Despite improvements in transplantation practice, AR continues to impact graft and patient survival, increasing the cost of transplantation among recipients of SCD, LD and ECD transplants. In this study of a contemporary cohort of US kidney transplant recipients, we found that both Ab-treated AR and non-Ab–treated AR are associated with increases in the cost of care. However, the cost impact of AR is markedly higher among affected patients with compromised allograft function. We also found that, although AR events are costly complications for affected individuals, given the low contemporary incidence of AR, the costs attributable to AR represent a relatively small proportion of posttransplantation costs when considered at the population level.
The cost of AR treatment itself, including admission, diagnostic testing, and Ab therapy, and the potential need for additional medical interventions are likely mediators of the increased costs of care associated with AR events. Although average costs are high for an episode of AR, the contribution of AR to total annual costs of care are relatively low. This reflects the low incidence of AR, particularly late after transplantation in an era of induction therapy. Furthermore, joint assessment of the cost impacts of first-year AR and eGFR level on subsequent costs revealed that substantial reductions in allograft function (defined by eGFR) result in significantly greater increases in the cost of medical care for transplant recipients.
Despite the high initial cost of renal transplantation, the subsequent cost savings associated with avoidance of dialysis and related complications contribute to a highly beneficial cost-effectiveness ratio (1). Although differing in magnitude, the cost-effectiveness of kidney transplantation is generalizable across all donor types including ECD transplants. The economic impact of transplantation reflects the integrated effects of donor and recipient characteristics, medical management, and reimbursement practice. Prior analyses have demonstrated a profound increase in the cost of transplantation using organs with high-risk characteristics (older donors, donors after cardiac death, and kidneys with prolonged ischemia times) (4–6). These characteristics increase the risk of delayed graft function, resulting in longer hospital stays, increased need for posttransplantation dialysis, and subsequent readmissions. Although many of these factors are immutable, it is possible that mechanical devices including pulsatile perfusion of donor allografts may help to decrease the cost of care (7).
As demonstrated in this analysis, the development of AR is also associated with dramatic increases in the cost of posttransplantation care for an individual patient. However, currently available immunosuppressive agents are also expensive, requiring careful analysis to determine both the clinical and economic implications of their use. For example, recent data suggest that use of thymoglobulin can decrease the incidence of AR and rehospitalization, offsetting increased expenditures for pharmaceuticals (8). Alternative agents, including alemtuzumab, seem to offer both clinical and economic benefits through the reduction of early AR and lower pharmacy expenditures (9, 10). In the later period of transplantation, it is clear that strategies that increase medication adherence, thus reducing the development of AR, are crucial to controlling posttransplantation costs (11).
With respect to the assumptions of our models and interpretation, because the model parameters reflect AR in prior and current periods, the approach assumes that the cost impact of an AR event is realized by the end of the next period. If cost impacts of AR extend beyond the next period, the total estimated cost impact may not be completely captured, resulting in conservative estimates of the economic implications of AR. This approach has been used to estimate the cost impact of other complications in transplantation such as posttransplantation diabetes mellitus and cytomegalovirus infection (12, 13). Alternatives to our ordinary least squares (OLS) models, such as regressions estimating the determinants of the natural log of Medicare payments, may be more efficient but also may produce biased estimates and are difficult to interpret. Because we have access to cost data for very large samples, we used the unbiased estimator. Our past work has demonstrated nearly identical results with OLS cost regression and regressions on the natural log of Medicare payments (13), and OLS has become our standard in analyses of the economic impact of complications in transplantation (7,14).
Limitations of our study include the lack of histologic definitions of the AR events within the Organ Procurement and Transplantation Network (OPTN) registry. The OPTN does not track which AR episodes represent cellular or humoral rejection. However, because both humoral and cellular rejections are known to contribute to late allograft loss, the effects demonstrated here are likely to be true for population-based analyses. Furthermore, the need for Ab treatment is an excellent marker of the severity of rejection, as was demonstrated by relative impacts on both economic and clinical outcomes (15). Second, our sample was limited to patients insured by Medicare, and findings may not be generalizable to patients with private health insurance. Because Medicare benefits expire at 3 years after transplantation except in the case of those older than 65 years or with disability, our economic analyses focused on AR events within the first 3 years after transplantation. However, the limitations of the sample restriction are mitigated by the size and diversity of the study sample. Finally, as a sample of transplant recipients in 2000 to 2007, the patients are expected to have been managed primarily with calcineurin inhibitor– or mammalian target of rapamycin–based immunosuppression. The implications of other maintenance immunosuppression regimens for the cost and outcome from AR may be different than those that are reported in this analysis.
In conclusion, AR remains an important source of expense after renal transplantation. Strategies to reduce the incidence of AR have the potential to extend graft life and reduce overall costs. Although clinical consequences of AR may increase with later AR events after transplantation (15), the marginal cost impacts seem lower for later events. The net economic impact of AR depends strongly upon the degree of preservation of allograft function.
MATERIALS AND METHODS
Study data were drawn from the records of the United States Renal Data System, which integrate OPTN records with Medicare billing claims (16). This study was conducted in accordance with the Health Insurance Portability and Accountability Act of 1996, and all standards regarding the security and privacy of an individual’s health information were maintained.
The primary study sample comprised recipients of first single-organ kidney transplants in the United States in 2000 to 2007 with Medicare as the primary payer at the time of transplantation. This sample was used to estimate the incidence of AR across the spectrum of donor organ types (SCD, LD, and ECD). We defined ECD transplants according to the United Network for Organ Sharing (UNOS)/OPTN criteria as allografts from deceased donors 60 years or older, or those from donors aged 50 to 59 years with at least two of the following: history of hypertension, terminal serum creatinine level greater than 1.5 mg/dL, or cerebrovascular cause of death (17). These data were used to determine the proportion of costs attributable to AR across the entire Medicare-insured sample. A subsample of patients who survived with graft function to the first transplant anniversary and had available information for the computation of eGFR at 12 months was also used to examine the combined impact of AR and eGFR on subsequent costs in years 2 and 3 after transplantation. Finally, analytic samples for the economic analyses comprised patients with Medicare at the start of a given period who sustained coverage to the period end or died or experienced graft failure within the period.
Outcome Variable Definitions
The primary economic measure was actual payments for all health care services made by Medicare. Payments were evaluated at 1-year intervals during the first, second, or third years after transplantation. The cost analysis was limited to 3 years because Medicare transplantation benefits expire at 3 years except in the cases of people 65 years or older or with certain disabilities. Patient costs were included in analysis of an interval if: (1) the recorded Medicare eligibility extended continuously from the beginning to the end of the period or (2) Medicare eligibility ended in an interval because of death or graft loss. Monetary figures were adjusted to the prices in the year 2007 medical care component of the Consumer Price Index (18).
Predictor Variable Definitions
The primary predictor of interest was AR as defined by OPTN reporting. The OPTN surveys centers for information on clinical events among individual transplant recipients at 6 months, 1 year, and then annually. Data on AR are identified according to the period covered by a specific reporting form, but dates of AR within the period are not collected. We defined AR based on center reporting on the OPTN survey that an AR event occurred. Immunosuppression records were used to subclassify AR as Ab-treated or non-Ab-treated as a measure of AR severity. Ab-treated AR was defined by administration of polyclonal Abs, such as antithymocyte globulin or antilymphocyte globulin, or monoclonal Abs, such as OKT3, alemtuzumab, or rituximab, for the indicated purpose of treating AR. Patients with any Ab-treated AR event in a period were classified as having Ab-treated AR in that period, as the first level of classification. Patients with other indications of AR in a period who did not meet the criteria for Ab-treated AR were classified as having non-Ab–treated AR in the given period.
Renal function at 1 year after transplantation was defined by eGFR as computed with the abbreviated Modification of Diet in Renal Disease equation as: eGFR (mL/min/1.73 m2)=186×(serum creatinine [mg/dL])−1.154× age−0.203×(1.212, if African American)×(0.742, if female) (19). Serum creatinine values were drawn from the OPTN 1-year recipient follow-up reporting form. The abbreviated Modification of Diet in Renal Disease equation has superior performance for prediction of measured GFR among renal transplant patients when compared with the Nankivell and Cockcroft-Gault formulas (20). Renal function was categorized by levels of function as: > 60, 45−59, 30−44, and <30 mL/min/1.73 m2.
Data management and analysis were performed with SAS for Windows software, version 9.2 (SAS Institute Inc., Cary, NC). Continuous data were summarized as means (SDs), and categorical data were summarized as proportions. Cost period analyses considered the proportion of patients who met eligibility criteria for inclusion in a given cost period (as defined previously) who experienced AR events within the cost period of interest.
The marginal cost impacts of AR events during and before the cost periods of interest (first, second, and third years after transplantation) were computed by OLS regression equations as: E(Y)=β1X1+β2X2+…βkXk, where E(Y) indicates Medicare payments within a period of interest, Xk indicates the value of a given predictor variable, and βk indicates the marginal costs associated with a 1-unit change in a given variable after adjustment for other observed factors in the model. Thus, for binary variables such as AR, the βk parameters quantify the marginal costs associated with AR compared with no AR in a given or prior period, respectively. Estimates were adjusted for the recipient, donor, and transplant factors in the UNOS survival models (see Appendix, SDC,http://links.lww.com/TP/A670). In addition to the UNOS covariates, the primary cost period models were also adjusted for the impact of death and graft failure within the period of interest. The cost (in dollars) contribution of AR to population costs within a given period was computed with a weighted average, as: Σ(proportion of period sample with AR event)×(marginal cost impact of that AR event). The proportion of total period costs attributable to AR was computed as: Σ[(proportion of period sample with AR event)×(marginal cost impact of that AR event)]/(total period costs).
The combined impact of AR within the first year and eGFR at the first transplant anniversary on second- and third-year period costs was examined among subsamples of patients who met criteria for the second- and third-year cost samples, respectively, and also had available information for the computation of eGFR at the first anniversary. These models were explored with and without adjustment for death and graft failure within the period of interest. Predicted second- and third-year costs according to AR status and eGFR level at the first anniversary were computed from these multivariable regression models, with values of adjustment covariates set to sample averages.
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