Abstract 9847: Glomerular Filtration Rate in Pediatric Heart Transplant Recipients Can be Measured During Routine Catheterization and is Best Estimated by the Full CKiD Formula
Bibliographic record
Abstract
Identification of chronic kidney disease (CKD) after pediatric heart transplantation (PHT) is limited by inaccuracies in creatinine-based estimates of glomerular filtration rate (GFR); iohexol plasma clearance is a proven method of measuring GFR. We hypothesized that GFR can be measured by a modified iohexol clearance protocol during routine coronary angiography in PHT recipients, and that the recently developed CKiD GFR estimating formula, utilizing serum creatinine and cystatin C, provides a better estimate of GFR than creatinine or cystatin equations alone. We performed a cross-sectional study of PHT recipients, ages 2-18 yrs, undergoing surveillance coronary angiography. GFR was measured by obtaining iohexol levels at 2, 4, and 5 hours post-iohexol infusion for angiography, then calculating area under the curve of iohexol disappearance from plasma. Agreement between measured GFR and multiple GFR estimating equations was assessed with Bland Altman plots, correlation, and % of estimates within +/- 10 and 30% of measured GFR. Of 40 enrolled subjects, 31 had complete GFR data. Median age was 15.0 yrs [IQR 7.6, 16.6], time since HT was 5.9 yrs [2.0, 10.8], height %ile was 26 (7, 48), serum creatinine was 0.7 (0.5, 0.8) mg/dL, cystatin C was 0.83 (0.72, 0.91) mg/L, and measured GFR was 89.5 (78.7-107.0) ml/min/1.73m 2 (mean 93.8, SD22.5). A comparison of measured GFR to estimated GFR is shown in Table 1; the full CKiD formula showed best agreement with measured GFR. Conclusions: 1. We describe a novel modified iohexol plasma clearance method to measure GFR in PHT recipients undergoing coronary angiography. 2. Measured median GFR in this cohort is 89.5 ml/min/1.73m2, suggesting early CKD. 3. The full CKiD formula performs best with respect to bias, accuracy, and correlation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".