MétaCan
Menu
Back to cohort
Record W2983118487 · doi:10.1681/asn.2019070646

Economic Evaluation of Extending Medicare Immunosuppressive Drug Coverage for Kidney Transplant Recipients in the Current Era

2019· article· en· W2983118487 on OpenAlexafffund
Matthew Kadatz, John S. Gill, Jagbir Gill, Richard N. Formica, Scott Klarenbach

Bibliographic record

VenueJournal of the American Society of Nephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of AlbertaCentre for Advancing Health OutcomesUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniversity of British ColumbiaMichael Smith Health Research BCUniversity of AlbertaNational Center for Advancing Translational SciencesAmerican Society of Transplantation
KeywordsMedicineImmunosuppressionTransplantationIntensive care medicineCohortKidney transplantationInternal medicine

Abstract

fetched live from OpenAlex

Significance Statement Kidney transplant recipients must take immunosuppressive medications to prevent rejection of their transplant kidney. Coverage of immunosuppressive drugs under Medicare’s ESKD program ends 36 months after transplantation, putting patients at risk for premature transplant failure. The authors analyzed the cost and benefits of extending Medicare immunosuppressive drug coverage for the entire duration of survival after transplantation using current generic immunosuppressive drug costs and estimates of increased transplant survival. From the Medicare payer perspective, extending immunosuppression drug coverage was cost-saving and led to better patient outcomes compared with the current policy. The findings may be useful in advancing legislative efforts to ensure kidney transplant recipients have access to essential life-saving immunosuppressive medications. Background Kidney transplant recipients must take immunosuppressant drugs to prevent rejection and maintain transplant function. Medicare coverage of immunosuppressant drugs for kidney transplant recipients ceases 36 months after transplantation, potentially increasing the risk of transplant failure. A contemporary economic analysis of extending Medicare coverage for the duration of transplant survival using current costs of immunosuppressant medications in the era of generic equivalents may inform immunosuppressant drug policy. Methods A Markov model was used to determine the incremental cost and effectiveness of extending Medicare coverage for immunosuppressive drugs over the duration of transplant survival, compared with the current policy of 36-month coverage, from the perspective of the Medicare payer. The expected improvement in transplant survival by extending immunosuppressive drug coverage was estimated from a cohort of privately insured transplant recipients who receive lifelong immunosuppressant drug coverage compared with a cohort of Medicare-insured transplant recipients, using multivariable survival analysis. Results Extension of immunosuppression Medicare coverage for kidney transplant recipients led to lower costs of −$3077 and 0.37 additional quality-adjusted life years (QALYs) per patient. When the improvement in transplant survival associated with extending immunosuppressant coverage was reduced to 50% of that observed in privately insured patients, the strategy of extending drug coverage had an incremental cost–utility ratio of $51,694 per QALY gained. In a threshold analysis, the extension of immunosuppression coverage was cost-effective at a willingness-to-pay threshold of $100,000, $50,000, and $0 per QALY if it results in a decrease in risk of transplant failure of 5.5%, 7.8%, and 13.3%, respectively. Conclusions Extending immunosuppressive drug coverage under Medicare from the current 36 months to the duration of transplant survival will result in better patient outcomes and cost-savings, and remains cost-effective if only a fraction of anticipated benefit is realized.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.336
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicRenal Transplantation Outcomes and TreatmentsFrench-language works237,207