This document presents a model to evaluate the potential budget impact of using the kSORT assay to monitor kidney transplant patients for subclinical rejection. The model projects costs over two years for a commercial health plan covering 285 kidney transplant patients under different monitoring scenarios, including using kSORT alone or with protocol biopsies. The results suggest using kSORT would have a minimal positive budget impact of $0.0057 per member per month, attributed to small patient numbers and low acute rejection and graft failure rates. Sensitivity analysis found costs of kSORT to be the most influential factor on budget impact.

1. A Model to Explore the Potential Budget Impact of a Novel
Screening Tool for the Detection of Subclinical Rejection
among Kidney Transplant Patients
Disclosure: This project was funded by Immucor, Inc.
IMMUCOR
20925 Crossroads Circle
Waukesha, WI 53186
T262-290-8534 www.immucor.com
1.
Avalere Health, LLC, Washington, DC2.
Immucor, Inc. Waukesha, WI
Timothy J. Inocencio, PharmD, PhD2
Kevin Jaglinski, BA1
Hiroshi Uchida, PhD1
Kathleen E. Hughes, MBA2
Background & Objectives /
1
Acute rejection is
associated with long-term effects. Previous studies have suggested that the occurrence of acute rejection
and increasing frequency of acute rejection results in decreased long-term renal allograft survival among
kidney transplant patients.2-4
Routine monitoring of kidney function includes monitoring for an increase in
detected after substantial damage has occurred.5
Detection of subclinical rejection (SCR) can give providers the opportunity to intervene earlier before
the presence of
histological changes specific for acute rejection on screening or protocol biopsy, in the absence of clinical
symptoms or signs.6
However, blood-based gene markers for kidney rejection may occur before histologic
abnormalities are found through biopsy and offer an opportunity for earlier intervention and adjustment to
immunosuppressive regimens.
The Kidney Solid Organ Response Test (kSORT) assay is a 17-gene set non-invasive molecular assay that
measures blood-based gene markers for transplanted kidney rejection. In the recently published Acute
Rejection in Renal Transplantation (AART) study, acute rejection was detected by kSORT up to 3 months
before detection by biopsy7
, giving providers an earlier opportunity to modify immunosuppression to
prevent subsequent rejection. Previous data have shown that more frequent monitoring for SCR results in
8
The objective of this exploratory analysis is to evaluate the potential budget impact of the kSORT assay
from a commercial payer perspective.
Methods /
kSORT assay as a monitoring tool for patients who have undergone a renal transplant. The model
projects the impact of adoption of this product over the course of two years. It considers costs
associated with the kSORT assay in relation to the cost impact of improved detection, development of
alternative strategies, and subsequent management of subclinical rejection (SCR). The model was built
as an interactive spreadsheet using Microsoft Excel 2010.
Clinical and economic inputs and assumptions were based on information from the peer-reviewed and
publicly available literature (see references) across a number of clinical scenarios. When published
sources were not available, data and assumptions were based on the consensus of a multidisciplinary
panel of experts. See Tables 1 and 2 for inputs around costs and probabilities.
These scenarios include the following:
A. No monitoring
B. Monitoring through protocol biopsy (PB) only
C. Monitoring through kSORT assay only
D. Monitoring through combination of PB and kSORT
The monitoring strategies and their assumed mixes before and after kSORT coverage are outlined below:
Assumptions
1. This model assumes equal probabilities of disease progression for the pediatric and adult
populations.
2. As a starting point, the base case scenario assumes equal diagnostic performance of PB and
the kSORT—a conservative assumption.
3. Diagnosis of SCR results in a change in management by physicians to optimize drug regimens,
thereby modifying the effect of SCR on acute clinical rejection (ACR) and graft failure (GF).
4. It is assumed that a diagnosis of SCR results in a 90 percent reduction in ACR.
5. Diagnosis of SCR results in a 50% reduction in GF, independent of ACR.
6. The model includes the costs of the tests, per their indications for use, only before ACR or
GF has occurred.
7. The prevalence of acute SCR decreases through time9
, and assumes that prevalence of SCR is
between approximately 15 to 30 percent.10
8. Active SCR results in an increase in the risk of ACR (probabilities are calibrated in model).10
9. The development of SCR and ACR (given SCR) are both time-dependent, and are associated
with decreased probabilities as time passes.
10.The kSORT assay was assumed to be performed for incident kidney transplant patients during
months 1, 3, 6, 9, 12, 18 and 24 post-procedure, while PB was assumed to take place during
months 3 and 9.
Cohort Size
The size of the commercial plan is assumed to be 5 million, with a mix of 78% of adult and 22%
pediatric (obtained through the Current Population Survey in 201311
). The incidence of kidney transplant
was estimated to be 70.3 per million for adults and 10.3 per million in the pediatric population (taken
from the SRTR/OPTN 2012 report1
, and adjusted using U.S. Census Data). For a plan of 5 million
ansplants incorporating the aforementioned
incidence data was estimated to be 285 patients.
Model Structure
A 2-year exploratory semi-Markov cohort model with monthly cycles was constructed to model the
relationship between SCR, ACR, and GF. Four separate Markov models were created, each representing
one of the four scenarios described above, while incorporating the months during which a patient
receives the respective monitoring tool (i.e., kSORT, PB, or both).
Patients enter the model in the “No SCR” health state, and can either develop SCR or GF. Patients
who are in the SCR health state can enter a “post-SCR” state where they no longer have active SCR,
or they can experience ACR and enter a post-acute rejection state (PARS), after which patients may
continue in that state or progress to GF (See Figure 1a). Patients may enter the death state at any time
from any health state. For patients who have kSORT and/or PB monitoring, patients may transition from
the “No SCR health state and instead may transition to a “Detected SCR” health state (See Figure 1b),
progression to these health states.
Transition Probabilities
Detailed data on transition probabilities, especially those related to SCR, were not available for this
patient population. As such, data were calibrated to approximate actual rates of ACR and GF, obtained
from the SRTR/OPTN registry data1
, while ensuring that modelled rates of SCR were within the range
of prevalence data obtained from the literature. Transition probabilities are presented in Table 1, while
comparisons between the modelled and actual acute rejection and graft failure rates are shown in
Figures 2, respectively.
Figure 1a / Markov Model Diagram—
Base Model
Figure 1b / kSORT and PB Monitoring
Cohorts—Monitoring Submodel
Results /
In the base case scenario, kSORT is expected to produce a minimal budget impact of $0.0057 PMPM
in Table 3. Total costs for the plan during Year 1 and Year 2 are provided in Figure 3.
Table 2 / Cost Inputs*
Background Costs Source
Year 1 $2,171.39
SRTR/OPTN 2012 Report1
Year 2 $1,213.19
Marginal AR costs
Year 1 $2,148.00
Gheorghian et al.13
Year 2 $1,094.99
Marginal GF Costs
$6,644.36 Gheorghian et al.13
Drug Costs
Monthly Drug Costs for First 3 Months $2,373.80 SRTR/OPTN 2012 Report1
and Red Book
AWP prices14
Monthly Drug Costs After 3 Months $2,050.47
Percent Increase in Drug Costs for Diagnosed SCR 2% Assumption
Percent Decrease in Drug Costs for No SCR 2% Assumption
Protocol Biopsy Costs $3,878.00 Nankivell and Chapman10
kSORT Assay $1,500.00 Modeled Price**
Table 3 / Budget Impact Estimates for kSORT by Year
BUDGET IMPACT
Total Year 1 Year 2
Total Cost Difference $ 686,696.07 $ 340,282.41 $ 346,413.66
Per Member $ 0.1373 $ 0.0681 $ 0.0693
PMPY $ 0.0687 $ 0.0681 $ 0.0693
PMPM $ 0.0057 $ 0.0057 $ 0.0058
*The PARS health state represents state after ACR event has occurred
** Patients may transition to the ‘death’ state from any health state.
Graft
Failure
Detected
SCR2
No
SCR
PARS*
Death**
SCR
No
SCR1
No
SCR2
Death**
PARS*
Graft
Failure
Monitoring
Submodel
Figure 3 / Costs With and Without kSORT
Test Costs
ACR-Related Costs
GF-Related Costs
Rx Drug Costs
Background Costs
$35
$30
$25
$20
$15
$10
$5
$0
TotalCost(Millions)
Baseline
(No kSORT)
With
kSORT
Year 1
$17.0 $17.3
$30.5 $30.9
Year 2
Baseline
(No kSORT)
With
kSORT
Figure 2 / Comparisons of ACR and GF Rates Between SRTR/OPTN Rates
and Modeled Rates
16
14
12
10
8
6
4
2
0
Percent
Month
0 5 10 15 20 25
SRTS/OPTN 2012
GF Rates
Modeled GF Rates:
No Diagnosis
SRTS/OPTN 2012
ACR Rates
Modeled ACR Rates:
No Diagnosis
MONITORING APPROACH BEFORE KSORT COVERAGE AFTER KSORT COVERAGE
Routine monitoring 60% 50%
PB only 40% 30%
kSORT only -- 10%
PB + kSORT -- 10%
Table 1 / Model Transition Probabilities
Parameter Value Parameter Value
Probability of Detecting SCR, Given SCR Probability of GFc
Protocol Biopsya
0.9 Months 0 to 3 0.01450
kSORTa
0.9 Months 3 to 12 0.00120
Reduction in ACR, Given Dx of SCRa
90% Months 12 to 24 0.00001
Probability of SCR Resolutiona
0.5 Increase in GF, Given SCRa
0%
Probability of SCRb
Months 3 to 12 0.00624
Month 0 (Immediate) 0.2 Months 12 to 24 0.00005
Months 1 to 3 0.25 Reduction in GF, Given Dx of SCRa
0.5
Months 3 to 12 0.2 Monthly Probability of Deathc
0.002223
Months 12 to 24 0.15
Probability of ACR, Given SCRc
Months 0 to 1 0.115
Months 1 to 3 0.017
Months 3 to 12 0.034
Months 12 to 24 0.01
Monthly Probability of Deathc
50%
SCR: subclinical rejection; ACR: acute clinical rejection; Dx: diagnosis; GF: graft failure
a. Assumption
b. Calculated from Nankivell and Chapman10
c. Calibrated based on rates obtained from SRTS/OPTN 2012 Report1
d. Calculated from Meier-Kriesche et al.12
Sensitivity Analysis
Because of the small number of individuals in the plan with kidney transplant, the overall budget impact
e model was most sensitive to kSORT
costs (Figure 4).
Figure 4 / Tornado Diagram for One-Way Sensitivity Analysis Around Costs**
and Transition Probabilities
*Background costs are fixed, and do not change according to the method used for surveillance
**Costs were varied between +/- 25% of their base case value
0.002
kSORT Costs
Probability of ACR, given SCR (+/- 25%)
Probability of SCR (+/- 50%)
Probability of GF, Given ACR (+/- 25%)
Probability of SCR Resolution (0.3 to 0.7)
Reduction in ACR, Given Diagnosis of SCR (0.4 to 0.95)
Marginal GF Costs
Reduction to ACR, Given SCR (0.2 TO 0.8)
Probability of Diagnosis of SCR: kSORT (0.8 to 1)
Marginal ACR Costs – Year 1
Probability of Death (+/- 25%)
Drug costs – Post 3 Months
Marginal ACR Costs – Year 2
Percent Increase in GF, Given SCR (0% to 10%)
Probability of Diagnosis of SCR: Protocol Biopsy (0.8 to 1)
Protocol Biopsy Costs
Drug costs – First 3 Months
Background Costs*
-0.001 -0.0005 0 0.0005 0.001 0.0015
Conclusion /
This exploratory model indicates that the kSORT monitoring assay is expected to produce minimal
in AR or GF. The low budget impact is attributed to the small patient population within plans with renal
transplants every year, in addition to relatively low acute rejection and graft failure rates. Although the
budget impact is small, additional clinical data showing how kSORT improves patient outcomes are
needed. Long-term data may be of particular import in showing true value of the technology.
References /
1 Matas AJ, Smith JM, Skeans MA, et al. OPTN/SRTR 2012 Annual Data Report: kidney. Am J Transplant. 2014;14 Suppl 1:11-44.
2 Wu J, Chen J, Wang Y, et al. Impact of acute rejection episodes on long-term renal allograft survival. Chin Med J (Engl).
2003;116(11):1741-1745.
3 Emiroğlu R, Yagmurdur MC, Karakayali F, et al. Role of donor age and acute rejection episodes on long-term graft survival in cadaveric
kidney transplantations. Transplant Proc. 2005;37(7):2954-2956.
4 Matas AJ, Gillingham KJ, Payne WD, Najarian JS. The impact of an acute rejection episode on long-term renal allograft survival (t1/2).
Transplantation. 1994;57(6):857-859.
5 onic renal allograft injury in a randomized trial on steroid
avoidance in pediatric kidney transplantation. Am J Transplant. 2012;12(10):2730-2743.
6 Kidney Disease: Improving Global Outcomes (KDIGO) Transplant Work Group. KDIGO clinical practice guideline for the care of kidney
transplant recipients. Am J Transplant. 2009;9 Suppl 3:S1-155.
7 Roedder S, Sigdel T, Salomonis N, et al. The kSORT assay to detect renal transplant patients at high risk for acute rejection: results of the
multicenter AART study. PLoS Med. 2014;11(11):e1001759.
8 J Am Soc Nephrol.
1998;9(11):2129-2134.
9 Nankivell BJ, Borrows RJ, Fung CL, O’Connell PJ, Allen RD, Chapman JR. Natural history, risk factors, and impact of subclinical rejection in
kidney transplantation. Transplantation. 2004;78(2):242-249.
10 Am J Transplant.
2006;6(9):2006-2012.
11 United States Census Bureau. Current Population Survey. Table HI01. Health Insurance Coverage Status and Type of Coverage by Selected
Characteristics: 2013. http://www.census.gov/hhes/www/cpstables/032014/health/hi01_1.xls. Accessed April 28, 2015.
12 Meier-Kriesche HU, Ojo AO, Hanson JA, et al. Increased impact of acute rejection on chronic allograft failure in recent era.Transplantation.
2000;70(7):1098-1100.
13 Gheorghian A, Schnitzler MA, Axelrod DA, Kalsekar A, L’italien G, Lentine KL. The implications of acute rejection and reduced allograft
function on health care expenditures in contemporary US kidney transplantation. Transplantation. 2012;94(3):241-249
14 Micromedex THA. Red Book. 2014; http://www.redbook.com/redbook/index.html. Accessed December 1, 2014.
Base
Model
*Costs are reported in 2014 U.S. dollars, using the medical component of the Consumer Price Index (http://www.bls.gov/cpi/) to account for inflation
**Price for the kSORT assay has not been finalized at the time of this publication; estimate represents the modeled price for purposes of this analysis