Transition of Renal Patients Using AlloSure Into Community Kidney Care (TRACK): Protocol for Long-Term Allograft Surveillance in Renal Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage kidney disease require complex and expensive medical management. Kidney transplantation remains the treatment of choice for end-stage kidney disease and is considered superior to all other modalities of renal replacement therapy or dialysis. However, access to kidney transplant is limited by critical supply and demand, making it extremely important to ensure longevity of transplanted kidneys. This is prevented through lifelong immunosuppression, with caution not to overly suppress the immune system, resulting in toxicity and harm. Transition of care to community nephrologists after initial kidney transplantation and monitoring at a transplant center is an important process to ensure delivery of effective and patient-centric care closer to home. Once transplanted, laborious surveillance of the immune system and monitoring for potential rejection and injury are undertaken through an armamentarium of screening modalities. Posttransplant surveillance for kidney function and injury remains key to follow-up care. While kidney function, quantified by estimated glomerular filtration rate and serum creatinine, and kidney injury, measured by proteinuria and hematuria, are standard biomarkers used to monitor injury and rejection posttransplant, they have recently been demonstrated to be inferior in performance to that of AlloSure (CareDx Inc, Brisbane, CA) circulating donor-derived, cell-free DNA (dd-cfDNA). OBJECTIVE: The outcomes and methods of monitoring renal transplant recipients posttransplant have remained stagnant over the past 15 years. The aim of this study is to consider intensive surveillance using AlloSure dd-cfDNA in an actively managed protocol, assessing whether it increases long-term allograft survival in kidney transplant recipients compared with current standard clinical care in community nephrology. METHODS: The study protocol will acquire data from a phase IV observational trial to assess a cohort of renal transplant patients managed using AlloSure dd-cfDNA and patient care managers versus 1000 propensity-matched historic controls using United Network for Organ Sharing U.S. Scientific Registry of Transplant Recipients data. Data will be managed in a centralized electronic data server. The primary outcome will be superior allograft survival, as a composite of return to dialysis, retransplant, death due to allograft failure, and death with a functional graft (infection, malignancy, and cardiovascular death). The secondary endpoints will assess improved kidney function through decline in estimated glomerular filtration rate and immune activity through development of donor-specific antibodies. RESULTS: The total sample is anticipated to be 3500 (2500 patients managed with AlloSure dd-cfDNA and 1000 propensity-matched controls). Active enrollment began in November 2020. CONCLUSIONS: Based on a significant literature base, we believe implementing the surveillance of dd-cfDNA in the kidney transplant population will have a positive impact on graft survival. Through early identification of rejection and facilitating timely intervention, prolongation of allograft survival versus those not managed by dd-cfDNA surveillance protocol should be superior. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25941.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".