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Keywords:

  • Expanded criteria donor;
  • hepatitis;
  • HIV;
  • kidey transplantation;
  • organ donation

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Kidneys from organ donors who have behaviors that place them at increased risk for infection with human immunodeficiency virus (HIV) or hepatitis C virus (HCV) are often discarded, even if viral screening tests are negative. This study compared policies that would either ‘Discard’ or ‘Transplant’ kidneys from Centers for Disease Control classified increased-risk donors (CDC-IRDs) using a decision analytic Markov model of renal failure treatment modalities. Base-case CDC-IRDs were current injection drug users (IDUs) with negative antibody and nucleic acid testing (NAT) for HIV and HCV, comprising 5% of kidney donors. Compared to a CDC-IRD kidney ‘Discard’ policy, the ‘Transplant’ policy resulted in higher patient survival, a greater number of quality-adjusted life-years (QALYs) (5.6 vs. 5.1 years per patient), more kidney transplants (990 vs. 740 transplants per 1000 patients) and lower cost of care ($60 000 vs. $71 000 per QALY). The total number of viral infections was lower with the ‘Transplant’ policy (13.1 vs. 14.8 infections per 1000 patients over 20 years), because the ‘Discard’ policy led to more time on hemodialysis, with a higher HCV incidence. We recommend that kidneys from NAT-negative CDC-IRDs be considered for transplantation since the practice is estimated to be beneficial from both the societal and individual patient perspective.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

In 1994 the Centers for Disease Control (CDC) advised the transplant medical community that organs from seronegative donors with behavioral risk factors (Table 1) for human immunodeficiency virus (HIV) infection should not be transplanted ‘unless the risk to the recipient of not performing the transplant is deemed to be greater than the risk of HIV transmission and disease (e.g. emergent, life-threatening illness requiring transplantation when no other organs/tissues are available and no other lifesaving therapies exist)’. (1) This recommendation has been generally interpreted as meaning that transplantation of ‘life-saving’ organs like the heart, liver and lungs from such donors can be justified, because the risk of death without these transplants would likely outweigh the risk of viral transmission. However, transplant centers have often declined to use kidneys from persons the Centers for Disease Control classifies as increased-risk donors (CDC-IRDs) because the availability of dialysis eliminates the risk of imminent death when a kidney transplant is not done. As a result, kidneys from such donors have been routinely discarded by most transplant centers.

Table 1.  CDC-defined behavior/history exclusion criteria for organ donors
1. Men who have had sex with another man in the preceding 5 years.
2. Persons who report nonmedical intravenous, intramuscular or subcutaneous injection of drugs in the preceding 5 years.
3. Persons with hemophilia or related clotting disorders who have received human-derived clotting factor concentrates.
4. Men and women who have engaged in sex in exchange for money or drugs in the preceding 5 years.
5. Persons who have had sex in the preceding 12 months with any person described in items 1–4 above or with a person known or suspected to have HIV infection.
6. Persons who have been exposed in the preceding 12 months to known or suspected HIV-infected blood through percutaneous inoculation or through contact with an open wound, nonintact skin or mucous membrane.
7. Inmates of correctional systems.

Certain developments since the CDC issued its recommendations suggest that a re-examination of the practice of routine discard of kidneys from donors at increased risk may be warranted, including: (i) It is now known that kidney transplantation is a ‘life-saving’ procedure, in that recipients live much longer than those on dialysis (2,3), so the risk to the recipient of not performing a kidney transplant is greater than was once believed; (ii) Laboratory screening tests for HIV and hepatitis C virus (HCV) have become more rapid and sensitive, so the chance of detecting a recent infection in the donor before transplantation is greater; (iii) Survival rates for HIV- or HCV-infected patients with renal failure are improving, so the mortality risk to the recipient as a consequence of disease transmission is lower than once believed (4–7).

A major barrier to transplantation of kidneys from CDC-IRDs is that the associated risks and benefits compared to waiting for a standard donor kidney given the recent advancements are unknown. This lack of information impairs the informed consent process and leads to a decision perceived as being ‘safest’ for both the potential recipient and transplant surgeon, which is to discard the kidney. The current study was performed to formally analyze the most relevant consequences of this decision.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Study design

A cost-effectiveness analysis from the societal perspective was performed on hypothetical cohorts of patients on hemodialysis awaiting a deceased donor kidney transplant. The analysis was designed to compare outcomes for policies that would either ‘Discard’ or ‘Transplant’ kidneys from CDC-IRDs (Figure 1). We focused our analysis on four groups of CDC-IRDs who were engaging in the risk behavior at the time of donation: (i) Injection drug users (IDUs); (ii) Men who have sex with men (MSM); (iii) Commercial sex workers (CSWs) and (iv) Prison inmates (Inmates).

image

Figure 1. Decision tree representing two management policies for hemodialysis patients on the kidney transplant waitlist regarding use of kidneys from CDC-IRDs.

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A decision-analytic Markov model was developed to simulate progression of cohorts of patients with end stage renal disease through health states they would typically traverse (Figure 2). The model considered the patients' risk of death (adjusted for age and the presence of HIV or HCV infection), probability of receiving a transplant (either from a CDC-IRD or a standard donor), the risk of infection with HIV or HCV (through an infected CDC-IRD kidney, dialysis unit or community exposure) and the risk of transplant kidney failure. Estimates for these event probabilities, as well as the utilities and costs associated with Markov health states, were taken from the published literature and national end-stage renal disease database reports (7,8). The main outcome measures were patient survival, quality-adjusted life-years (QALYs), number of kidney transplants, cost of care and incidence of HIV and HCV infections.

image

Figure 2. Markov trees for ‘Transplant’ and ‘Discard’ policies, which are the same, except the former tree includes the branches labeled ‘T’ (get transplant from CDC-IRD).

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Study populations

For the base-case analysis we studied a cohort of 50-year-old hemodialysis patients without HIV or HCV infection, with no live kidney donors, who were on the 2002 national US kidney transplant waitlist and willing to accept kidneys from CDC-IRDs. In order to simplify the analysis by eliminating concerns about HBV transmission with a CDC-IRD transplant, patients were assumed to have immunity to acute HBV infection (9). Base-case CDC-IRDs were current IDUs, who had negative antibody and nucleic acid screening tests for HIV and HCV. Separate analyses for the other CDC-IRD groups were done by using the incidence and prevalence rates of HIV and HCV infection in the risk population.

Markov analysis

A Markov process is a decision analysis modeling technique that follows a cohort of patients as they transition from one health state to another over time (10). The conclusions of the current analysis were drawn by comparing the outputs of two similar Markov processes, differing only by whether there were branches in the tree which allowed the possibility of kidney transplantation from a CDC-IRD. The model was analyzed using TreeAge Pro Suite, Release 1.3, TreeAge Software (Williamstown, MA, USA).

For the current study we defined nine health states: (i) ‘Waitlist; No HIV or HCV’ for patients on hemodialysis and on the kidney transplant waitlist, with no HIV or HCV infection; (ii) ‘Transplant; No HIV or HCV’ for patients with a functioning kidney transplant, also with no HIV or HCV infection; (iii) ‘Waitlist; HIV’ for patients on the waitlist and infected with HIV; (iv) ‘Waitlist; HCV’ for patients on the waitlist and infected with HCV; (v) ‘Waitlist; HIV + HCV’ for patients on the waitlist and infected with both HIV and HCV; (vi) ‘Transplant; HIV’ for patients with a transplant and HIV; (vii) ‘Transplant; HCV’ for patients with a transplant and HCV; (viii) ‘Transplant; HIV + HCV’ for patients with a transplant and both HIV and HCV and (ix) ‘Dead’ for deceased patients. At the beginning of the analysis, all patients were in the ‘Waitlist; No HIV, HCV’ health state. Cycle lengths were set at 1 year and the analysis was terminated after 20 cycles (20 years). Transitions between health states depended on the annual probability that a patient would die, acquire a new HIV or HCV infection, receive a kidney transplant (for those on the waitlist) or return to dialysis (for those with a transplant). The ‘Transplant’ health states were modeled as ‘tunnel’ states, with a higher probability of graft loss, death and higher monetary costs during the first year after transplant than in subsequent years. For the health states where patients were at risk for death, the probability of death during a cycle was derived from published age-stratified annual survival rates (7,8,11).

Each health state was assigned a value for the monetary cost and quality of life utility ascribed to a patient spending one cycle in that state (Table 2). The monetary costs taken from the medical literature were adjusted to 2002 US dollars using the medical care component of the consumer price index. The cost of stat HIV and HCV nucleic acid testing (NAT), estimated at $500 per donor or $250 per kidney (Charles Alexander, Chief Executive Officer, Transplant Resource Center of Maryland, Catonsville, MD, November, 2006, personal communication), was added to the cost of a CDC-IRD transplant. Data on quality of life was obtained from studies that expressed this parameter in terms of a utility, ranging between 0 (representing death) and 1 (representing the best possible life). An aggregate utility for health states was estimated when necessary by calculating the product of the separate utilities (12). Both monetary costs and utilities were discounted at 3% per year (13).

Table 2.  Parameters for Markov process and sensitivity analyses
 Base-CaseRangeSource
  1. py = person-year; USD = US dollars; IDV = Injection drug user; QOL = Quality of Life; CDC-IRDs = Centers for Disease Control classified increased-risk donors; CSW = commercial sex workers; MSM = men who have sex with men.

  2. 1No data; estimated at half the HCV incidence in IDUs, since sexual transmission of HCV is low and half of CSWs are IDUs (47).

  3. 2All rates converted to probabilities in the Markov analysis.

Risk of HIV or HCV Infection
Calculation of residual risk of infection in CDC-IRD populations
Length of window period for donor screening test (days) 14
  HIV antibody alone22 
  HIV antibody plus NAT11 
  HCV antibody alone70 
  HCV antibody plus NAT10 
IDU infection incidence
  Incidence of HIV infection (per 100 py)21–326–30
  Incidence of HCV infection (per 100 py)2110–4530–33
MSM infection incidence
  Incidence of HIV infection (per 100 py)31–1226,34,35
  Incidence of HCV infection (per 100 py)0.20–136,37
CSW infection incidence
  Incidence of HIV infection (per 100 py)103–3026,27,38,39
  Incidence of HCV infection (per 100 py)105–23See footnote 1
Inmates infection incidence
  Incidence of HIV infection (per 100 py)0.20–0.440–42
  Incidence of HCV infection (per 100 py)10.3–623,40,43
Calculation of infection risk due to lab error in CDC-IRD populations
  False-negative testing error rate (%)0.0515
IDU infection prevalence
  Prevalence of HIV infection (%)181–4926,44
  Prevalence of HCV infection (%)3810–9017,32,33,45
MSM infection prevalence
  Prevalence of HIV infection (%)250–4034,35,44
  Prevalence of HCV infection (%)42–1817
CSW infection prevalence
  Prevalence of HIV infection (%)240–6026,38,44,46,47
  Prevalence of HCV infection (%)126–4517,48,49
Inmates infection prevalence
  Prevalence of HIV infection (%)21–1726,40,41,50
  Prevalence of HCV infection (%)2316–4123,40,50
Infection in US and ESRD populations
US population
  Incidence of HIV infection (per 100 py)0.02051
  Prevalence of HIV infection (%)0.12852
  Incidence of HCV infection (per 100 py)0.01153
  Prevalence of HCV infection (%)1.853
Hemodialysis patients
  Incidence of HIV infection (per 100 py)0.020US population
  Incidence of HCV infection (per 100 py)0.340.1–317,19
Kidney transplant patients
  Incidence of HIV infection (per 100 py)0.020US population
  Incidence of HCV infection (per 100 py)0.011US population
Transplant Waitlist
 Number of patients on US kidney waitlist, end of year 2002505358
 Annual number of standard deceased donor kidney transplants, 200282888
 Median time to transplant for new waiting list registrations, 20023.58
 Annual standard donor kidney transplant rate 20.208
 Percent of patients on waitlist who would be candidates for CDC-IRD kidney5%1–30%Estimated
 Number of patients on waitlist who would be candidates for CDC-IRD kidney2527505–15160Calculated
 Percent of kidney donors who are CDC-IRDs5%2–8%Estimated
 Potential number of CDC-IRD kidney transplants, 2002414166–663Calculated
 Median waiting time for CDC-IRD kidney transplant (years)3.5Calculated
 Annual CDC-IRD kidney transplant rate 20.200.02–0.53Calculated
 Age when adult patient is first placed on the waitlist5018–658
Mortality
 Annual death rate among all patients on US kidney waitlist 2 8
  Age 18–340.037 
  Age 35–490.054 
  Age 50–640.092 
  Age 65+0.129 
 Annual death rate among kidney transplant recipients, 1st year 7
  Age 18–340.023 
  Age 35–490.039 
  Age 50–640.080 
  Age 65+0.116 
 Annual death rate among kidney transplant recipients, after 1st year 7
  Age 18–340.021 
  Age 35–490.023 
  Age 50–640.040 
  Age 65+0.072 
 Relative risk of death due to HIV infection1.51.0–2.04,5,7
 Relative risk of death due to HCV infection1.71.3–2.37,54,55
Transplant
 Probability of graft failure after kidney transplant (death-censored) 7
  First year after transplant0.060.04–0.07 
  Subsequent years0.040.02–0.06 
 Relative risk of graft failure due to HIV infection1.01.0–1.35,7
 Relative risk of graft failure due to HCV infection1.61.4–1.87,56
Utilities for Health States
 Annual Cost (×$1000 USD, 2002)
  Hemodialysis6149–717
  Transplant (1st year)9776–1777
  Transplant (subsequent)2117–387
  HIV infection2119–2357,58
  HCV infection21–3059–62
QOL for health states
 Hemodialysis QOL utility0.570.41–0.6463–65
 Transplant QOL utility0.700.62–0.8263–65
 HIV infection QOL utility0.820.45–1.0066,67
 HCV infection QOL utility0.780.60–0.8668,69

Risk of HIV or HCV infection

HIV or HCV infection from a seronegative donor could occur if (i) the donor is in the infective window-period between viral exposure and detectability by screening tests or (ii) the serologic markers are actually at detectable levels in the donor, but are reported negative because of procedural error (14,15). The first situation, called the ‘residual risk’ of viral transmission during early infection, depends on the incidence rate of viral infection in the donor's population and the length of the screening test's window period (16). In the second case, the risk of infection that is not detected because of laboratory error depends on the prevalence of undiagnosed viral infection in the donor's population and the false negative rate of the screening test. Residual risk of HIV or HCV infection for groups of CDC-IRDs was calculated as the product of the seroincidence of infection and the duration of window periods for viral testing assays (14,16). The risk of undetected infection due to a laboratory error was calculated as the product of the seroprevalence of infection and the probability of a laboratory error (15). We assumed that a reasonable estimate of the risk of infection in a seronegative donor was given by the sum of the residual risk and the infectious risk due to lab error. We also assumed that the risk of a false negative test because of a variant virus or an incomplete immunologic response was relatively small and they were not factored into the analysis. Since the probability of both HIV and HCV being transmitted by a seronegative donor was very low, it was also not included in analysis.

Estimate of the annual CDC-IRD kidney transplant rate

The annual CDC-IRD kidney transplant rate was derived from existing data on the time-to-transplant (TT) for standard deceased donor kidney transplants (8). Published data indicate that the TT is a function of the size of the kidney transplant waiting list and an inverse function of the number of deceased donor kidney transplants performed per year. This relationship, derived from US national transplant data from 1994 to 2000, was used to estimate the TT and annual CDC-IRD kidney transplant rate, assuming 5% of the 2002 national waiting list would be CDC-IRD transplant candidates and 5% of donors would be CDC-IRDs.

The estimate for the portion of the waitlist willing to accept a kidney from a CDC-IRD was empirically set at a low level (5%), based the assumption that most transplant candidates would be reluctant to accept a kidney which carried any increased risk of infection. Due to the uncertainty of this parameter estimate, a wide range was used for the sensitivity analysis (1–30%). The estimate of 5% for the portion of kidney donors who are CDC-IRDs was based on the prevalence of persons with increased-risk behaviors in the US population (17).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Base-case analysis

A waitlist cohort managed with a ‘Discard’ CDC-IRD kidney policy received 740 kidney transplants from standard donors per 1000 waitlist patients over a 20-year period. When managed with a ‘Transplant’ policy, patients received 495 kidneys from standard donors, plus an additional 495 kidneys from CDC-IRDs, for a total of 990 kidneys per 1000 waitlist patients. Over the 20-year time horizon the ‘Transplant’ policy allowed patients to spend less time on the transplant waitlist than the ‘Discard’ policy (3.1 vs. 4.6 years) and more time with a functioning transplant (8.0 vs. 5.7 years, Figure 3). Patient survival for the ‘Transplant’ policy was the same as the ‘Discard’ policy at 1 year (91%), but higher at 5 years (68% vs. 65%), 10 years (49% vs. 45%) and 20 years (23% vs. 20%). The ‘Transplant’ policy also led to a higher number of discounted, quality-adjusted life years (QALY) than the ‘Discard’ policy (5.6 vs. 5.1 years). The cost of caring for patients over 20 years was lower with the ‘Transplant’ than the ‘Discard’ policy ($338 000 vs. $363 000; $60 000 vs. $71000 per QALY). So in the base-case analysis the ‘Transplant’ policy was both less costly and more effective than the ‘Discard’ policy.

image

Figure 3. Base-case model predictions of the number of patients in the ‘Waitlist’, ‘Transplant’ and ‘Dead’ health states for cohorts of 1000 patients. The ‘Discard’ policy was associated with more patients left on the waitlist, fewer patients with a functioning transplant and more dead patients than the ‘Transplant’ policy.

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The model predicted that, compared to the ‘Discard’ policy, the ‘Transplant’ policy would lead to more HIV infections (2.3 vs. 1.9 HIV infections per 1000 patients over 20 years), but fewer HCV infections (10.8 vs. 12.9 HCV infections). Of the infections with the ‘Transplant’ policy, 0.3 of the HIV infections and 2.9 of the HCV infections were window-period infections from CDC-IRDs and the rest were dialysis unit or community acquired. The total number of viral infections was lower with the ‘Transplant’ policy (13.1 vs. 14.8 infections per 1000), because the ‘Discard’ policy led to more time on hemodialysis, with its relatively high HCV incidence (Figure 4).

image

Figure 4. Cumulative number of HIV and HCV infections over a 20-year period for a cohort of patients managed with either a ‘Discard’ or ‘Transplant’ policy, stratified by CDC-IRD groups and showing the source of the infections. Most infections with both the ‘Discard’ and ‘Transplant’ policies were due to acquisition from the dialysis unit or community, rather than through transmission with transplantation. Availability of the extra CDC-IRD kidneys with the ‘Transplant’ policy reduced the average time on hemodialysis and the dialysis unit HCV exposure, thereby lowering the cumulative number of infections.

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Individual CDC-IRD group analyses

Since there was substantial variability in the risk of HIV and HCV infection among the different CDC-IRD groups and to facilitate application of the analysis by transplant practitioners to evaluation of actual donors, separate analyses were conducted for each group. Output from these analyses showed that for the other CDC-IRD groups, outcomes were nearly identical to those for the base-case, except for the number of HIV infections per 1000 patients (Figure 4), which were 2.4 for MSM donors (0.5 from CDC-IRD transmission, 1.9 from the community), 3.4 for CSW donors (1.5 from CDC-IRD transmission, 1.9 from the community) and 2.0 for inmate donors (0.1 from CDC-IRD transmission, 1.9 from the community) and the number of HCV infections, which were 8.0 for MSM (0.1 CDC-IRD, 7.9 community), 9.3 for CSW (1.4 CDC-IRD, 7.9 community) and 8.1 for inmates (0.2 CDC-IRD, 7.9 community). Therefore, regardless of the CDC-IRD group, the cumulative number of HIV or HCV infections was lower with the ‘Transplant’ policy.

One-way sensitivity analyses

One-way sensitivity analyses were performed to determine if the conclusions of the base-case analysis changed with variations of the input parameters over the range of plausible values shown in Table 2. These analyses showed that the conclusions of the base-case analysis described above were not substantially changed by modifications of input parameters within a wide range of probable values, except that the relative number of infections between the two policies was sensitive to both the incidence of infection in CDC-IRDs and the incidence of infection on hemodialysis. The relationship between these two parameters is depicted by the two-way sensitivity analysis in Figure 5, showing that the ‘Discard’ policy would yield fewer HCV infections only in a setting where a recipient's risk of infection on dialysis is very low, while the probability of CDC-IRD infection in a donor is high.

image

Figure 5. Two-way sensitivity analysis showing the relationship between plausible ranges of HCV incidence on hemodialysis, the HCV incidence in the donor population and the number of HCV infections predicted by the model. The ‘Transplant’ policy yields more HCV infections only for situations where there is both a low infection incidence in the recipient's hemodialysis unit and a high HCV incidence in the CDC-IRD donor population.

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Multiway sensitivity analysis

A multiway sensitivity analysis was conducted to observe the outcome of a worst-case scenario which would favor the discard of CDC-IRD kidneys, including a high rate of HIV and HCV infection in the kidney donor (HIV incidence = 25 infections per 100 person-year, HCV incidence = 25 infections per 100 person-year; prevalence of both = 50%), a low incidence of HCV infection in the patient's dialysis unit (0.1 infections per 100 person-year), a low probability of receiving a CDC-IRD kidney offer because of a large number of candidates (30% of the waitlist) and a small number of kidneys (2% of donors) and a high cost ($30 000 per year) and low utility (0.45) associated with an HIV or HCV infection. Results of the worst-case scenario still favored the ‘Transplant’ policy since it resulted in more kidney transplants (775 vs. 742 transplants) and patients spent less time on the waitlist (4.4 vs. 4.6 years), had more time with a functioning transplant (6.1 vs. 5.7 years), had a higher survival, more QALYs (5.2 vs. 5.1 years) and lower cost of care ($361 000 vs. $364 000; $69 000 vs. $71 000 per QALY). In this scenario there were more HIV infections (2.4 vs. 1.9 HIV infections per 1000 patients) and more HCV infections (4.6 vs. 4.3 HCV infections) than with the ‘Discard’ policy.

Analysis without HIV or HCV NAT

Since local resources and the time constraints of organ viability with cold storage might eliminate the possibility of CDC-IRD NAT, a separate analysis was conducted as though only HIV and HCV antibody testing were available. This was done by raising the screening test window period in the base-case analysis to 22 days for HIV and 70 days for HCV. In this analysis, the ‘Transplant’ policy was still superior to the ‘Discard’ policy in that it led to more transplants (990 vs. 740), less time on the waitlist (3.1 vs. 4.6 years), more time with a functioning transplant (8.0 vs. 5.7 years), higher survival, more QALYs (5.6 vs. 5.1 years) and lower cost of care ($338 000 vs. $363 000; $60 000 vs. $71 000 per QALY). However, substantially more infections occurred with the ‘Transplant’ policy (2.6 vs. 1.9 HIV infections per 1000 patients; 27.7 vs. 12.9 HCV infections) than with the ‘Discard’ policy. With no NAT available, most of the HCV infections with the ‘Transplant’ policy (19.8) occurred through transmission from the CDC-IRD transplants.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

There are now more than 65 000 patients on the kidney transplant waiting list in the United States, which has doubled in size over 10 years. In contrast the number of organ donors only increased by one-third during the same time period, creating longer waiting times for all blood groups. By the year 2010 there may be 100 000 patients on the US kidney waiting list, with an average waiting time of nearly 10 years (18). The fact that renal transplantation reduces the mortality of patients with end-stage renal disease (ESRD) heightens the urgency to identify more sources of transplantable kidneys. Recent initiatives to utilize kidneys from expanded criteria donors have provided a modest increase in the supply of organs, but the results are less than ideal.

Our analysis indicates that behavioral risk factors for acquisition of HIV or HCV, which can raise the possibility of undetected recent infection 1000-fold, should not automatically exclude deceased kidney donors. We found that the possibility of viral exposure from kidney donors with risk behaviors was overshadowed by the advantages of having additional kidneys in the pool, including increased quality-adjusted survival and lower costs for cohorts of patients willing to accept such kidneys. Surprisingly, we also found that transplantation of kidneys from donors with risk behaviors was also associated with fewer viral infections over the 20-year analysis period than a discard policy.

The observation that fewer viral infections occurred when cohorts were managed with a policy which transplants kidneys from CDC-IRDs emerged because the risk of infection from an NAT-negative CDC-IRD kidney transplant was approximately equivalent to that of acquiring HCV in 1 or 2 years on hemodialysis. The availability of extra kidneys from CDC-IRDs shortened the average time patients needed to remain on hemodialysis, thereby reducing the hemodialysis-associated infection exposure and shifting the overall infectious risk in favor the transplant policy. This balance was sensitive to the incidence of HCV on hemodialysis. The base-case analysis, in which transplantation of CDC-IRDs led to fewer infections, used an annual hemodialysis HCV incidence rate of 0.34%, as reported in 2005 in the CDC's 2002 national US surveillance (19). Much higher incidence rates have been reported by others and if the latter are more reflective of a patient's actual risk of HCV in a particular dialysis unit, an argument to accept a CDC-IRD kidney would be further supported (20–22).

The cumulative number of infections in cohorts managed with the CDC-IRD transplant policy was sensitive to the risk of undetected infection in CDC-IRD kidneys, which was a function of the length of the infectious window period of the screening test and the incidence of viral infection in the CDC-IRD population. The infection advantage with the transplant policy was realized despite the assumption of CDC-IRD risk behavior right up to the time of donation. Most CDC-IRDs encountered in clinical practice would have lower infection risk than this, especially if there were reasonable certainty that there had been no risk behavior within the screening test's window period before donation.

The analysis demonstrated the benefit of testing CDC-IRDs with NAT, because the infection rate advantage of a CDC-IRD kidney transplantation policy in the base-case analysis was lost if the longer window period of antibody-only testing was used. While preferable, NAT testing may not be available to some transplant centers within a timeframe of organ viability. This should not necessarily exclude use of kidneys from CDC-IRDs, since transplantation with negative antibody testing would still have the advantages of more kidneys available for transplant, less time for candidates on the waitlist, more time with a functioning transplant, higher patient survival, more QALYs and lower cost of care, compared to discard of the kidneys. The possibility of viral transmission could be mitigated by strategies like utilizing only CDC-IRDs who have not practiced risk behaviors within the screening test's window period before donation, limiting recipients to those already infected with the virus predominantly associated with the CDC-IRD's behavior and excluding donors at highest risk.

It is important to recognize that, while there were fewer total viral infections with the CDC-IRD transplant policy, the number of HIV transmissions increased. Any HIV transmission with transplantation would not only harm the recipient, but also exert a deleterious effect on the field through the publicity it would invoke. Since HIV transmission is such an unacceptable outcome, implementation of a policy to transplant CDC-IRD organs might best begin with the lower risk subgroups. Prospective data on the policy's impact could then guide future decisions to include the subgroups at highest risk.

There is scant data at hand to directly estimate the percent increase in deceased donor kidneys that persons with risk behaviors would yield. Our base-case estimate of 5% was conservatively derived from reported prevalence rates of risk behaviors in the US population, of which 5% are men who have sex with men, 5% and 0.5% are past or current IDUs, 4% have had over 50 sex partners and 0.7% are incarcerated (17,23). In some US cities the prevalence of HIV risk factors is much higher (24,25). If our base-case estimate of 5% more kidneys from utilization of CDC-IRDs were borne out in clinical practice, then over 400 more kidney transplants could be performed in the United States each year.

A limitation of this study is the accuracy of available epidemiologic data pertaining to HIV and HCV infections in CDC-IRD groups. Such data are derived from high-risk population surveys, which may be biased by self-selection, mobility and nonparticipation of persons with marginal lifestyles and influenced by coincidence of multiple risk factors, misclassification of risk behaviors and geographic variations (26). However, for the individual patient or surgeon who is trying to decide whether to proceed with a kidney transplant from a particular CDC-IRD, it is probably sufficient to offer an estimate of the infectivity risk, as outlined in Table 3.

Table 3.  Estimated percent of donors by CDC-IRD group who have HIV or HCV infection despite negative antibody and NAT
CDC-IRD Group1% of Seronegative Donors with with Infection2 
  1. 1IDUs = injection drug users; MSM = men who have sex with men; CSWs = commercial sex workers.

  2. 2Calculated from data in Table 2. Donor assumed to be currently engaged in increased-risk behavior. Infection risk would be less if donor was known to be abstinent from behavior within the viral screening test's window period before donation.

 AverageHigh-risk
HIV
IDUs0.070.11
MSM0.100.38
CSWs0.310.93
Inmates0.010.02
HCV
IDUs0.591.28
MSM0.010.04
CSWs0.280.65
Inmates0.040.18

The transplant community uses guidelines published by the CDC over 10 years ago to decide which donors should be excluded. They were developed during the peak period of AIDS reporting in the United States, before highly active antiretroviral therapy became available in 1996 and before rapid NAT was accessible. While they are dated, they still represent a standard of care that many practitioners are reluctant to contravene. A particularly vexing aspect of the guidelines is the 5-year interval that must elapse before certain risk behaviors need not be considered a barrier to donation. This waiting period is nearly 200 times greater than the HIV and HCV NAT window periods. Without major revision or elimination of the guidelines, significant expansion in CDC-IRD utilization could result from closer alignment of these waiting periods to the testing windows.

In conclusion we recommend that kidneys from NAT-negative CDC-IRDs be considered for transplantation. Compared to the current practice of routine discard of these kidneys, transplantation would be beneficial from a societal perspective because it would lower the cost of care for patients who are candidates. It would also be beneficial from the individual patient's standpoint because transplantation of CDC-IRDs would lead to better quality-adjusted survival and fewer viral infections. Prior to establishment of a new national policy supporting the use of CDC-IRD kidneys, the logistical, financial and ethical ramifications should be vetted through a United Network for Organ Sharing (UNOS) sponsored public forum.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References
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