Discordance between creatinine and cystatin C-based estimation of glomerular filtration rate (eGFR) in solid organ transplant recipients
Bibliographic record
Abstract
• Cystatin C-based eGFR is significantly depressed in transplant recipients. • Non-GFR determinants of cystatin C may underlie the negative bias in transplant recipients. • The value of cystatin C-based eGFR in transplant patients remains undetermined. Estimation of glomerular filtration rate is a critical component of assessing kidney function post-solid organ transplantation and in monitoring risk of acute injury. Our objective was to evaluate estimated glomerular filtration rate (eGFR) as derived from creatinine (eGFR cr ), cystatin C (eGFR cys ), and both (eGFR cr-cys ) in a cohort of transplant recipients. A total of 47 unique post-solid organ transplant patients receiving tacrolimus were included. Residual specimens were assayed for creatinine (Jaffe and enzymatic), cystatin C, and tacrolimus. eGFR was estimated using the 2021 CKD-EPI formulae. Results were compared by Deming regression and bias was assessed using non-parametric cumulative distribution plots. Percent agreement in chronic kidney disease (CKD) by stage was evaluated across equations. eGFR c ys relative to eGFR c r estimates demonstrated a median bias of –22 mL/min/1.73 m 2 and an overall 21.3 % [95 % CI: 12.1, 35.0] agreement in CKD staging. eGFR cr-cys demonstrated a median bias of −14 mL/min/1.73 m 2 and overall agreement of 34.0 % [95 % CI: 22.3, 48.4] relative to eGFR cr (enzymatic). Discordance increased proportionally with eGFR and did not differ by creatinine assay (Jaffe or enzymatic). Cystatin C incorporation leads to markedly negative biases between eGFR estimates in patients with solid organ transplant, implying lack of applicability of eGFR cys or GFR cr-cys in the transplant setting. This highlights the dependency of patient characteristics on equation performance and the need to consider confounding factors in interpretation and utilization of cystatin C based equations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".