CT Volumetry Is Superior to Nuclear Renography for Prediction of Residual Kidney Function in Living Kidney Donors.
Bibliographic record
Abstract
Background: Donor evaluation protocols commonly include nuclear renography to assess total and split renal function. With the aim of improving the efficiency of donor workup and minimizing exposure to injected radioisotopes, we sought to determine if kidney volumes from CT could be used to accurately estimate split renal function in healthy living kidney donors. Methods: We retrospectively identified 88 consecutive kidney donors at our centre whose pre-donation evaluation included both CT scan and nuclear renography, and who had a serum creatinine performed at 6-months post-donation. We utilized Myrian computer software to determine right and left kidney volumes from the pre-donation CT scan and compared these as a proportion of total kidney volume to the split renal function measured by nuclear renography. We used linear regression to examine the association between estimated glomerular filtration rate or eGFR (based on the CKD-EPI and Mayo formulas) at 6-months post-donation and the residual eGFR predicted by CT volumetry or nuclear renography for the kidney remaining in situ. Results: Proportional kidney volumes determined by CT volumetry were not well correlated with split renal function measured by nuclear renography for both left and right kidneys (r = 0.53 and 0.56, respectively). Each 10 mL/min increase in predicted residual donor eGFR by nuclear renography was associated with an increase in post-donation CKD-EPI eGFR of 13.12 mL/min (95% CI: 10.10-16.15, p<0.001) and Mayo eGFR of 13.37 mL/min (95% CI: 8.77-17.98, p<0.001). Each 10 mL/min increase in predicted residual donor eGFR by CT volumetry was associated with an increase in post-donation CKD-EPI eGFR of 14.45 mL/min (95% CI: 11.29-17.62, p<0.001) and Mayo eGFR of 17.18 mL/min (95% CI: 12.13-22.24, p<0.001). The predicted post-donation eGFR values generated by CT volumetry were a better fit to the actual post-donation eGFR values than nuclear renography for both CKD-EPI eGFR (J test p=0.024, Cox-Pesaran test p=0.005) and Mayo eGFR (J test p=0.004, Cox-Pesaran test p=0.001). Conclusions: Measures of split renal function by nuclear renography do not correlate well with CT volumetry. CT volumetry is a superior predictor of residual renal function in donors than nuclear renography. These observations support the use of CT volumetry, with eGFR, for estimation of split renal function in healthy individuals with normal kidney morphology. DISCLOSURE:Kim, S.: Grant/Research Support, Astellas Pharma Canada, Novartis Pharma Canada, Genzyme Canada.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".