Comparison of eGFR Equations to Guide Dosing of Medications for Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Clinicians caring for kidney transplant recipients (KTRs) most commonly use estimated glomerular filtration rate (eGFR) to guide medication dosing as it is the most readily available measure of kidney function. Which eGFR equations provide the most accurate medication dosing guidance for KTRs remains uncertain. METHODS: We studied 415 stable KTRs in Canada and New Zealand. Participants completed same-day measurements of creatinine and cystatin C and measured GFR (diethylenetriaminepentaacetic acid). Chronic Kidney Disease Epidemiology Collaboration, European Kidney Function Consortium, and transplant-specific eGFR equations were compared with both Cockcroft-Gault creatinine clearance (CrCl) and measured GFR. eGFR equations were assessed both indexed to a standardized body surface area (BSA) of 1.73 m 2 (milliliter per minute per 1.73 m 2 , as is conventional reporting from most clinical laboratories) and nonindexed (milliliter per minute) accounting for actual BSA. The primary outcome was the proportion of medication dosing discordance relative to Cockcroft-Gault CrCl or measured GFR for 8 commonly prescribed medications. Stratified analyses were performed on the basis of obesity status. RESULTS: Nonindexed eGFR equations (milliliter per minute) resulted in substantially lower medication dosing discordance compared with indexed eGFR equations (milliliter per minute per 1.73 m 2 ). These findings were most pronounced among KTRs with obesity, in whom underdosing was frequent. When compared with Cockcroft-Gault CrCl, the lowest proportion of discordance was found with the nonindexed 2023 transplant-specific equation. When compared with measured GFR, the lowest proportion of discordance was found with the nonindexed 2021 Chronic Kidney Disease Epidemiology Collaboration Cr/CysC equation. CONCLUSIONS: Nonindexed eGFR values accounting for actual BSA should be used by clinicians for medication dosing in KTRs. These findings may inform KT providers about which eGFR equations provide the safest, most accurate medication dosing guidance for KTRs.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".