Wednesday, December 17, 2014

The future of nephrology training: A fellow's perspective


Much is being said about the steady and dramatic decline in applications to nephrology training programs. The recent match shows a continuation of this trend: 67.9 percent of offered positions filled, leaving 50% of US programs unfilled on match day. The writing is on the wall: the number nephrology training positions needs to shrink. However, there is going to be no agreeable and easy way to decide which program should reduce size or close its doors. Should we let programs decide what to do individually or should we defer decisions to some governing body? Should programs that go unfilled be forced to reduce numbers or shut down, the so-called “survival of the fittest” model? Or should we create an algorithm to decide how to reduce positions more equitably?

Tejas Desai posted a paper that describes a more equitable model by allocating training positions according to ESRD prevalence in US states/jurisdictions. In his model, he estimates that fewer jurisdictions would reduce in size under an equitable model compared to the “survival of the fittest” model. An “equitable” process using an algorithm is attractive because it would distribute the allocation of positions based on some objective measure, like ESRD prevalence. This would benefit training programs that have a harder time recruiting. An equal proportions model may “share the pain” so certain regions are not affected disproportionately than others, thus retaining program directors and training infrastructure for when applications rebound (assuming they will).

As a fellow in training, I worry that any algorithmic approach to this problem will be focused too heavily on the needs of the training program and not the applicants. In that sense, the “survival of the fittest” model is more oriented to a fellow’s actual needs. Program desirability is likely driven by a mix of perceived program quality and factors unrelated to quality like geographic region, cost of living, or job opportunities for spouses, for example. In this thin market with so few applicants, program quality is not as much of a distinguishing factor. I think it’s safe to assume that fellows will work hard everywhere and that training program directors and faculty truly care about fellow education at all programs. In that sense, these factors not directly related to program quality will likely influence an applicant’s decision. I think it’s safe to assume that if these factors matter today, then they will likely be valued again by applicants in future years. If we are truly on board with a mission to increase interest in nephrology, we can start by paying attention to where people want to train and why. The NRMP Match rank list is a reasonable way to understand this.

Simply allocating positions based on ESRD prevalence or any other equitable algorithm favors at-risk programs, but it does not take into account trainee preferences. Many trainees desire specialized training in transplantation, glomerulonephritis, interventional nephrology, clinical research, basic science research, medical education, quality improvement, or health informatics. Some programs are more desirable because they can provide these individualized opportunities for career development. Access to one of these programs might be more limited through an algorithmic approach to training position allocation. Who knows, if word got out that positions have been weighted to regions based on ESRD prevalence alone, it may perpetuate the stereotype among residents that nephrologists are nothing more than dialysis technicians, missing the breadth and depth of actual practice. If fewer positions are made available in highly desirable programs, then it would be wrong to assume an applicant will be just as happy or available to train elsewhere. Given that some applicants desire certain locations due to factors like job opportunities for spouses, reducing positions in those desirable locations may be enough to convince the applicant to choose an alternative career like hospital medicine for example, where opportunity is abundant.

The nephrology community should remember that the primary issue is lack of interest. Efforts to increase interest should be at the center of the discussion. Deciding how to reduce positions will be controversial and it will be tough to find agreement. Maybe the best solution will need to consider everyone’s needs equally: considering applicant choices/preferences and also minimizing program dissolution. One model for position allocation could be based on an incentive for producing more nephrology applicants: You get fellowship training positions if you contribute to the applicant pool by mentoring/developing the residents and students at your institution. This would actually address the underlying problem wouldn't it? I applaud Dr. Desai for starting this conversation. Even if some final complex algorithm is required, I just hope that applicant and fellow preferences are not ignored.

Friday, December 12, 2014

Effects of Intensive Low-Salt Diet Education on Albuminuria: CJASN e-Journal Club

This month’s CJASN e-journal club was hosted at the Department of Nephrology at the Royal Infirmary of Edinburgh. It discussed a trial designed to investigate the efficacy of an intensive low salt diet intervention (weekly 30 minutes phone feedback by a dietary consultant) on albuminuria in non-diabetic hypertensive patients already on an ARB. This study is an open-label, case-control, randomized clinical trial based in South Korea. 

It is important to note that selected patients were a relatively healthy, homogenous population with good blood pressure control and an already low urinary sodium excretion. Very few were active smokers and many took regular exercise. All RAAS antagonists and diuretic agents were stopped for an 8 week run in and blood pressure was controlled with alternative agents. At week 0, Olmesartan was then commenced, and at 8 weeks, subjects were randomised to control or intensive education groups. 
The primary endpoint was the decrement in albuminuria by week 16 and was reached in the intensive salt reduction group. They demonstrated a significant reduction in albuminuria (278 mg/d to 178 mg/d) compared to the conventional therapy group (258 mg/d to 231 mg/d). There was a greater reduction in urinary sodium excretion in the intensive education group than the conventional group, but no change in blood pressure or renal function. In subgroup analyses, the authors examined those that received a reduction of > 25% of their dietary salt and those who did not, irrespective of treatment group. Notably, only 60% of those who achieved this reduction came from the intensive group. 

Overall this is an interesting, well-performed study which successfully demonstrates that lower salt excretion correlates with lower albuminuria. This occurred despite no effect on BP in a cohort with already excellent BP control. It may be better to interpret this as a “proof of concept” that salt restriction in humans could influence albuminuria. For clinical practice however, there are issues around the generalizability of these findings to everyday patients, given this was a highly selected, compliant group of patients. Also, as we know in the nephrology community from previous trials, an improvement in a relatively soft endpoint like albuminuria will not necessarily translate into improvements in renal/cardiovascular events or mortality. 

Check out the full text of the paper, our complete post and discussion over at the CJASN eJC website.

Authored by Eoin O’Sullivan & Paul Phelan

Wednesday, December 3, 2014

SHROOM3, Making sense of GWAS risk alleles

In this months edition of JCI a group led by Dr B Murphy from Icahn School of Medicine at Mount Sinai describe a beautiful set of experiments that explain the mechanism by which the CKD and eGFR risk allele rs17319721 causes chronic kidney allograft damage.

The single nucleotide polymorphism rs17319721 is in intron 1 of a gene called SHROOM3. The risk allele A (major allele G) of rs17319721 was found in large GWAS studies of European ancestry to be associated with GFR (p=1*10−12), incident CKD (p=0.005) and GFR in type 2 diabetic patients (P= 3.18E-03). The risk allele, A, frequency is about 40% in caucasian populations but is less frequent in non-caucasian populations.

The authors decided to investigate the effect of rs17319721, the SHROOM3 risk genotype, on kidney allograft fibrosis and chronic allograft nephropathy (CAN). Furthermore, they sought to determine what role, if any, SHROOM3, plays in allograft fibrosis. The risk locus was genotyped in over 500 allograft recipients and 500 allograft donors from the GoCAR transplant cohort. Also, SHROOM3 transcript levels in 3month protocol allograft biopsies were recorded in some of these patients. Donor genotype carrying one risk allele A (A/A or A/G) was associated with higher 3month SHROOM3 expression levels compared to the normal donor G/G genotype. Interestingly, correlation occurred only in Caucasian donors when analyzed separately and there was no association with the recipient risk genotype.

The authors then looked at 12month allograft GFR and chronic allograft dysfunction index score at 12 months (CADI-12). 3month SHROOM3 expression levels were inversely related to 12 month GFR, predictive of CADI-12 and were predictive of worsening CADI score (3m to 12m)(termed ‘progressors’). These associations were not found in non-Caucasian donors.

To assess whether 3M SHROOM3 levels could predict CAN they generated logistic models that included recipient age, sex, race, AR and CIT with or without 3M SHROOM3 levels to predict 12M CADI ≥2 or 3-12M CADI change (ΔCADI) of ≥2. AUC for prediction of high CADI-12 and ΔCADI were improved in each subgroup when SHROOM3-3M level was added. For Caucasian donors AUC for ‘progression’ was 0.81 with SHROOM3 and 0.74 without SHROOM3. Furthermore, the A allele in the donor was associated with greater risk of CADI-12≥2 in all allografts (OR 1.98; CI, 1.10–3.59), indicating a higher risk of CAN with the risk allele.

This work demonstrates that the A risk allele (rs17319721) in donors is associated with higher 3M SHROOM3 levels and increase risk of CAN. Also, 3M SHROOM3 levels predict CAN and 12M GFR.

So what is the mechanistic consequence of having the A allele vs the G allele? rs17319721 is located in a transcription factor binding motif. The authors found that the transcription factor TCF7L2 binds more strongly to this motif when A is present vs when G is present. Wnt agonist increased TCF7L2/β-catenin complex binding to the A allele binding site but not the G allele site. TGF-β1 is a known key growth factor regulating renal fibrosis. The authors found that TGF-β1 induced increases in SHROOM3 expression via the Wnt/β-catenin/TGF-β1 pathway in renal tubular cells.

Then they showed SHROOM3, in turn, enhances the TGF-β1/SMAD3–induced expression of profibrotic genes including CTGF, Vimentin, and Collagen IV (downstream targets of TGF-β1/SMAD3 signaling) and these genes were significantly upregulated in allografts within the highest quartile of SHROOM3 expression. Finally the authors verified these data in a mouse model of fibrosis.

Taken together, this data suggest an increased profibrotic program in the presence of the enhancer function of the risk allele and/or increased SHROOM3 expression. This schema is illustrated below.

This paper nicely describes the mechanism of action conferred by a single risk allele found in large GWAS studies. Until recently there has been little data to explain the relevance of the many risk SNPs described in GWAS studies. Without an understanding of the mechanism through which these SNPs confer disease there can be no progress towards identifying potential therapeutic targets. This study has identified SHROOM3 as a potential therapeutic target for chronic allograft nephropathy.

Monday, December 1, 2014

Vote for the Top Nephrology Stories of 2014

This is the 5th year RFN has hosted the Top Nephrology Stories of the year poll. This is an attempt to look at the year in nephrology voted by the readers of RFN. Take a few moments and vote for any of the stories you feel are worthy of making the top 10 list. You can vote for as many as you wish. Write a quick blog post about a story you think deserves the vote. If we missed anything please feel free to comment below. Note, the story must be published after the year end poll of 2013.

Please go to the right hand side -----> of the RFN page and make your selection until Dec 12th. Results will be posted on Dec 25, 2014

Below contains links to each of the stories for more information.

JNC8 Hypertension Guidelines published in JAMA (this was published online after the poll last year)

ZS-9 trials for hyperkalemia in JAMA and NEJM (Nov 2014)

Patiromer OPAL-HK trial for hyperkalemia in NEJM (Nov 2014)

The PREDIAN Trial: pentoxifylline for diabetic kidney disease in JASN (June 2014) 

SYMPLICITY-3 trial for renal denervation in resistant HTN in NEJM (March 2014)

Mesoamerican nephropathy continues to unfold

Dendritic cell isoketals activate T cells and promote HTN in JCI (October 2014)

Sustainable Growth Rate legislation in March 2014 pushed inclusion of oral only meds (phos binders) in ESRD out of bundle until 2024

α–Intercalated cells defend the urinary system from bacterial infection in JCI

Peritoneal dialysis solution shortage 

IgA Nephropathy GWAS implicates genes involved in helminth immune response in Nature Genetics (Sept 2014) 

Anti-Phospholipase A2 receptor assay licensed for commercial testing

Co-trimoxazole and sudden death in patients receiving RAS inhibitors in BMJ (Oct 2014)

Identification of thrombospondin type-1 domain-containing 7A in idiopathic membranous nephropathy in NEJM (Nov 2014)

Perivascular Gli1+ progenitors contribute to myofibroblast pool leading to fibrosis in multiple organs including kidney Cell Stem Cell (Nov 2014)

HALT-PKD Trials of low BP target and ACEi/ARB combo in NEJM (Nov 2014) 

CORONARY Trial of on-pump versus off-pump CABG and subsequent CKD (June 2014)

POSEIDEN Trial fluid administration guided by LV end diastolic pressure in Lancet (May 2014)

Gestational HTN or preeclampsia was more common in kidney donors in NEJM (Nov 2014)

Please go to the right hand side -----> of the RFN page and make your selection until Dec 12th. Results will be posted on Dec 25, 2014


Wednesday, November 26, 2014

A cause and a cure of hyperkalemia? The next #NephJC

There has been a flurry of publications in the field of hyperkalemia with 3 separate trials of oral potassium binding agents within a week of each other (Sodium Zirconium in JAMA, and NEJM and Patiromer in NEJM) and a potentially related observational trial on the risks of co-trimoxazole in patients on RAAS blockade in the BMJ. With all that reading to get through, the next NephJC on Tuesday Dec 2nd will be a double whammy. We will look at the HARMONISE trial of ZS-9, and a large study of co-trimoxazole and potential associations. 

Trim-Sulfa and Sudden Death in patients receiving inhibitors of renin-angiotensin system. 

The first paper for discussion is a large, Canadian, case control series, by the Canadian Drug Safety and Research Effectiveness Network, published in the BMJ. The hypothesis is the risk of sudden death in patients on RAAS blockade is higher following administration of specific antibiotics rather than amoxicillin. To answer their question, they searched 17 years of records representing over 1.6 million patients. They identified 39,879 with a label of sudden death and a subsequent group of 1,027 that had a prescription for the target antibiotics in the 7 days prior to dying. 
The authors write: “In the primary analysis, co-trimoxazole was associated with a significantly increased risk of sudden death within seven days relative to amoxicillin (OR 1.8 C.I 1.5-2.24)” Ciprofloxacin was associated with a somewhat lower risk of sudden death. I found it strange that norfloxacin, which has similar QT prolonging properties to ciprofloxacin, had had no such risk. The authors speculate this observed association may be due to trimethoprim’s activity as an ENaC antagonist. There are a number of important limitations to consider. There was no indication for antibiotics recorded. Also, the cases and controls had some important differences in terms of diuretic use and co-morbidities. Only 8.2% of the cases had renal disease, the stage of which was unclassified. The authors can only speculate about a possible mechanism involving hyperkalaemia as no K levels were obtained for any of these patients, nor any ECG to help explain the effect of ciprofloxacin. 

Harmonise: Effect of Sodium Zirconium Cyclosilicate on Potassium Lowering for 28 Days Among Outpatients With Hyperkalemia. 

ZS-9 is a zirconium silicate, a non-absorbable potassium binding agent. It is an inorganic cation exchanger crystalline with the capacity to bind both potassium and ammonium in the GI tract. Its creators tout its non-absorbable nature as the key to minimising systemic side effects. HARMONISE is a phase 3, multicenter, randomized, double-blind, placebo-controlled trial spanning 44 centres. Inclusion criteria was simply a serum K of ≥ 5.1 on 2 occasions. Initially, 258 patients who met eligibility criteria were given ZS-9 10g three times daily. If they achieve normokalaemia within 48 hours, they were then randomized to a placebo, or increasing doses of ZS-9 once daily. The mean eGFR was 46 ml/min/1.73m2 and no ESKD patients are represented. 
Did it work? The short answer is yes. ZS-9 had a reasonable rapid rate of onset and within 2 hours, serum Potassium has dropped by −0.4 mEq/L (95% CI, −0.5 to−0.4) and was - 1.1 mEq/L by 48hours. Encouragingly, it seems generally well tolerated with some edema and hypokalemia as the doses increased. In conclusion, this is a well executed phase 3 trial and ZS-9 has potential to be a well tolerated and predictable treatment option for hyperkalemia. The authors quite rightly point out we still have no data beyond 4 weeks, nor have we any meaningful endpoint such as mortality or hospital admissions. It is an encouraging study none the less, and should lead to FDA approval and another tool in our kit.

Full post can be seen at www.nephjc.com 

Authored by Eoin O'Sullivan.

Creatininase

There was a fascinating case published a couple of months ago in the American Journal of Medicine. I have a particular interest in this case as it was my clinic mentor, Julian Seifter, who made the diagnosis and published the case. I have waiting for a long time to write a post about it but couldn't until the paper came out.
The case is a 50yr old man with a history of CKD, quadraplegia and an ileal conduit who was being investigated for CKD. His serum creatinine was 3mg/dl and a creatinine clearance was done to estimate his GFR. His urine creatinine concentration was 175mg and his calculated creatinine clearance was only 3 mls/min. At this point, RRT was recommended and the suggestion was that his serum creatinine overestimated his GFR because of reduced muscle mass. 
However, because the urine creatinine still seemed inordinately low and he had no symptoms, an inulin clearance was done which revealed a true GFR of 21 ml/min. What could explain this discrepancy?
A urine culture grew diphtheroids, staphlococcus and streptococcus. Although creatinine, once it is produced in the muscle cannot be metabolized in humans, some bacteria produce creatininase and as a result are able to break it down. Corynebacterium is a diphtheroid that has been associated with the production of creatininase. Dr. Seifter suspected that there was a creatininase-producing bacterium in the ileal conduit that was metabolizing the creatinine leading to a falsely low creatinine clearance. To confirm this, he took a sample of the patient's urine, added a known quantity of creatinine and incubated it for 24 hours at 20 degrees. The results are shown in the figure below.


Our GI tract has some creatininase-containing bacteria but under normal circumstances, the amount of creatinine clearance that they contribute is negligible - less than 2 ml/min GFR equivalent. However, in individuals with advanced CKD, both the relative and total clearance contributed by these GI bacteria increases such that in people with a GFR <10 the contribution of gut clearance can be as high as 4ml/min or approximately 50%. The opposite can also occur. The highest serum creatinine I ever saw was in a 50yr old woman with inflammatory bowel disease. She weighed 40kg but her admission creatinine was 38mg/dl. Despite this, she felt relatively well. She had previously undergone multiple bowel resections and had almost no functional bowel remaining. As a result, she had no gut clearance of creatinine and no upper limit to her serum creatinine concentration.

One final note, the urea clearance was not useful in the above patient either - his urine also contained urease -  the clue to this was a very high urine pH (>9) in the presence of a mild metabolic acidosis and no history of RTA.

Thursday, November 20, 2014

EDELMAN IS THE ROOT OF ALMOST ALL GOOD IN NEPHROLOGY

Almost all the formulas we use in the management of the disorders of water homeostasis are derived from the Edelman equation. I am presenting where these formulas come from for the math aficionados.

Edelman equation

·         Original Edelman equation (J Clin Invest. 1958;37:1236-56):
[Na+] = {1.1 x (Nae + Ke)/TBW} – 25.6
Where [Na+] = plasma sodium concentration, Nae=total body exchangeable sodium, Ke=total body exchangeable potassium, TBW = total body water.

·         Simplified Edelman equation: [Na+] = (Na + K)/TBW
·         [Na+] x TBW = Na + K
·         Na + K = [Na+] x TBW

Calculating Free Water Deficit (FWD)

Method #1 (Using baseline weight, certainty about what % of body weight is water)

1.       Assuming only pure water has been lost, the total body sodium and potassium remain constant so the total body sodium and potassium at baseline (Na + K)baseline and the total body sodium and potassium after water loss (Na + K)current are equal:

·          (Na + K)baseline = (Na + K)current

2.       Total body sodium and potassium can be expressed as sodium concentration ([Na+]) multiplied by total body water (TBW):

·         [Na+]baseline x TBWbaseline = [Na+]baseline x TBWcurrent
·         TBWcurrent = [Na+]baseline x TBWbaseline/[Na+]current … (1)

3.       Free water deficit can be expressed as:

·         FWD = TBWbaseline – TBWcurrent … (2)

4.       Then replacing (1) in (2):

·         FWD = TBWbaseline – ([Na+]baseline x TBWbaseline)/[Na+]current
·         FWD = TBWbaseline x (1 – [Na+]baseline/[Na+]current)

5.       If [Na+]baseline is considered normal at 140 mEq/L then:

·         FWD = TBWbaseline x (1 – 140/[Na+]current)

Method #2 (Using current weight, uncertainty about what % of body weight is water)

1.       Assuming only pure water has been lost, the total body sodium and potassium remain constant so the total body sodium and potassium at baseline (Na + K)baseline and the total body sodium and potassium after water loss (Na + K)current are equal:

·         (Na + K)baseline = (Na + K)current

2.       Sodium and potassium masses can be expressed as sodium concentration ([Na+]) multiplied by total body water (TBW):

·         [Na+]baseline x TBWbaseline = [Na+]current x TBWcurrent
·         TBWbaseline = [Na+]current x TBWcurrent/[Na+]baseline … (1)

3.       Free water deficit can be expressed as:

·         FWD = TBWbaseline – TBWcurrent … (2)

4.       Then replacing (1) in (2):

·         FWD = [Na+]current x TBWcurrent/[Na+]baseline – TBWcurrent
·         FWD = TBWcurrent x ([Na+]current/[Na+]baseline - 1)

5.       If [Na+]baseline is considered normal at 140 mEq/L then:

·         FWD = TBWcurrent x ([Na+]current/140 - 1)

Calculating Rate of Infusion of Hypertonic Saline

Method # 1: Na deficit formula

Deriving Na deficit formula

1.       Na deficit = Nagoal – Nacurrent … (1)

2.       Since Na + K = [Na+] x TBW, then Na = [Na+] x TBW – K … (2)

3.       Replacing (2) in (1)
·         Na deficit = TBWgoal x [Na+]goal – Kgoal – {TBWcurrent  x [Na+]current – Kcurrent}

4.       Assuming TBW and K remain constant, so TBWgoal = TBWcurrent, and Kgoal = Kcurrent, then TBW = TBWgoal = TBWcurrent and K is cancelled out from equation:

·         Na deficit = TBW x [Na+]goal – TBW x [Na+]current
·         Na deficit = TBW x ([Na+]goal – [Na+]current)

5.       Since now we aim for an increase in [Na+] of 6 mEq/L, so [Na+]goal – [Na+]current = 6 mEq/L then:

·         Na deficit = TBW x 6 mEq/L

Calculating volume of infusate

·         Volume of infusate = Na deficit x (1000 mL/513 mEq)

Calculating rate of infusion

·         Rate of infusion = volume of infusate/24h

Method #2: Adrogue-Madias formula

Deriving Adrogue-Madias formula

1.       [Na+] = (Na + K)/TBW … (Edelman equation)

·         [Na+]current = (Nacurrent + Kcurrent)/TBWcurrent
·         [Na+]current x TBWcurrent = (Nacurrent + Kcurrent) … (1)

2.       [Na+]goal will be the new [Na+] that results when we administer 1L of an infusate containing Nainfusate and Kinfusate, then:

·         [Na+]goal = (Nacurrent + Kcurrent + Nainfusate + Kinfusate)/(TBWcurrent + 1) …(2)

3.       Substracting [Na+]current from both terms of equation (2), then:

·         [Na+]goal – [Na+]current = (Nacurrent + Kcurrent + Nainfusate + Kinfusate)/(TBWcurrent + 1) – [Na+]current

4.       But [Na+]goal – [Na+]current is the same as change in [Na+], then:

·         Change in [Na+] = (Nacurrent + Kcurrent + Nainfusate + Kinfusate)/(TBWcurrent + 1) – [Na+]current
·         Change in [Na+] = {(Nacurrent + Kcurrent + Nainfusate + Kinfusate) – (TBWcurrent + 1) x Nacurrent}/(TBWcurrent + 1)
·         Change in [Na+] = {Nacurrent + Kcurrent + Nainfusate + Kinfusate – ([Na+]current x TBWcurrent –[Na+]current)}/(TBWcurrent + 1) … (3)

5.       Replacing equation (1) in (3), then:

·         Change in [Na+] = {Nacurrent + Kcurrent + Nainfusate + Kinfusate – (Nacurrent + Kcurrent) - [Na+]current}/(TBW + 1)

6.       Cancelling out Nacurrent + Kcurrent then:

·         Change in [Na+] = {Nainfusate + Kinfusate - [Na+]current}/(TBWcurrent + 1)

Calculating volume of infusate

·         Volume of infusate = {1000 mL x (Change in [Na+])goal}/(Change in [Na+])
·         Volume of infusate = {1000 mL x 6 mEq/L}/(Change in [Na+])

Calculating rate of infusion

·         Rate of infusion = volume of infusate/24h





Tuesday, November 18, 2014

SEVERE CHRONIC HYPONATREMIA: A Pathophysiological Rumination.

[This is the final post in the five-part series covering some important and often overlooked (and under-published) issues and concepts in the management of severe hyponatremia. While this is not, by any means, an exhaustive discussion of the topic, I hope that these posts will not only help the readers enhance their understanding of the pathophysiology of severe hyponatremia but also help them manage it more effectively with a lot less stress and mental anguish.]


PART 5: A CALCULATED APPROACH
In this video post, I discuss --- what I find to be --- an extremely useful method of calculating the dose of sodium chloride based infusions and predicting response to therapy while treating chronic severe hyponatremia.


Posted by Hashim Mohmand

Monday, November 17, 2014

Michelle P Winn Endowed Lectureship, ASN 2014

At this year's ASN Kidney Week in Philadelphia Andrey Shaw, MD, presented the inaugural Michelle P Winn Endowed Lectureship. Dr Shaw was not only a longtime collaborator of Michelle’s but also a very close personal friend making him the perfect choice for this inaugural lectureship. Dr Shaw delivered an excellent talk interweaving highlights from Michelle’s stellar career with examples of Michelle’s fun loving and genuine kindhearted nature. I was lucky enough to work in Michelle’s lab from 2012 to 2014. She cared greatly about all her mentees both professionally and personally. She was a huge inspiration and a friend.

Michelle did her undergraduate studies at the University of North Carolina before going to medical school at East Carolina University. She then entered Duke University for residency and fellowship before joining the Duke faculty. Despite spending most of her career at Duke she remained a true Tar Heel (UNC) fan!

She received her training in classical human genetics from Drs Jeffery and Peggy Vance at the Duke Center for Human Genetics. In collaboration with another longtime friend and collaborator and early mentor at Duke, Dr Peter Conlon, Michelle began investigating the genetic heterogeneity of FSGS.
  • Together Drs Winn and Conlon collected what is now one of the largest Familial FSGS datasets in the world.
  • Michelle’s early work linked familial FSGS in one large family from New Zealand to a locus on chromosome 11.
  • Following this she identified TRPC6 as the cause for FSGS in this family. This was a seminal paper published in Science and introduced an ion channel and calcium into the burgeoning field of podocyte biology. 
  • Michelle’s further work on TRPC6 made a huge contribution to the understanding of the biology of TRPC6 in kidney disease. 
Michelle was also very interested in other inherited kidney diseases.
  • She described linkage of a gene causing MPGN type III, 
  • identified TNXB mutations causing vesicoureteral reflux, 
  • was involved in studies of genetic factors influencing the development and progression of IgA nephropathy 
  • a hybrid CFHR3-1 gene causing familial C3 glomerulopathy. 
  • Her work also helped to define the disease burden and impact of other FSGS causing genes such as INF2, NPHS2 and PLCe1
Towards the end of her career and even while fighting her illness she remained very involved and continued to contribute in a huge way to the field we all love.
  • She discovered Anillin a new gene causing FSGS, 
  • a new mutation in the WT1 gene 
  • added further insights into the function and regulation of TRPC6 in podocytes. 
Michelle was a leader in her field of podocyte biology and renal genetics. In 2007 Michelle won the ASN Young Investigator Award. I am sure that if her life had not been tragically cut short she would have been awarded the highest honors our specialty has to offer. The creation of the Michelle P Winn Endowed Lectureship is testament to this probability. Michelle was a beautiful person and will be missed by all who knew her.

Tuesday, November 11, 2014

Blogger night at #KidneyWk14 on Thursday

Thursday night at 8:30 pm, Blogger Night (after the ASN Presidents Reception). If you like the Neph Social Media Crew from Twitter, Renal Fellow Network, AJKDblog or NephJC, join us for drinks at Field House Philly sports bar. Look for Joel in his AJKD hat.

Sunday, November 9, 2014

Nephrology fellows events at ASN #KidneyWk14

Welcome Reception: Nov 12th: 6-7PM
Marriott Downtown, Grand Ballroom, Salon H

Fellows In Training Bowl (Mystery Case Debate): Nov 14th 2-3PM
Convention Center, Room  119A

Fellows In Training Bowl (Jeopardy game Nephrology Challenge): Nov 14th 3-4PM
Convention Center, Room  119A

Meet the Experts Session Nov 15th  9:30AM - 10:30AM (Meeting the ASN Award Winners)
Convention Center, Hall D

Fellows Forum Nov 15th 10:30-11:30AM
Convention Center, Room 203

Fellows Poster Discussion Section Nov 15th 2-3PM
Convention Center, Room 112

h/t Kenar Jhaveri

Thursday, November 6, 2014

NephJC Live at ASN Kidney Week 2014

The Nephrology twitter journal club will come to life on Saturday 15th November at Kidney Week in Philadelphia. NephJC co-founders Dr Joel Topf and Dr Swapnil Hiremath will co-host the event at the Double Tree hotel.

There will be 2 live presentations on the day, both of which are sure to stimulate active discussion. There will be a study, presented by Dr. Deirdre Sawinski of University of Pennsylvania, looking at transplanting HIV positive kidneys into seropositive donors. Dr Francis Wilson will also present his RCT on acute kidney injury.

The event is filling up so please visit www.nephjc.com before this Sunday November 9th to register. For those of us who cannot make it to Philadelphia, the event will, as ever, be live tweeted using #NephJC.

Wednesday, November 5, 2014

Preventing PD Peritonitis: What's the Evidence?

Peritoneal dialysis associated peritonitis is the second commonest cause of death among PD patients (35/1000 years) and the most common cause of treatment failure. It confers a CV risk of 7 times normal for 6 months following the bacteremia, so we need to remain ever vigilant when dealing with PD patient, and its worth refreshing our knowledge on how to prevent this feared complication. There is a paucity of high quality evidence for many of the most fundamental questions in PD. Such is the lack of evidence, the International Society for Peritoneal Dialysis (ISPD) have issued a consensus document where they state they are unable to issue formal guidelines.

The best resources I have found on the topic are a Kidney International supplement from 2006, and the ISPD document already mentioned. In the first instance, which patients are at risk of developing peritonitis? The best described risk factors are hypoalbuminemia (similar to the association in haemodialysis patients), Staph aureus  carriage at inception of dialysis (HR 1.53), initiation of PD early after catheter insertion (HR 0.98/day), PD after transplant failure (HR 2.18), lower hemoglobin (HR 0.88/gram/l), faster PD transport rates (HR 2.92) and previous peritonitis. A special risk group to consider are those PD patients undergoing invasive procedures such as endoscopy or IUD insertion. There is evidence that antibiotic prophylaxis using cephalosporins may help reduce peritonitis rates.

The cornerstone of peritonitis prevention is minimizing contamination risk with effective hand washing and immaculate exchange technique. Specialized nurse-led training is key. If peritonitis occurs, retraining and re-education are the most important interventions. Home visits by PD nurses can cut recurrence rates in half, and should be performed where possible. A Cochrane review could find no RCT data to support any particular insertion technique, catheter type, number of cuffs or positioning. It demonstrated that of all catheter-related interventions designed to prevent peritonitis in PD, only disconnect (twin-bag and Y-set) systems have been proved to be effective. Topical antibiotic prophylaxis is a standard of care and there are multiple RCTs demonstrating the efficacy of mupirocin cream application at the exit site. Ointment is to be avoided as it can erode the catheter polymer.  In contrast, nasal mupirocin is more difficult to justify. Cochrane point out that while there is trial data that nasal mupirocin reduces exit-site/tunnel infection, it has no effect on peritonitis rates. Preoperative intravenous prophylaxis reduces early peritonitis but not exit-site/tunnel infection. 

Recent published trials in the area of antimicrobial prophylaxis have been disappointing. They include the Honeypot study, discussed previously on RFN, which demonstrated the application of honey to exit sites approximately doubled the risk of developing peritonitis in diabetic patients. The MP3 study published in JASN in 2012 found polysporin to increase rates of fungal exit site infections without any improvement in primary outcomes of exit site infection or peritonitis. Finally, a special mention goes to cats, who are responsible for at least 25 case reports of pastuerella peritonitis, as well as an assortment of other bugs. Not to be outdone, rodents feature  in the case reports also, coining the term “hamster bite peritonitis” caused by pastuerella aerogenes.
In summary, technique and continuing re-education are of fundamental importance, as are topical antibiotics to prevent exit site infection.  Beyond this, trial data are severely lacking and local opinion and consensus must guide practice.

Authored by Eoin O'Sullivan