Risk Factors for Developing Low eGFR and Albuminuria in Living Kidney Donors
Bibliographic record
Abstract
Background: Chronic kidney disease is associated with significant morbidity and mortality in the general population, but little is known about the incidence and risk factors associated with developing kidney dysfunction in living donors following nephrectomy. Methods: We conducted a retrospective, population-based control study using linked healthcare databases in Alberta, Canada to identify 590 donors who underwent nephrectomy between May 2001 and December 2017. The primary outcome was evidence of sustained kidney dysfunction following nephrectomy, defined as either 2 estimated glomerular filtration rate (eGFR) measurements <45 mL/min/1.73 m2 or 2 measurements of moderate or severe albuminuria that were at least 90 days apart. We used Cox proportional hazard regression analyses to examine the association between potential risk factors and the outcome of post-donation kidney dysfunction. Results: Over a median follow-up period of 8.6 years (interquartile range [IQR]: 4.7-16.9 years), 47 donors (8.0%) developed sustained kidney dysfunction, with an incidence rate of 9.2 per 1,000 person-years (95% confidence interval: 6.6-11.8). The median time for development of kidney dysfunction beyond the first year after nephrectomy was 2.9 years (IQR: 1.4-8.0 years). Donors who developed kidney dysfunction after donation were more likely to be older, male, have lower pre-donation eGFR, have evidence of pre- or post-donation hypertension, and post-donation diabetes. Conclusions: A small proportion of kidney donors will develop post-donation kidney dysfunction. Donors with risk factors associated with sustained kidney dysfunction may benefit from more diligent follow-up care. Funding: Government Support - Non-U.S.Kaplan-Meier curve with 95% confidence interval of estimated kidney dysfunction free survival in living kidney donors from one-year post-donation onwards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".