Mortality Trends After Transfer From Peritoneal Dialysis to Hemodialysis
Bibliographic record
Abstract
Introduction: Transition to hemodialysis (HD) is a common outcome in peritoneal dialysis (PD), but the associated mortality risk is poorly understood. This study sought to identify rates of and risk factors for mortality after transitioning from PD to HD. Methods: Patients with incident PD (between 2000 and 2014) who transferred to HD for ≥1 day were identified, using data from Australia and New Zealand Dialysis and Transplantation registry (ANZDATA), Canadian Organ Replacement Register (CORR), Europe Renal Association (ERA) Registry, and the United States Renal Dialysis System (USRDS). Crude mortality rates were calculated for the first 180 days after transfer. Separate multivariable Cox models were built for early (<90 days), medium (90-180 days), and late (>180 days) periods after transfer. Results: Overall, 6683, 5847, 21,574, and 80,459 patients were included from ANZDATA, CORR, ERA Registry, and USRDS, respectively. In all registries, crude mortality rate was highest during the first 30 days after a transfer to HD declining thereafter to nadir at 4 to 6 months. Crude mortality rates were lower for patients transferring in the most recent years (than earlier). Older age, PD initiation in earlier cohorts, and longer PD vintage were associated with increased risk of death, with the strongest associations during the first 90 days after transfer and attenuating thereafter. Mortality risk was lower for men than women <90 days after transfer, but higher after 180 days. Conclusion: In this multinational study, mortality was highest in the first month after a transfer from PD to HD and risk factors varied by time period after transfer. This study highlights the vulnerability of patients at the time of modality transfer and the need to improve transitions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".