Health Outcomes and Cost Considerations of Assisted Peritoneal Dialysis: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Peritoneal dialysis (PD) is underutilized in many parts of the world despite pro-PD health policies. The physical and cognitive demands of PD means that over half of eligible patients require some form of assistance. As such, many countries now offer assisted PD (aPD) programs to help patients start or stay on PD as opposed to in-center hemodialysis (HD). In order to evaluate the potential scope of aPD, it is important to review the outcomes and cost considerations of aPD. SUMMARY: We reviewed available data from different countries and regions for health outcomes between aPD and in-center HD, with a focus on quality of life (QoL), mortality, hospitalization, and technique survival. We also evaluated studies discussing the overall costs of delivering aPD, including training, operating costs, and indirect costs and compared these to in-center HD costs for the same regions. Key Messages: aPD patients are older and more frail than either self-care PD patients and many in-center HD patients. We found no evidence for any difference in QoL, mortality, or hospitalization between aPD and in-center HD after adjustment for these differences. There is some evidence for an association between nurse assistance and improved technique survival as compared to family assistance or self-care PD. Despite increased cost of providing assistance in PD, it is still significantly less expensive than in-center HD in Western Europe and Canada.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".