An unusual case of acute kidney injury due to vancomycin lessons learnt from reliance on eGFR
Bibliographic record
Abstract
We present a case of renal impairment in an emaciated HIV-infected male that initially went unrecognized because of reliance on serum creatinine and estimated glomerular filtration rate (eGFR). Inaccurate vancomycin dosing led to toxic drug levels (66 mg/l), associated with acute and severe worsening of kidney function. This occurred in the context of escalating doses of vancomycin given in the presence of changing kidney function, albeit kidney function that always remained well within the normal range (serum creatinine 29 - 42 mumol/l). In the absence of other plausible explanations, a presumptive diagnosis of vancomycin nephrotoxicity was made. Given the rarity of this diagnosis in the current era, we discuss the pathophysiology of vancomycin nephrotoxicity. We also explore the potential reasons for inaccuracy of GFR prediction equations in the HIV population, and discuss the potential pitfalls associated with application of eGFR or even serum creatinine without appropriate understanding of their limitations. We believe our case highlights a number of important teaching points: Vancomycin nephrotoxitiy is rare but can occur in the setting of kidney dysfunction. Current assessment of kidney function using creatinine and eGFR requires awareness of the clinical caveats in which these measures may be misleading. Acute changes in kidney function, irrespective of the test used, should be contextualized to the individual situation. Persons with HIV and low muscle mass constitute a specific subgroup in whom assessment of kidney function may be problematic using creatinine. We support ongoing efforts to develop or refine equations for specific unique and easily identifiable populations.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".