As mentioned in a Nov 2009 blog post, there isn’t very much data on the long-term outcomes for live kidney donors. That’s because they are not being tracked. Further, there is little data on what attributes (independent variables) may indicate which donors (cases) are more likely to suffer adverse outcomes (dependent variables).

Harvey Mysel of Living Kidney Donors Network recently posted a link on Facebook to an article that shows that medical outcomes for living kidney donors vary by race. The study in the New Engl. J. Med. Aug 2010  (subscription required) caught my attention because two of the authors, Connie Davis and Paolo Salvalaggio, are at the Univ Washington Medical Center where my donor surgery will be performed. Dr. Davis is a nephrologist and director of the kidney transplant program. Dr. Salvalaggio is a surgeon in the program and was originally assigned to be the surgeon for my nephrectomy. (A schedule change led to a change in surgeon.)

They used a clever technique called a retrospective study to find the outcomes of donors. Rather than ask donors as they enter a transplant program to participate in a longitudinal study (called a prospective study) they looked at historical medical data after the fact. They obtained the historical medical data by matching the ID of donors in the United Network for Organ Sharing (UNOS) database with the customer database of a cooperating health insurer (the insurer is not identified, but my guess is Kaiser Permanente). Retrospective studies are fast (no need to wait several years to collect data) and inexpensive (no need to track patients for years as they move, stop cooperating, change insurance plans, etc.). However, these studies are subject to many types of sampling bias, which are beyond the scope of this blog post.

The authors make two findings. First is that some donors, both black and white, receive treatment for hypertension, diabetes mellitus, and chronic kidney disease after their surgery. Second is that black donors had higher prevalence of these morbidities than whites for all three conditions. On their own, these findings are not particularly surprising since these three diseases are very common chronic conditions and the black population as a whole has higher rates than whites.

However, it does lead to two concerns. The first is that although kidney donors are healthier than the population at large, doctors must not assume they will remain so. They should be vigilant for signs of chronic diseases among their patients who were kidney donors. This study shows that even within a few years someone who was thoroughly tested (and kidney donors get an extremely detailed examination) may begin to show symptoms of chronic disease. Hypertension, diabetes mellitus, and chronic kidney disease are often called silent killers. This study shows just how silent.

Second, the article says prevalence of these diseases among certain groups of kidney donors were in some cases as high as or higher than expected for a similar subpopulation that were not donors. This deserves additional research. Using prevalence rate (proportion who have the diagnosis) rather than incidence rate (proportion who receive their first diagnosis) may understate the seriousness of the problem. That’s because within the general population, the prevalence of these three chronic conditions is higher than it was for the kidney donors during the year in which they underwent their donor surgery. Thus, if the prevalence of these chronic conditions is the same as the general population in later years, then the incidence rate each year among kidney donors must be higher than for the general population. This may indicate that the kidney donation itself may be a factor in the evolution of the disease.

Or it could be a result of sampling bias. That is, kidney donors are more likely to have insurance and thus more likely to see a doctor who will diagnose the disease. The authors state,

“In our study, the increased prevalence of hypertension among Hispanic donors, as compared with the general population, may, in part, reflect underreporting of hypertension in this ethnic group, as compared with white respondents, in NHANES. We speculate that medical surveillance after kidney donation may mitigate barriers to the recognition of hypertension rather than differentially affect the risk of hypertension among Hispanic donors.”

When designing advertisements, creative directors often prepare mock ups of proposed designs to show to clients. Creative directors rely on their experience and training to prepare combinations of images and text that will hopefully engage readers. But they may not be able to articulate why their combination works or is better than another combination. Similarly, clients often accept or reject designs based on subjective and personal criteria. Is there a better way to judge the potential impact of an ad?

One method used by market researchers is eye tracking studies. This technique records a person’s eye position and movement when viewing visual media. You want the audience to be attracted to the image and headline which piques curiosity. Then they will be drawn into the text, and finally look at the company name, logo, tagline, and contact information.

The human eye and mind are remarkable devices. The human eye is not just a camera that records color and light. It also processes images before sending it to the brain. The eye is very slow at resolving an image. It takes about 1/20th of a second for the eye to generate an image to send to the brain. During that time, the eye must remain fixed on the point of interest, even if your body is in motion. This is called fixation. Then your eye moves quickly to the next point of interest using an action called saccade. During the saccade, your eye can’t create a good image, it is just a jittery blur. It doesn’t send this blurry image to the brain, it sends nothing. Yet you never notice that your entire visual life consists of a series of rapid still images with blackouts in between. In fact, that’s why movies and television (which consist of 24 or 30 still images a second) can fool you into thinking that there is constant motion.

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Fixation and saccade. Image from Wikipedia

Eye tracking studies for advertising have been conducted for years, starting in the 1960s though as equipment improved and fell in price, the practice expanded. A new web service from 3M (Creativepro May 2010) called Visual Attention Service does not require actual consumer testing. Instead, this service uses a database of previous studies to predict what a person will look at and rate the effectiveness of the image and text in holding a person’s attention.

VAS

Visual Attention Service. Video from 3M

I’ve signed up as a user, but haven’t tried the service out myself yet. My guess is that the current tool is quite crude. But the concept makes sense and I can see that Google, Microsoft, and Yahoo! would be interested. Anything that increases the effectiveness of advertising is valuable and worth a lot to advertisers, publishers, and design firms.

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While researching this blog entry, I came across a study by Think Eye tracking, a market research firm in Berkshire, UK, that reports the hilarious eye tracking results of a guy and what he looks at while attending a speed dating event. I think what actually happened is that he was so embarrassed and self-conscious about his appearance while wearing the eye tracking video camera headset that he had to avert his eyes. Yeah, that’s it.

And speaking of research into speed dating, previous research had indicated that women are pickier than men when selecting who to meet again. Two psychologists at Univ. of Penn. found that among 2,650 participants at HurryDate, the average woman was chosen by 49% of the men but the average man was only chosen by 34% of the women. However, this result may be biased because at most speed dating events, the man moves from table to table while the woman remains seated. A study by two psychologists at Northwestern in Psych. Sci. Sep 2009 shows that the gender selectivity difference disappears if women are the ones who rotate and men sit. The act of approaching someone increased self-confidence and reduced selectivity. Research bias can be very subtle.

[Update: Fixed a broken link to VAS YouTube video.]

The kidney transplant waiting list maintained by the UNOS gets longer every year as does the average waiting time for patients on the list. This is true even though the number of patients diagnosed with end-stage renal disease (ESRD) has declined slightly over the past few years. What is driving this? To examine this problem, I examined the data for annual changes in the number of patients with ESRD and the number on the waiting list.

About the data

The data for annual incidence (number of new cases of ESRD diagnosed in a year) and prevalence (total number of people with ESRD at the end of each year) were obtained from the USRDS 2009 annual report. The report contains a wealth of data on chronic kidney disease and ESRD. It has an entire chapter devoted to transplantation.

The data on the UNOS waiting list data was obtained from the UNOS. Some is available from OPTN annual reports or from the report builder web service. Others were generated specifically for me by UNOS. I want to thank Katarina Linden of UNOS for summarizing the SAS data used in this blog post. Any errors in analysis are mine alone.

A few notes regarding the UNOS data. First, the data being analyzed is for the kidney-only list. Patients are placed on lists based on what organ(s) they need. The UNOS maintains separate lists for each organ combination a patient needs, kidney only, kidney and pancreas, kidney and liver, etc. A single patient can be on more than one list. If the candidate receives a transplant, the transplant center is required to remove the patient from all the other lists as a duplicate entry.

Second, the counts are for registrations not candidates. In any year, about 5% of the kidney-only candidates (patients on the kidney-only waiting list) are registered at more than one transplant center. Most are people who have moved and are transferring their registration to a transplant center closer to their new address. But a few, most likely wealthy patients, are actually registered at multiple transplant centers in an effort to get an organ faster. The most famous example of this is Steve Jobs, who needed a liver transplant and had access to a corporate jet. But anybody who lives in a large city can benefit by getting on the list at a hospital in a more rural area, then traveling to that town and waiting for a donor after they reach the top of the list. Again, after the patient receives a transplant, all transplant centers are required to inform the UNOS that duplicate registrations for that patient should be removed from the list.

Third, there isn’t a direct correlation between the number of people on the UNOS waiting list and the number of people with ESRD (the prevalence rate). Once a patient receives a transplant, they are removed from the waiting list. However, they are not cured and so are still counted as having ESRD. Similarly, there is no direct correlation between the number of people added to the UNOS waiting list in a year and the number of people newly diagnosed with ESRD. That’s because a patient who enters the waiting list may have been diagnosed with ESRD years earlier. Also, they may enter the waiting list if the they previously received a kidney transplant and the organ fails.

Finally, there are two categories to the waiting list. Registrants are classified by the transplant center as either active and inactive. Active registrants are considered medically able to get a transplant immediately if an organ becomes available. Inactive registrants are currently unable to accept a transplant, but are considered good long-term candidates for a transplant. I will discuss this in more detail later.

The UNOS data is collected via a survey that each transplant center must complete for each registrant on their waiting list once a year to determine the registrant’s current status. As any of you who have dealt with survey data realize, cleaning survey response data from respondents (both the candidates and the administrators at the transplant center) who are not familiar with statistical analysis is one of the most difficult tasks in any research project and is a major source of nonsampling error.

Growth in prevalence of ESRD and in size of kidney transplant waiting list

In 2007, the latest year data is available, about 111,000 people in the U.S. were diagnosed with ESRD for an average incidence rate of 361 per million population. Figure 1 shows the incidence rate of ESRD has been rising dramatically over the past two decades, though it seems to have peaked. Blacks are more than three times likely to be diagnosed as whites. The ratio of prevalence by race is not as high, meaning that once diagnosed, whites tend to live longer with ESRD than blacks.

Incidence

Figure 1. Incidence and prevalence rates for ESRD by race. Data from USRDS

In 2007, the prevalence of ESRD was about 1,700 per million population, representing over 527,000 people. Of these people, about 150,000 have a functioning transplanted kidney and 375,000 are on dialysis. (The remainder refuse treatment and will die within a few months.) However, as shown in Figure 2, there were only 78,300 registrants (and fewer candidates) on the UNOS transplant waiting list. This means only one-fifth of patients on dialysis were on the UNOS waiting list. As discussed in earlier blog posts (Dec 5 and Dec 18), the reasons people fail to get on the waiting list are complex.

Total

Figure 2.Total active and inactive wait list. Data from UNOS

Figure 3 shows even though the number of transplants is growing (right), the incidence rate of ESRD is growing faster (left), so the wait times for a deceased donor kidney is getting longer (center) and the transplant rate is falling (left). For every 100 people newly diagnosed with ESRD in 2007, there were only four transplants.

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Figure 3. Transplant trends. Data from USRDS

Growth in the UNOS waiting list

Figure 4 shows the number of new registrants being added to the active list each year rose from about 17,000 in 1995 to 25,000 in 2004 and has flattened out since then. Unfortunately, the number of transplants, from either deceased or live donors, has not kept pace. The seemingly good news is that the number of registrants removed from the active list without a transplant (which consists of people who decide they no longer want a transplant, are too sick for a transplant, or die) has not been growing. The categories that has been growing (and growing rapidly) are movements to and from the inactive list, with more patients going to the inactive list than coming from it. Notice the size of this churn represents a large proportion (more than one-fourth) of the active waiting list population.

Active

Figure 4. Active wait list. Data from UNOS

Figure 5 shows the rapid growth of the inactive waiting list. First, notice the jump in the number of new inactive registrations starting in 2004. The most common reason for being initially placed on the inactive list is an incomplete evaluation by the transplant center. That is, the patient starts the evaluation process but is unable to complete it before the survey date (perhaps due to difficulty getting transportation to the transplant center). Other reasons for being on the inactive list are the presence of treatable comorbidities such as obesity, addiction (smoking, alcohol, or drugs), hypertension, or type 2 diabetes.

Next, notice the large number of registrants moving to the active list. Many of them are the new registrants who have completed their evaluations and are moved to the active waiting list, but also includes a large number of registrants who started as active and were on the inactive list for a period of time.

Most of the people on the inactive list are removed without a transplant, which makes sense, since they were not considered good transplant candidates. However, when coupled with the large flow of registrants from the active list (equal to about half of the total number of inactive registrants each year), this may also indicate that many transplant centers are moving active candidates to the inactive list rather than removing them entirely. Thus, the inactive list may contain many registrants who are too sick to receive a transplant and have little chance of recovery prior to death.

There are a large number of registrants in the Not coded category. This indicates that people on the inactive list are less likely to be in close contact with their transplant center and either could not be contacted or incorrectly completed the survey.

Inactive

Figure 5. Inactive wait list, data from UNOS

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In a future post, I will continue to explore the USRDS and UNOS data to revisit the issue of long wait times experienced by patients with type O blood.

[Update1: Added explanation that the counts of incidence and prevalence cannot be directly compared to the counts on the waiting list.]

[Update2: Corrected an error in proportion of patients with ESRD on the UNOS waiting list. Patients who have a functioning transplant should be excluded from the calculation.]

by George Taniwaki

The short answer is yes. The long answer is complex and interesting. (Well, it’s interesting if you are a statistics geek like me.)

First, some good news. Most of the available data indicates that live kidney donors lead long healthy lives. Studies show that they live longer than the general population. For instance, see Transpl. Oct 1997 and New Engl. J. Med. Jan 2009. This is not an unexpected result and does not mean that donating a kidney will lengthen your life. Instead, it is probably a result of the fact that kidney donors are screened for good health (called selection bias) and are healthier than the general population, and thus more likely to live longer.

NewEnglJMed2009

Survival rate of kidney donors is similar to general population. Image from New Engl J Med

A more meaningful comparison would be to look at longevity of kidney donors compared to a stratified sample of the general population controlled for age, income, gender, geography, medical history, and access to health care (or health insurance). Such a study would be difficult to conduct. That’s because neither hospitals nor the United Network for Organ Sharing (UNOS) do a good job of tracking kidney donors after surgery. They do a better job of tracking recipients. So the data on the long-term outcomes of donors is sparse.

Donating a kidney does expose donors to several near-term risks that may shorten their lives. A study in J. Amer. Med. Assoc. Mar 2010 shows that in the 90 days after a donation, the mortality rate was 3.1 per 10,000 for donors compared to 0.4 per 10,000 for a control group. A good summary of these risks is provided by the Mayo Clinic and by the National Kidney Foundation. Actual risks may vary and donors should discuss them with the transplant surgeon. However, the risks are small, especially when compared to the great benefits that will be experienced by the recipients. Not all researchers are quite as sanguine. A note in Clinical J. Amer. Soc. Nephr. Jul 2006 cautions that more studies are needed.

For the long-term, the risk of premature death are low. The same JAMA study cited above shows the long-term survival is excellent. The risk of death was the same or lower than for the control group after five years (0.4% vs. 0.9%) and after 12 years (1.5% vs. 2.9%), respectively.

There is one risk that is correlated with kidney donation that is very odd and deserves additional investigation by epidemiologists. Specifically, it appears that kidney donors are more likely than the general population to develop end stage renal disease (ESRD). My friend, Ken Klima at Hebert Research, heard this surprising finding in a UWTV lecture entitled Understanding a Chronic Killer: Kidney Disease, Part 1 (additional kidney related videos are also available). The data is reported in a rather shocking manner by Wendell Fleet a professor of nephrology at the UWMC, which is where I am expecting to have my surgery. At 34:40 into the video, he says:

“If you donate one of your kidneys to a loved one, or in a fit of philanthropic zeal to a total stranger (audience laughs), you may wear out your kidney. We initially told people you only need one, ‘give it up and save someone’s life.’ So we followed those people and in a few of them the creatinine levels inch up. A few have required renal replacement therapy. They wore out their remaining kidney. On the positive side, you go directly to the top of the list for a transplant yourself if you give someone a kidney (audience laughs).”

Dr. Fleet may be referring to data from various studies, such as one reported in Transpl. Nov 2002 or Transpl. Proc. June 2008 (subscription required), that show that among patients undergoing living donor nephrectomies, about 0.35% developed ESRD compared to 0.25% for the general population. Although this is a difference of only 0.10%, it represents a huge increase of 40% (=0.10/0.25). Am I putting myself at risk for kidney disease by donating my kidney?

I don’t think so. I have a guess as to what’s really happening. Susceptibility to kidney disease is partially hereditary, as are other chronic conditions like diabetes and hypertension that are correlated with ESRD. Since historically a majority of kidney donors are family members of the recipient, they may have also inherited the genes that cause ESRD. A similar conclusion is stated in an editorial in the Nephr. Dialy. Transpl. May 2003. Again, a more complete analysis would compare rates of ESRD correcting for age, income, gender, geography, access to medical care, and medical history (especially a family history of ESRD).

For more information on becoming a kidney donor, see my Kidney donor guide.

[Update1: I added a link to a study that questions the low medical risks reported for live kidney donors.]

[Update2: I added a link to a new JAMA study.]

[Update3: An Aug 2010 blog post contains additional findings on the safety of kidney donation.]