes into account the duration as well as the extent of that change (I called this the RAVC). I explain this in the chapter.
  2. When we use plasma creatinine to judge kidney function, we need to know what the concentration was prior to someone ending up in intensive care (ie we are interested in the change from a baseline).  About half of patients have a suitable measurement on record.  What do we do about the other half?  I present a way of dealing with the problem from a clinical trial perspective.  Previously I had shown that the first recommendations given to solve this problem were no better than using a random number generator.  There are some more clinically relevant (and less mathematical!) ways of determining baseline creatinine.
  3. Finally I deal, a little (for this is an ongoing saga) with how to use the new injury biomarkers (often little enzymes measured in the urine – see “I am a pee scientist”).  Ideally, we would initiate therapy (or placebo) on the basis of measured injury even before we were able to detect a change in function.  My colleague (Prof Zoltan Endre) was the first to attempt a randomized control trial based on just such a measurement.  I was brought in to manage the numbers and since then have managed to show that it isn’t quite as easy as we hoped…hence the ongoing saga

I hope that wasn’t too boring.  I think the chapter is pretty accessible to most, and what’s more anyone can download it.  If you do do that, let me me know just if it was understandable at all.

Addendum: One of the nice things about Open Access is that people from all over the world get access to your article – according to http://www.intechopen.com/statistics/29478  one person from the Dominican Republic, 2 from Macedonia, 8 from Poland, and 9 from New Zealand have downloaded this book chapter…cool.