Risk of death following kidney allograft failure: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: People with kidney allograft failure represent an increasing fraction of all those starting dialysis therapy. We sought to summarize prognosis following kidney allograft failure and identify potentially beneficial interventions or modifiable risk factors. METHODS: We searched MEDLINE and EMBASE (inception to 1 October 2013) and article reference lists without language restriction and selected cohort studies of all-cause mortality and fatal infection-related and cardiovascular events in people starting dialysis following kidney allograft failure. Two reviewers independently extracted data on study design, participant characteristics, dialysis modality, transplant nephrectomy, immunosuppression strategy, transplant-naive comparators and risk of bias. Discrepancies were resolved with a third reviewer. RESULTS: Forty studies comprising 249 716 participants met the inclusion criteria. The first year of dialysis therapy was associated with the highest mortality. By random effects meta-analysis, annual risk of death, from years 1 to 4, was 0.12 [95% confidence interval (95% CI): 0.09-0.15], 0.06 (95% CI: 0.05-0.07), 0.05 (95% CI: 0.04-0.06) and 0.05 (95% CI: 0.04-0.06), respectively. We found high heterogeneity in each meta-analysis, which remained unexplained by prespecified subgroup analyses. We could not find sufficient information to summarize the risk for fatal infection-related and cardiovascular events, or to test the role of transplant nephrectomy or different immunosuppressive strategies. Risk of bias was high, especially participation bias. CONCLUSION: Mortality is higher during the first year of dialysis treatment following kidney allograft failure than in subsequent years. Insufficient data are available to assess factors or interventions potentially impacting prognosis following kidney allograft failure. In a culture promoting transplantation, clinical research of different models of care in this growing high-risk population should be a research priority.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| 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".