Recommendations for the assessment of glomerular filtration rate in patients with cancer: A position statement and systematic review on behalf of the American Society of Onco-Nephrology (ASON).
Bibliographic record
Abstract
e24143 Background: Accurate assessment of glomerular filtration rate (GFR) is crucial to guiding drug eligibility, dose-adjusting systemic therapy, and minimizing the risks of both undertreatment and toxicity in patients with cancer. To date, there has been a lack of guidance to standardize approaches to GFR estimation in the cancer population based on the available evidence. We aim to present data on the first systematic review (SR) of GFR estimating equations in patients with cancer. Methods: We conducted a SR on behalf of the American Society of Onco-Nephrology (ASON) on the accuracy of equations to estimate GFR in adult patients with cancer, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We required that studies use a reference methodology as comparator involving measured GFR via urinary or plasma clearance of an exogenous filtration marker or carboplatin. Two search strategies focusing on studies using serum creatinine (Cr) and cystatin C (Cys) were developed. Risk of bias and quality assessment was performed for all studies. Results: A total of 39 studies (21,949 patients in total, 19,025 with solid cancers) were included. Studies assessing populations in Europe were most frequent (50%), followed by Asia (27%) and the remaining from Australia/New Zealand and North and South America. Risk of bias and quality assessment allocated 15 studies (38%) as low or very low quality, and 24 (62%) as low-moderate or moderate quality. The most commonly assessed GFR estimating equations included Cockroft-Gault (32 studies), Modification of Diet Renal Disease (MDRD) (24 studies), Chronic Kidney Disese Epidemiology Collaboration Study (CKD-EPI)Cr (22 studies), Wright (13 studies), CKD-EPICys (8 studies), CKD-EPICr-Cys (8 studies), and Jeliffe (8 studies). Given the importance of accurate and timely eGFR assessment and the evidence supporting the performance (i.e., accuracy) of the CKD-EPI incorporating both Cr and Cys, we suggest the use of this equation in patients with cancer (grade 2C). Where Cys is unavailable, CKD-EPICr is the next preferred equation given the supporting data for its performance and widespread availability. Since included studies were moderate quality or lower, the ASON rated the certainty of evidence as low. Also, additional data on clinical outcomes related to the selection of eGFR equations is needed. We suggest measurement of GFR via an exogenous filtration marker in patients with cancer for whom eGFR results in borderline eligibility for therapies or clinical trials (ungraded). Conclusions: Based on the results of this SR, we suggest use of contemporary methods for estimation of GFR (CKD-EPICr-Cys and CKD-EPICr-Cr) and avoidance of older and less accurate formulae such as Cockcroft-Gault.
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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 | 0.094 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".