The Impact of Direct-Acting Antiviral Agents on Liver and Kidney Transplant Costs and Outcomes

D. A. Axelrod; M. A. Schnitzler; T. Alhamad; F. Gordon; R. D. Bloom; G. P. Hess; H. Xiao; M. Nazzal; D. L. Segev; V. R. Dharnidharka; A. S. Naik; N. N. Lam; R. Ouseph; B. L. Kasiske; C. M. Durand; K. L. Lentine

Disclosures

American Journal of Transplantation. 2018;18(10):2473-2482. 

In This Article

Methods

Data Sources

We conducted a retrospective cohort study using linked healthcare databases in the United States to ascertain patient characteristics, pharmacy fill records, and outcome events for LT and KT recipients. This study used transplant data from the Scientific Registry of Transplant Recipients (SRTR). The SRTR system includes data on all donors, waitlisted candidates, and transplant recipients in the United States, submitted by the members of the Organ Procurement and Transplantation Network (OPTN). The Health Resources and Services Administration (HRSA), US Department of Health and Human Services, provides oversight of the activities of the OPTN and SRTR contractors. Baseline demographic information ascertained for LT and KT recipients from OPTN included age, sex, and race as reported by the transplant centers.

Pharmacy fill data were assembled by linking SRTR records for LT and KT recipients with billing claims from a large US pharmaceutical claims data (PCD) warehouse that collects prescription drug fill records including self-paid fills and those reimbursed by private and public payers. PCD comprises National Council for Prescription Drug Program format prescription claims aggregated from multiple sources including claims warehouses, retail pharmacies, and prescription benefit managers for approximately 60% of US retail pharmacy transactions. Individual claim records include the pharmacy fill date with the national drug code identifying agent and dosage. After Institutional Review Board and HRSA approvals, PCD records were linked with SRTR records for transplant recipients. We applied a deterministic de-identification strategy wherein patient identifiers (last name, first name, date of birth, sex, and ZIP code of residence) were transformed before delivery to the Saint Louis University researchers with Health Information Portability and Accountability Act and Health Information Technology for Economic and Clinical Health (HITECH)-certified encryption technology from PCD. The patient de-identification software uses multiple encryption algorithms in succession to guarantee that the resulting "token" containing encrypted patient identifiers can never be decrypted. However, the algorithm yields the same results for a given set of data elements, such that linkages by unique anonymous tokens are possible.[27]

Sample and Clinical Characteristics

We identified adult LT and KT recipients (age ≥18 years) with SRTR records of transplants between 2007 and 2016 and available pharmaceutical fill records for up to 36 months posttransplant. Recipient clinical and demographic characteristics, characteristics of the donated organ, and other transplant factors including ischemic time and sharing, were defined by the OPTN Transplant Candidate and Recipient Registration forms (Table 1). Patients were identified has being HCV+ based on HCV serostatus at the time of transplant as noted on the Transplant Recipient Registration (TRR) form. As patients who were HCV antibody positive but nucleic acid testing (NAT) negative may be classified as positive on the TRR, we further identified patients who were given an HCV antibody or NAT-positive donor organ as evidence of an active viremic state (as use of HCV+ organs in NAT-negative recipients remains uncommon). Patient insurance coverage was dichotomized as public (Medicaid, Medicare, self-pay) vs private (all others). Patient and graft outcomes were determined from SRTR registry data.

HCV Treatments

Using pharmacy fill records, we identified claims for approved HCV medications and combinations. In the pre-DAA era, defined as before January 2014, HCV treatment was defined as pegylated interferon, interferon, and ribavirin. DAA-era HCV treatments, with or without ribavirin, are shown in Table S1.

Analyses

Demographic characteristics. Donor and recipient characteristics were drawn from the SRTR data. Pre- and post-DAA-era differences were assessed using Student t test and χ 2 analyses as appropriate.

Propensity to receive HCV treatment. Kaplan-Meier survival analysis was performed to identify the proportion of patients receiving HCV treatment by era and primary payer. Multivariate regression analyses were separately performed for LT and KT recipients to assess factors correlated with HCV treatment before and after introduction of DAAs. Donor and recipient characteristics, including primary payer, were included as independent variables.

Cost of treatment analysis. The direct cost of HCV treatment was calculated using pharmacy claims for LT and KT recipients before and after introduction of DAAs.

Survival analysis. Posttransplant survival was assessed in HCV+ patients who did and did not receive HCV treatment in the pre- and post-DAA eras. Multivariate Cox proportional hazard models were constructed with HCV treatment as a time-varying covariate. Models were separately constructed for patients noted to be HCV+ on the TRR and for HCV+ patients who received HCV+ donor organs. Donor and recipient characteristics were included as covariates in the model.

Statistical significance. For all models, P < .05 was used to determine statistical significance. Analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC).

Approval. This project was reviewed and approved by the Institutional Review Board of Saint Louis University.

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