Mortality from infections and malignancies in patients treated with renal replacement therapy: data from the ERA-EDTA registry
Bibliographic record
Abstract
BACKGROUND: Infections and malignancies are the most common non-cardiovascular causes of death in patients on chronic renal replacement therapy (RRT). Here, we aimed to quantify the mortality risk attributed to infections and malignancies in dialysis patients and kidney transplant recipients when compared with the general population by age group and sex. METHODS: We followed 168 156 patients included in the ERA-EDTA registry who started RRT in 1993-2007 until 1 January 2012. Age- and cause-specific mortality rates per 1000 person-years (py) and mortality rate ratios (MRRs) compared with the European general population (WHO) were calculated. To identify risk factors, we used Cox regression. RESULTS: Infection-related mortality was increased 82-fold in dialysis patients and 32-fold in transplant recipients compared with the general population. Female sex, diabetes, cancer and multisystem disease were associated with an increased risk of infection-related mortality. The sex difference was most pronounced for dialysis patients aged 0-39 years, with women having a 32% (adjusted HR 1.32 95% CI 1.09-1.60) higher risk of infection-related mortality than men. Mortality from malignancies was 2.9 times higher in dialysis patients and 1.7 times higher in transplant recipients than in the general population. Cancer and multisystem disease as primary causes of end-stage renal disease were associated with higher mortality from malignancies. CONCLUSION: Infection-related mortality is highly increased in dialysis and kidney transplant patients, while the risk of malignancy-related death is moderately increased. Young women on dialysis may deserve special attention because of their high excess risk of infection-related mortality. Further research into the mechanisms, prevention and optimal treatment of infections in this vulnerable population is required.
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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 | 0.000 | 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".