Donors Older than 70 Years Old Are a Good Source of Cadaveric Kidney Transplants, a Fifteen Years Experience
Bibliographic record
Abstract
Introduction: Due to a significant organ shortage and an increasingly older population, the use of older deceased donors (DD) for kidney transplantation (KT) has increased worldwide. According to UNOS, donors 65 and older represented 5.8% of kidney transplant in 2011 vs 1.9% in 1990. In Canada this number is even higher due to a significantly older population. In this study, we examine graft survival and graft function for DD 70 and older when compared to younger ones. Methods: Patients who underwent a DDKT (n=816) at our institution from 1996 to 2011 were reviewed. DD were divided have into younger (n=775) and 70 and older (n=41). Demographics, death-censored graft survival, graft function subdivided into delayed graft function (DGF), slow graft function (SGF) and normal graft function (NGF) and transplant characteristics(pump, cold and warm ischemia) were collected. Statistical analysis was done using ANOVA for demographics, COX and Kaplan Mayer for survival, logistic regression for graft function and Chi-square or Fisher's exact tests for univarite analysis. Results: Recipient's age was higher in the older (61.7) vs. the younger (51.9) group (p-value< 0.000). Transplant characteristics were similar among groups. The Kaplan-Mayer graft at 1, 5, 10 and 15 years was 97.7%, 88.4%, 79.8% and 71.8% respectively for the younger vs 97.5%, 77.1% and 65.9% for the 70 and older group. After correcting for recipient age, sex and transplant characteristics the survival hazard ratio (HR) was 2.02(95% CI 1.03-3.94,p-value=0.038) in the older vs the younger DD. In a sub-analysis, survival was similar in those aged between 60 and 70 and those older than 70 with a HR of 1.14 (95% CI 0.56-2.35,p-value=0.716). The risk ratio (RR) of DGF in the older group was 1.93 times that in the younger group with a 95%CI1.35-2.80 (p-value=0.004). However the risk of DGF was comparable in those aged between 60 and 70 and those older than 70 (RR1.49,95%CI 0.96-2.27,p-value=0.093). The risks of SGF and NGF were comparable among the younger and older groups.Figure: [Kaplan Meier Survival Estimate]Conclusion: Donors aged 70 and older had a lower death censored graft survival, when compared to younger donors however survival is similar to that of donors aged 60 to 70, as well as DGF. With the scarcity of organs available and rising age of recipients, donors 70 and older could potentially be an important source of cadaveric kidneys.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".