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Original Clinical Science

The Relative Benefits and Costs of Solid Phase Bead Technology to Detect Preformed Donor Specific Antihuman Leukocyte Antigen Antibodies in Determining Suitability for Kidney Transplantation

Nguyen, Hung T. Do MBBS, FRACP1; Lim, Wai H. MBBS, FRACP, PhD1,2; Craig, Jonathan C. MBBS, PhD3,4; Chapman, Jeremy R. MD, FRACP, FRCP4; Lord, Sarah J. MBBS, MSc5,6; Howard, Kirsten BSc (Hons1), M AppSc, MPH, MHlthEc, PhD3; Wong, Germaine MBBS, FRACP, PhD3,4

Author Information
doi: 10.1097/TP.0000000000000697

Preformed donor-specific anti-HLA antibodies (DSA) in kidney transplantation are associated with an increased risk of developing acute antibody-mediated rejection (AMR), transplant glomerulopathy and graft loss.1-8 The complement-dependent cytotoxicity (CDC) crossmatch is the standard screening test for detecting pretransplant complement-fixing DSA in deceased and living donor kidney transplantation worldwide. Positive T-cell CDC crossmatch is considered an absolute contraindication for transplantation because of the potential risk of hyperacute rejection and early graft loss resulting from high levels of class I DSA. On the contrary, the clinical significance of an isolated positive B-cell CDC crossmatch is unclear. An isolated B-cell crossmatch positivity may reflect the presence of class II and/or low levels class I DSA and has also been shown to be associated with poorer longer-term allograft outcomes.9,10 False-positive findings may occur in the presence of autoantibodies that are considered to be of no clinical relevance.

In contrast to the CDC crossmatch, the newer solid phase assays, such as the bead-based multiplex assays, are capable of identifying and quantifying DSA with a greater degree of test sensitivity and specificity.11 There is now consistent evidence showing a concentration-dependent relationship between higher levels of preformed DSA and poorer allograft outcomes, including rejection and graft loss, but the thresholds at which these adverse events occurred have not been determined.2,4,12-14 Over the past decades, transplant units worldwide have adopted the solid phase bead-based assays as the preferred screening tool to identify preformed DSA,15-17 but the costs and longer-term benefits of this technique are unknown. Decision analytical modeling, which takes into account the uncertainties of the existing evidence, compares the expected costs and consequences of decision options by synthesizing information from multiple sources, with the ultimate goal of providing clinicians and policy makers the best available evidence to reach a decision.18 The aims of this study were 2-fold. First, to estimate the average and incremental health benefits and health care costs of using the bead-based assay as an add-on test to the CDC crossmatch for the detection of preformed DSA compared to the CDC crossmatch alone. Second, to evaluate the relative effects of the various thresholds of preformed DSA and to identify the specific mean fluorescence intensity (MFI) levels that achieve the optimal balance between health benefits and costs for determining transplant suitability.

METHODS

From a third-party payer perspective, 3 probabilistic Markov models (each to evaluate AMR in the first year, 5-year graft loss and quality-adjusted life years [QALY] gained over 20 years) were developed to simulate the natural history of a hypothetical cohort of adult kidney transplant recipients (n=10,000, age ≥ 18 years, living and deceased donor kidney transplant recipients), stratified according to the peak levels of pretransplant class I and/or II DSA.

Structure of the Model

The simplified structure of the model is outlined in Figure 1. The cost-effectiveness model was constructed with 2 arms to compare the health benefits (the number of rejection episodes prevented, number of graft loss prevented, and QALY gained) and costs of screening with the CDC crossmatch alone or the CDC crossmatch and the bead-based multiplex assay. The model considered only those who had received a kidney transplant and, as such, the costs of dialysis for those who did not receive a transplant and remained on dialysis were not included in our analyses. Individual transplant recipients progressed through the different health states (including rejection and transplant glomerulopathy) based on the age-specific transition probabilities. Immunosuppression regimen used during and after transplantation differed according to the pretransplant levels of DSA and the B-cell CDC crossmatch results (Appendix A, SDC,http://links.lww.com/TP/B140). The model assumed all recipients were transplanted only once in their lifetime. All patients with failed transplants were managed either on dialysis or with best supportive care until death. Transplantation was contraindicated in the presence of a positive T-cell CDC crossmatch. The transition probabilities of acute AMR, graft failure, and returned to dialysis were varied according to the different threshold values of DSA. The thresholds for preformed DSA used in the models included: no DSA or 100 MFI or less, 500 MFI or less, 2000 MFI or less, and 5000 MFI or less. The test performance characteristics of the bead-based assay at the various thresholds were derived from a local cohort19 and are shown in Appendix B, SDC,http://links.lww.com/TP/B140. The model time horizon was 1 year when considering the outcomes of acute rejection episodes prevented, 5 years when the numbers of graft losses prevented were considered, and 20 years when the longer-term outcome of QALYs gained were measured.

Figure 1
Figure 1:
Health states of Markov model.

Sensitivity Analysis

Uncertainties surrounding the point estimates for clinical parameters including graft survival, patient survival, and cost were tested over a range of plausible values to assess the robustness the model using sensitivity analysis. One-way sensitivity analyses were used to identify all the influential variables within the model. Probabilistic sensitivity analysis was undertaken using Monte Carlo simulation with assigned distributions to estimate expected values for each variable. We used log-normal distribution for relative risk, gamma distribution for costs, and β and dirichlet distributions for binomial and multinomial probabilities/proportions (Appendix C, SDC,http://links.lww.com/TP/B140). Ten thousand iterations were randomly sampled for each variable of interest.

Ethics

Ethics approval for this study was not required because no new participants were recruited for this study. Clinical parameter estimates were obtained from published literature and from deidentified data from existing registries.

Input Parameter Estimates for the Model

Clinical Data

Clinical input data used are shown in Appendix C (SDC,http://links.lww.com/TP/B140). A comprehensive literature search was conducted to identify the best available data on the probability of acute rejection, transplant glomerulopathy, graft loss, and death after transplantation. Other relevant data such as the probability of acute AMR and graft failure with positive B-cell CDC crossmatch and/or the presence of DSA with solid-phase bead technology were obtained from other published sources.12,19

Cost Data

Itemised cost data used in the model are shown in Appendix D (SDC,http://links.lww.com/TP/B140). The cost of kidney transplantation (including surgical procedure, induction immunosuppressive therapy, outpatient care, and maintenance immunosuppressive drugs), management of rejection (cellular and antibody-mediated), initiation and maintenance of dialysis and the bead-based multiplex assay testing were obtained from the Australian Refined Diagnosis Related Groups,20 the Medicare Benefits Schedule of Australia (December 2012),21 Schedule of Pharmaceutical Benefits (January 2013),22 and published literature. The proportions of patients receiving therapies for induction, maintenance immunosuppression, and treatment of rejection episodes were obtained from the Australian and New Zealand Dialysis and Transplantation registry reports. All costs were converted to the 2011 Australian dollar using the Australian Institute of Health and Welfare Total Health Price Index.23

Health-Related Quality of Life

Health state utilities used are shown in Appendix C (SDC,http://links.lww.com/TP/B140). The dialysis and transplant time-trade off utility-based quality of life weights were derived from published literature.24,25

Model Outcomes

The outcomes of our modeled analyses included the total and incremental costs and health outcomes (expressed as rejection episodes prevented in the first year, grafts saved over 5 years and QALYs gained) of screening using the solid phase bead-based technique in conjunction with CDC to detect pretransplant anti-HLA DSA compared with using CDC alone. The number of potential candidates eligible for transplantation was calculated for each screening program. This represented the number of potential candidates in the simulated cohort with a positive test (using CDC crossmatch and the various MFI threshold values for bead-based assays) that would have precluded these candidates from transplantation. Incremental cost-effectiveness ratio were estimated using the following formula26:

Future costs and benefits were discounted using a discount rate of 5% per annum in accordance with Australian guidelines27 and half-cycle corrections were applied. Discounting takes into consideration the society’s time preference, that is, the inclination to enjoy the benefits now and place less weight on the costs and consequences in the future.28 TreeAge Pro Suite 2013 (TreeAge Software, Williamstown, MA)29 and Microsoft Excel were used to develop and analyse the models.

RESULTS

Comparing Screening Using Bead-Based Assay With CDC Crossmatch Versus CDC Crossmatch Alone

Table 1 shows the baseline characteristics of the hypothetical cohort of kidney transplant recipients. The mean age of recipients was 48 (SD=17) years. The overall annual incidences of AMR were 21.7% and 10.5% among the CDC only and CDC with bead-based assay groups, respectively. Table 2 shows the total and incremental health benefits (in rejection episodes prevented in the first year, 5-year graft loss and QALY gained) and the costs of solid phase bead-based assay as an add-on test to the CDC crossmatch for identifying preformed DSAs and the progression of consequential events, including acute AMR and chronic transplant glomerulopathy.

TABLE 1
TABLE 1:
Baseline characteristics of the hypothetical cohort of transplant recipients
TABLE 2
TABLE 2:
Base case analysis of the costs and benefits of the solid phase bead-based as an add-on test to CDC for identification of preformed DSA compared with CDC alone with varying MFI thresholds

One-Year Rejection

Assuming transplantation only occurred in recipients with no preformed DSAs or with a DSA MFI value of 500 or less, screening using the solid phase bead-based assay and CDC crossmatch prevented a total of 11.2 acute rejection episodes within 1 year after transplantation for every 100 transplants performed compared with CDC crossmatch alone and cost savings of U.S. $25,546. If transplantation was permitted among potential candidates with DSAs to class I and/or II antigens at an MFI of 2000 or less, a total of 10.9 rejection episodes were prevented with additional costs of U.S. $268,797 per 100 transplants.

Five-Year Graft Survival

Compared with CDC crossmatches, the incremental benefits of screening for preformed DSA using the solid phase bead-based technique and CDC crossmatch were prevention of 7.5 transplanted grafts lost per 100 transplants performed at a threshold of value of MFI of 500 or less, with cost saving of U.S. $1,192,303 per 100 transplants. If the threshold was increased to an MFI of 2000 or less, an extra 7.4 grafts would be saved with cost savings of U.S. $867,203 per 100 transplants compared with screening using CDC crossmatch alone.

QALYs Gained

At a threshold value of an MFI of 500 or less, the incremental health gains associated with screening using the solid phase bead-based assay and the CDC crossmatch compared with using the CDC crossmatch alone were 54 QALYs, with cost savings of U.S. $3,968,752 per 100 transplants. Lowering the threshold values to an MFI of 100 or less resulted in incremental gains of 52 QALYs and savings of U.S. $3,858,991 per 100 transplants. If the threshold value was increased to an MFI or 2000 or less, the incremental gains reduced marginally to 51 QALYs with savings of U.S. $3,516,261 per 100 transplants.

One-Way Sensitivity Analyses

The test sensitivity and specificity of the solid phase bead-based technique for predicting AMR (i.e., the likelihood of missing individuals who were at risk of rejection) and the discount rate for costs and benefits were the most influential variables within the model. The results were dominant and favorable toward using bead-based assay with CDC across a wide range of rejection rates (range used for sensitivity analysis: 15% to 60% if CDC negative and bead-based assay positive) and bead-based assay costs (range used for sensitivity analysis: U.S. $750 to U.S. $2300).The extent of the variability on the incremental health outcomes and costs of using bead-based assay with CDC are shown in Figure 2. The vertical dotted line represents the incremental benefit in base case analysis of 54 QALYs gained (a) and cost saving of $3,968,752 (b) per 100 transplants. If the false-negative rates of screening using bead-based assay were varied between 30% and 0% at a threshold MFI value of 500 or less, the incremental gains in health benefits increased from 42 to 58 QALYs with overall gains in savings of at least U.S. $3,143,246 per 100 transplants. If the false-negative rates of CDC crossmatch for predicting AMR were varied from 24% to 15%, there was an increase in the total gains in QALYs from 40 to 67 QALYs, and cost savings of at least U.S. $2,984,760 per 100 transplants, in favor of a combined bead-based assay with CDC strategy. If the discount rate for health benefits and costs was 3% per annum, the health benefit was minus 12 QALY and cost saving U.S. $3,162,016 per 100 transplants. Increasing this to 8% per annum leads to an increase in health gain to positive 128 QALY and savings to U.S. $4,690,534 per 100 transplants. In a threshold analysis, a total of 25 of solid phase bead-based assays may be performed before reaching the “break even” point, whereby the costs associated with using the combined bead-based assay and CDC crossmatch were equalled to that of using the CDC crossmatch alone.

Figure 2
Figure 2:
One-way sensitivity analysis (threshold MFI 500) demonstrating the uncertainties of (A) incremental benefits (QALY gained) and (B) incremental costs of using the solid phase bead-based assay as an add-on test to CDC associated with influential variables in the Markov model.

Probabilistic Sensitivity Analyses

Findings of the probabilistic sensitivity analyses are presented in Appendix E (SDC,http://links.lww.com/TP/B140). The scatter plots illustrate the mean incremental costs and health outcomes, and the uncertainties surrounding the mean parameter estimates associated with screening for preformed DSA using the solid phase bead-based assay and the CDC crossmatch compared with the CDC crossmatch alone. The x axis represents the incremental gains in (i) rejection episodes prevented in the first year, (ii) grafts saved after 5 years and (iii) QALYs gained, whereas the y axis represents the incremental costs of the solid phase bead-based assay with CDC compared with CDC crossmatch alone. The majority of the scatter plots shown in Appendix E (SDC,http://links.lww.com/TP/B140) are located in the southeast quadrant of the cost-effectiveness plane, which represents negative incremental costs and positive incremental effects, indicating that screening with the solid phase bead-based assay and CDC crossmatch is less costly and more effective compared with CDC crossmatch alone when considering 1-year rejection, 5-year graft survival or QALYs gained, regardless of the threshold values of pretransplant DSA.

Comparing the Benefits and Costs of Transplantation at the Different Threshold Values of DSA

Table 3 shows the number of potential candidates eligible for transplantation, the number of episodes of rejection in the first year, and the 5-year graft loss at the various threshold values of pretransplant DSA. Using a threshold DSA MFI value of 500 or less as reference, if transplantation was permitted for potential candidates with preformed DSAs of MFI value of 2000 or less, the total number (and proportion) of eligible recipients would increase by 7 (9.7%), with a marginal rise in the total number of rejection by 0.3 episodes (<0.1%) in the first year or 0.1 (<0.1%) grafts lost after 5 years for every 100 transplants compared to a threshold of an MFI of 500 or less. If the threshold value was increased to an MFI of 5000 or less, an extra 9 (11.3%) candidates would be eligible for transplant but at the expense of one additional rejection episode in the first year and less than 1 graft lost for every 100 transplants 5 years after transplantation.

TABLE 3
TABLE 3:
The impact of varying MFI thresholds of the solid phase bead-based assay on eligibility for transplantation and 5-year graft loss

DISCUSSION

Findings from our modeled analyses suggest that the greatest benefits and cost-savings are achieved if transplantation occurs at a threshold value of an MFI of 500 or less or among those without preformed DSA. If the accepted threshold values for preformed DSAs are an MFI of 500 or less, screening using the combined bead-based assay and CDC will prevent a total of 11 rejection episodes at 1 year, save 6 grafts after 5 years and will gain a total of 54 QALYs over 20 years for every 100 transplants performed. Increasing the threshold value to an MFI of 2000 or less or 5000 or less increases the proportion of eligible recipients by at least 5% to 10%, respectively, at the expense of 1 additional episode of rejection in the first year or 1 additional graft lost after 5 years for every 100 transplants performed. The cost-savings and additional health care benefits observed in the screening arm with the bead-based assay and CDC crossmatch compared with CDC crossmatch alone may be explained by the use of early interventions, such as varying the types of induction therapies, including anti–T-cell therapies; use of desensitization strategies, such as intravenous immunoglobulin and plasma exchange, and using more potent immunosuppression protocols in recipients with high immunological risk.

Preformed DSA is a risk factor for AMR, chronic transplant glomerulopathy, and/or graft loss.2,6,7,30,31 In a single-center study of 402 kidney transplant recipients, increasing levels of pretransplant DSA was associated with a heightened risk of acute AMR, with the greatest excess risk of AMR at an MFI threshold of greater than 6000.2 Similarly, kidney transplant recipients with a coexisting B-cell–positive CDC crossmatch and the presence of class I and/or II DSA are associated with a 1.4-fold increased risk of early graft loss compared to recipients with B-cell–positive CDC crossmatch but without DSAs.12 Findings from our modeled analyses suggested that kidney transplant recipients with DSAs MFI greater than 500 were 10 times more likely to develop AMR compared with recipients with no DSAs or DSAs with an MFI of 500 or less.

The current accepted threshold of preformed HLA DSA for transplant eligibility varies between centres in Australia and worldwide. In Australia, the presence of a Class I DSA with MFI value of greater than 2000 is deemed not suitable for deceased donor organ allocation. In the United States and Europe, no explicit threshold values are defined for transplant eligibility. We have shown that increasing the threshold value to an MFI of 5000 or less may improve the transplant eligibility in 10% of the listed patients, but incur a small, but significant risk of acute rejection and longer-term graft failure even if the B-CDC crossmatch is negative. Balanced against the debilitating and detrimental health outcomes associated with dialysis, such as premature death from cardiovascular disease, accepting a deceased, or living donor transplant for potential candidates with pretransplant DSA of MFI values of 5000 or less, still achieve reasonable survival gains of at least 5 QALYs compared to being on dialysis.

Understanding the relative costs and benefits of a new intervention or diagnostic test compared to a preexisting intervention or test in health care is important to determine the relative efficiency of health production and ensuring maximal health gains are obtained in the context of limited health resources. Although screening for preformed DSAs is considered a standard practice for determining kidney transplant suitability worldwide, the cost-effectiveness of using this add-on test to CDC crossmatch has not been evaluated. A single modeled analysis32 suggested that routine use of flow-cytometric screening is cost-effective, at least among those in centers where the false-negative rates of flow-cytometric screening is low. Similarly, our model was sensitive to the test performance characteristics of the solid phase bead-based assay for AMR. High false-negative rates may have underestimated the immunological risk of transplant recipients, resulting in greater risk of adverse events. On the contrary, the high false-positive rates of a screening test may lead to exclusion of potential candidates from transplantation, prolongation of dialysis and exposure to the consequential complications, such as cardiovascular disease and premature death on dialysis. Few studies have defined the test characteristics of solid phase bead-based assay testing involving large populations lending this as an area for future prospective study.

Our study has several strengths. This is the first modeled analysis that assesses the benefits and costs of screening for preformed DSA in potential candidates for kidney transplantation based on published data. Using probabilistic sensitivity analyses, we have taken into consideration the joined uncertainties surrounding the distribution of individual parameter estimates.

Our study also has potential limitations. First, the test performance characteristics of solid-phase bead technology were based on the findings from a single-center study, and point estimates for clinical outcomes and costs were based on local Australian data. As a result, we may have overestimated or underestimated the true benefits and costs experienced by our patients. Although the estimates of graft survival, patient survival, and costs used in the modeling may differ between countries and regions, our sensitivity analyses demonstrated that the benefits and costs savings achieved are plausible over a wide range of input values suggesting our findings are generalizable to other health care systems outside of Australia. Also, given the potential for interlaboratory and intra- and interassay variation in the MFI levels, our findings must be interpreted in accordance with the individual centers’ practices. Second, the lack of sufficient outcomes data among recipients with high levels of preformed DSAs and those of high immunological risk may have precluded comprehensive analyses of the benefits and costs of the combined bead-based assay and CDC screening strategy. Third, uncertainties in the point estimates of our analyses may exist because we have not considered the clinical significance of the differential class effects of pretransplant DSA and HLA loci beyond HLA-A, -B, and -DR (such as broad antigen mismatches to HLA-Cw, -DQ, and -DP) and graft outcomes. In this study, we assumed that the consequential events that followed after detection of pretransplant DSA are similar between living and deceased kidney transplantations. As such, we have not taken into consideration the potential variations in clinical practice between living and deceased donation. The presence of moderate to high level preformed DSAs may exclude deceased donation within the allocation algorithm, but it may allow sufficient time and effort for intervention, such as desensitization strategies in living donation. Lastly, our analysis only considered the costs of a single screening test before transplantation, whereas in reality, transplant candidates may receive more frequent screening to determine peak and current antibodies.

Implication for Future Research

There is a need for primary studies that determine the test accuracy of bead-based assays for the detection of preformed DSA in potential transplant candidates waiting for a kidney transplant. The correlation between peak/current DSAs and their intensity with clinical outcomes is also unclear. Few studies have shown that preformed peak sera DSA may better predict AMR than current sera DSA.2 Others have reported contradictory findings, suggesting that the current HLA DSA is a better predictor for adverse outcomes, such as graft survival.4,33 Future studies should also examine the clinical significance of detecting DSA directed at other loci, such as HLA-Cw, -DQ, and -DP. Emerging evidence from observational studies have shown differential immunogenicity among DSA directed against a variety of HLA loci,34,35 and these may have detrimental effects on both the graft and patients outcomes.

CONCLUSIONS

The addition of a solid phase bead-based assay to the CDC crossmatch is associated with significant gains in health benefits and is cost-saving compared to CDC crossmatch alone. Identification of preformed DSA is currently an integral part of assessment of transplant suitability. The greatest benefits and cost-savings are achieved if transplantation occurs at a threshold MFI value of 500 or less or in those without preformed DSAs. Increasing the threshold to an MFI of 2000 or less, however, may provide the best balance between improving transplant eligibility without compromising longer-term outcomes, such as rejection and graft loss attributed to pretransplant DSA.

REFERENCES

1. Patel R, Terasaki PI. Significance of the positive crossmatch test in kidney transplantation. N Engl J Med. 1969; 280 (14): 735–739.
2. Lefaucheur C, Loupy A, Hill GS, et al. Preexisting donor-specific HLA antibodies predict outcome in kidney transplantation. J Am Soc Nephrol. 2010; 21 (8): 1398–1406.
3. van den Berg-Loonen EM, Billen EVA, Voorter CEM, et al. Clinical relevance of pretransplant donor-directed antibodies detected by single antigen beads in highly sensitized renal transplant patients. Transplantation. 2008; 85 (8): 1086–1090.
4. Amico P, Hönger G, Mayr M, Steiger J, Hopfer H, Schaub S. Clinical relevance of pretransplant donor-specific HLA antibodies detected by single-antigen flow-beads. Transplantation. 2009; 87 (11): 1681–1688.
5. Vaidya S, Partlow D, Susskind B, Noor M, Barnes T, Gugliuzza K. Prediction of crossmatch outcome of highly sensitized patients by single and/or multiple antigen bead Luminex assay. Transplantation. 2006; 82 (11): 1524–1528.
6. Gloor JM, Sethi S, Stegall MD, et al. Transplant glomerulopathy: subclinical incidence and association with alloantibody. Am J Transplant. 2007; 7 (9): 2124–2132.
7. Jiménez VL, Fuentes L, Jiménez T, et al. Transplant glomerulopathy: clinical course and factors relating to graft survival. Transplant Proc. 2012; 44 (9): 2599–2600.
8. Riethmüller S, Ferrari-Lacraz S, Müller MK, et al. Donor-specific antibody levels and three generations of crossmatches to predict antibody-mediated rejection in kidney transplantation. Transplantation. 2010; 90 (2): 160–167.
9. Gebel HM, Bray RA, Nickerson P. Pre-transplant assessment of donor-reactive, HLA-specific antibodies in renal transplantation: contraindication vs. risk. Am J Transplant. 2003; 3 (12): 1488–1500.
10. Hwang HS, Yoon HE, Choi BS, et al. B-cell complement dependent cytotoxic crossmatch positivity is an independent risk factor for long-term renal allograft survival. J Korean Med Sci. 2011; 26 (4): 528–533.
11. Tait BD, Hudson F, Cantwell L, et al. Review article: Luminex technology for HLA antibody detection in organ transplantation. Nephrology. 2009; 14 (2): 247–254.
12. Eng HS, Bennett G, Tsiopelas E, et al. Anti-HLA donor-specific antibodies detected in positive B-cell crossmatches by Luminex predict late graft loss. Am J Transplant. 2008; 8 (11): 2335–2342.
13. Mujtaba MA, Goggins W, Lobashevsky A, et al. The strength of donor-specific antibody is a more reliable predictor of antibody-mediated rejection than flow cytometry crossmatch analysis in desensitized kidney recipients. Clin Transplant. 2010; 25 (1): E96–E102.
14. Song EY, Lee Y-j, Hyun J, et al. Clinical relevance of pretransplant HLA class II donor-specific antibodies in renal transplantation patients with negative T-cell cytotoxicity crossmatches. Ann Lab Med. 2012; 32 (2): 139–144.
15. Tait BD, Süsal C, Gebel HM, et al. Consensus guidelines on the testing and clinical management issues associated with HLA and non-HLA antibodies in transplantation. Transplantation. 2013; 95 (1): 19–47.
16. Bielmann D, Honger G, Lutz D, Mihatsch MJ, Steiger J, Schaub S. Pretransplant risk assessment in renal allograft recipients using virtual crossmatching. Am J Transplant. 2007; 7 (3): 626–632.
17. Bray RA, Nolen JD, Larsen C, et al. Transplanting the highly sensitized patient: the Emory Algorithm. Am J Transplant. 2006; 6 (10): 2307–2315.
18. Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the Economic Evaluation of Health Care Programmes. Third ed. New York, United States: Oxford University Press, 2005.
19. Fidler S. Pre-transplant Donor Specific Anti-HLA antibody present at time of renal transplantation is independently associated with antibody-mediated rejection, progressive graft dysfunction and patient death. Transpl Immunol. 2013; 28 (4): 148–153.
20. Australian Government Department of Health and Ageing. Cost Report Round 14 version 6.0; 2012 Apr 01.
21. Australian Government Department of Health and Ageing. Medicare Benefits Schedule Book; 2012 Nov 27.
22. Pharmaceutical Benefits Scheme (PBS). Australian Government Depart-ment of Health and Ageing; [cited 2012 01/11/2012]; Available from: http://www.pbs.gov.au/pbs/home.
23. Australian Institute of Health and Welfare. Health expenditure Australia 2010–11;2012 Sep.
24. Wong G, Howard K, Chapman J, et al. How do people with chronic kidney disease value cancer-related quality of life? Nephrology. 2011; 17 (1): 32–41.
25. Laupacis A, Keown P, Pus N, et al. A study of the quality of life and cost-utility of renal transplantation. Kidney Int. 1996; 50 (1): 235–242.
26. George B, Harris AH, Mitchell AJ. Cost effectiveness analysis and the consistency of decision making. Pharmacoeconomics. 2001; 19 (11): 1103–1109.
27. Commonwealth Department of Health and Ageing. Guidelines for the pharmaceutical industry on the preparation of submissions to the Pharmaceutical Benefits Advisory Committee. 2006. Canberra, ACT: Commonwealth Department of Health and Ageing.
28. Wong G, Howard K, Webster AC, Morton RL, Chapman JR, Craig JC. How is health economics relevant to transplant clinicians? Transplantation. 2014; 98 (2): 124–130.
29. TreeAge Software I TreeAge Pro Suite 2013. 2013.
30. Berga JK, Mateu LMP, Catalan SB, et al. Donor-specific HLA antibodies: risk factors and outcomes after kidney transplantation. Transplant Proc. 2011; 43 (6): 2154–2156.
31. Loupy A, Hill GS, Jordan SC. The impact of donor-specific anti-HLA antibodies on late kidney allograft failure. Nat Rev Nephrol. 2012; 8: 348–357.
32. McLaughlin K, Manns B, Nickerson P. The routine use of high-resolution immunological screening of recipients of primary deceased donor kidney allografts is cost-effective. Transplantation. 2006; 81 (9): 1278–1284.
33. Otten HG, Verhaar MC, Borst HPE, Hené RJ, Zuilen ADv. Pretransplant donor-specific HLA class-I and -II antibodies are associated with an increased risk for kidney graft failure. Am J Transplant. 2012; 12 (6): 1618–1623.
34. Kobayashi T, Maruya E, Niwa M, et al. Significant association between chronic antibody-mediated rejection and donor-specific antibodies against HLA-DRB rather than DQB in renal transplantation. Hum Immunol. 2011; 72 (1): 11–17.
35. Thammanichanond D, Ingsathit A, Mongkolsuk T, et al. Pre-transplant donor specific antibody and its clinical significance in kidney transplantation. Asian Pac J Allergy Immunol. 2012; 30 (1): 48–54.
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