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Posts Tagged Acute Kidney Injury

Cheesecake files: Just how deadly is it? John Pickering Nov 27

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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.

 

Tagged: Acute Kidney Injury, Acute Renal Failure, AJKD, AKI, Cardiac Surgery, Cardiopulmonary Bypass, CPB, death, Dialysis, Kidney, RRT

Can Doctors and Nurses help Dialysis patients recover? John Pickering Nov 07

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In the case of dialysis dependent acute kidney injury patients this is a question which Dr Dinna Cruz  and colleagues (University of California San Diego) are asking and seeking opinions from both nephrologists and non-nephrologist doctors and nurses involved in care of dialysis patients.  It was a question which arose out of discussions at this year’s Continuous Renal Replacement Therapies conference (CRRT 2014). Personally, I think it is a brilliant starting point for research to go out and seek the opinion of those “at the coal face” actually treating patients. If that includes you, please take a moment to complete the survey. If it includes someone you know, please pass this request to participate on.  Here is Dr Cruz’s request:

Currently there is much interest regarding the recovery aspect of AKI. A specific area of interest is how to enhance recovery in patients who remain dialysis-dependent at the time of discharge. It is hypothesized that patients with potential for renal recovery may require a different care plan than the “usual” ESRD patient.

Therefore we are asking your opinion regarding the post-discharge care of such patients, using this short survey. It will take only a few minutes of your time, and represents a starting point for developing potential strategies for these patients. We think it is very important to have the input of specialists from different healthcare settings and countries to give a more balanced view.

Kindly complete the survey appropriate for your specialty, then please share both these links with other colleagues so we get more responses from around the world

For nephrologists:

https://www.surveymonkey.com/s/postdischAKIcare_neph

For non-nephrologists, including acute and chronic dialysis nurses:

https://www.surveymonkey.com/s/postdischAKIcare

Thank you very much for your help!

Source: Anna Frodesiak-Wikimedia Commons

Source: Anna Frodesiak-Wikimedia Commons

Tagged: Acute Kidney Injury, Acute Renal Failure, chronic kidney injury, Dialysis, nephrologists, Nephrology, nurses, Research, survey, UC San Diego

Cheesecake files: A stadium full John Pickering Aug 06

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

 

 

Tagged: Acute Kidney Injury, Biomarkers, Kidney, Kidney Attack, Urine

Cheesecake files: A stadium full John Pickering Aug 06

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

 

 

Tagged: Acute Kidney Injury, Biomarkers, Kidney, Kidney Attack, Urine

Cheesecake files: Of bathtubs and kidneys John Pickering Jun 11

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

 

Tagged: Acute Kidney Injury, AKI, cheesecake, Creatinine, Dialysis, Intensive Care Unit, Kidney, kidney function, Nephrology

Does being unconscious mean you should miss out? John Pickering May 14

The front page of the Herald this morning questions the participation of unconscious patients in clinical trials.

While I understand Auckland Women’s Health Council co-ordinator Lynda Williams unease, I also detected a failure to understand the process of how progress in medicine is made.

First, all research in such cases is approved by ethics committees which include lay people and patient advocates. That is clear in the article. In my experience they are very very thorough at ensuring the best interests of patients are highest priority. Family or whanau consent is almost always required (especially if the research involves an intervention*). These are the same family or whanau who are talking with medical staff and, at times, providing consent for medical interventions.  When a person is vulnerable it is up to all around them to treat them with respect and care.  Offering them, through their family, the opportunity to participate in research is showing respect for them as a valued member of society who is prepared to give in the interests of others.  Indeed, it is a right of the patient, through their family, to be offered such research.

Second, without such research there can be no progress in medical treatment of unconscious critically ill patients. In order to save lives interventions must be made at critical junctures during the progress of a disease, normally at the earliest possible time. It is in the best interests of us all that such research take place. The alternative is to give up hope and allow current mortality rates to remain as they are. I research a disease (Acute Kidney Injury) which affects 1 in 3 people in the Intensive Care Unit and increases their chances of dying about 4 times. There is no treatment and it is devilishly difficult to detect in the early stages. An estimated 2 million a year die because of Acute Kidney Disease. Without the generosity of family and friends allowing trialling of an intervention (always based on years of prior research and judged to be possibly efficacious) there will be no progress and the death toll will remain high. I salute family and patients around the world who have participated in such studies in the past, and will do so in the future.

Disclaimers: 1. I have no knowledge or understanding of the antiobiotic trial under discussion.  2. I have been involved in an intervention study where participants were unconscious at the time consent was obtained.

*Note, there are some circumstances where when minutes count an intervention is required.  Research in these areas is ethically more difficult, but no less necessary.  I welcome public debate in this area.  While ethics committees can deal with ensuring minimisation of harm in such circumstances, we do need to decide as a society what sacrifices of individual rights we should make for the greater good.

Tagged: Acute Kidney Injury, Clinical Trial, Coma, Critically Ill, Ethics, Intensive Care Unit, Research, The Auckland Herald

A new entity is born: CDaR John Pickering Aug 29

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.

Tagged: Acute Kidney Injury, Bias, Big data, Biomarkers, Clinical Data Research, Data, Databases, Disease, drugs, Kidney Attack, medicine, personalized medicine

Nelson Mandela is on dialysis John Pickering Jul 06

CNN is reporting Nelson Mandela is on dialysis. http://t.co/HZTIlmGrtO.  This means he is suffering from Acute Kidney Injury, the disease I study.  Having to have dialysis is very serious. Unfortunately, survival rates are only about 50% by this stage, less in the very elderly.  Dialysis is not a treatment, merely a support for the kidney to try and give them time to recover  function on their own and  a means to remove toxins from the body.

 

Tagged: Acute Kidney Injury, Dialysis, Kidney Attack, Nelson Mandela

Too little pee John Pickering Jun 26

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.

———————————————————

*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.

Tagged: Acute Kidney Injury, Acute Renal Failure, AKI, Fluids, Intensive Care, Kidney, Kidney Attack, Urine

Injury, function, and death John Pickering May 29

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

Tagged: Acute Kidney Injury, Acute Renal Failure, Biomarker, Creatinine, death, Dialysis, Kidney Attack, Sensitivity, serum creatinine

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