Use of GFR equations to adjust drug doses in an elderly multi-ethnic group a cautionary tale
Bibliographic record
Abstract
BACKGROUND: Glomerular filtration rate (GFR) is the best index of kidney function. Mathematical estimations of GFR, based on serum creatinine (SCr), are a clinically useful method to follow renal function, but have certain limitations which need to be considered. Convention supports the use of Cockcroft-Gault (CG) for the purposes of drug dosing. The impact of using the modification of diet in renal disease (MDRD) formula has not been formally evaluated with respect to drug dosing; especially in an elderly multi-ethnic population. A cross-sectional study of long-term care facility patients was conducted to demonstrate the impact of the use of different formulae in the elderly for the purposes of medication dosing. METHODS: Patients with ESRD were excluded. GFR was calculated for all subjects using the four-variable modified MDRD equation (re-expressed using isotope-dilution mass spectrometry-based creatinine values) and the CG equation (corrected for body surface area). Discordance was defined as a reclassification of one stage of chronic kidney disease (CKD) by using a different formula. Calculated GFR from each formula was used to calculate the doses of two drugs: amantadine and digoxin, to demonstrate the potential impact of the use of different formulae on the risk of drug toxicity. RESULTS: A total of 180 patients were identified with a mean age of 85 years, of which 30% were Asian. Mean MDRD-GFR and CG-GFR in the same group were different (72.9 ml/min/1.73 m(2) vs 52.1 ml/min/1.73 m(2)). Only 37.2% of the patients were categorized in the same stage of CKD by both methods. When MDRD was used in place of CG to determine drug dose adjustments, we found that 20% fewer patients would have qualified for a dose reduction of amantadine, which would have translated to a higher total cumulative dose delivered. CONCLUSIONS: The use of CG and MDRD provided discordant estimations in over 60% of the elderly patients. While the importance of these equations cannot be questioned, caution should be exercised in situations where they have not been prospectively validated. Therefore, their interchangeable use cannot be advocated in the dosing of medications until further prospective validations are performed.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".