Comparing Estimated Glomerular Filtration (eGFR) Equations in Patients with Transfusion-Dependent Thalassemia
Bibliographic record
Abstract
Renal dysfunction is a well-described complication in children with transfusion-dependent thalassemia (TDT), and is linked to chronic anemia, hypoxia, hemolysis as well as nephrotoxicity from free iron and chelating agents. Nuclear medicine-based (DTPA) measurement of glomerular filtration rate (nmGFR) remains the gold standard test of renal function but is expensive, invasive and unsuitable for routine screening. Creatinine is the most used biomarker for renal function but is less accurate in children and those with low muscle mass. Cystatin C has been widely adopted as a more accurate marker but can be affected by iron chelation therapy. While early detection of renal dysfunction is important to guide management, there remain limited data on the most reliable method to estimate eGFR in children with TDT. We designed a single-center retrospective cohort study to explore the correlation between nmGFR and eGFR equations based on creatinine, cystatin C, or both. Patients with TDT aged 0-17 years who had a nmDTPA study and serum cystatin C between 2013-2023 were included. Cystatin C measurements taken on the same day or up to 7 days from their DTPA scan were used. Eight eGFR calculations were performed (Schwartz, Schwartz-Lyon, Cr CKID U25, Filler, CysC Zapatelli, CysC CKID U25, Cr-CysC Zapatelli, Cr-CysC CKID U25). The Pearson correlation (rho) was used to measure the correlation of each eGFR equation versus the nmGFR. Mean bias (95% confidence intervals, CI) was calculated using the Bland-Altman method assuming constant variance. Eleven children (55% female) with TDT (73% β0/β0 or β0/β+, 9% E0/β0, 18% non-deletional alpha thalassemia) with median age of 13 years were included. Nine (82%) children were receiving deferasirox for iron chelation, one (9%) was receiving deferoxamine and one (9%) was being phlebotomized having undergone a hematopoietic stem cell transplant. Two (18%) children were concurrently receiving hydroxyurea. The mean nmGFR was 94.1+/-20.9 mL/min/1.73 m2, 72.3% had a normal nmGFR > 90 mL/min/1.73 m2. The Cr-CysC CKID U25 equation showed the strongest correlation to nmGFR (rho=0.84, p=0.002) with the lowest risk of bias (-3.01, 95% CI -28.5-25.1). The Cr CKID U25 and CysC CKID U25 equations showed moderate correlations [(rho=0.61, p=0.045) and (rho=0.64, p=0.047)] but showed significant bias [(-21.8, 95% CI -58.1-14.6) and (19.2, 95% CI -10.3 - 48.7)]. The Schwartz, Schwartz-Lyon, Filler, CysC Zapatelli, Cr-CysC Zapatelli equations did not correlate well with the nmGFR. Most commonly used eGFR calculations poorly estimate renal function in children with transfusion-dependent thalassemia. The Cr-CysC CKID U25 equation showed strong correlation with the nmGFR and may represent a better biomarker for renal dysfunction in this population. A large prospective cohort study is needed to confirm and validate these findings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".