Cost Savings Associated with Extending Patient Time on Peritoneal Dialysis
Bibliographic record
Abstract
Background: Only 7.1% of end-stage kidney disease (ESKD) patients receive peritoneal dialysis (PD) (USRDS, 2019). This number is expected to increase dramatically because of a 2019 U.S. presidential executive order which set the goal for 80% of patients to receive treatment at home or a transplant by 2025. To achieve this goal, providers and payers will not only need to encourage the use of PD but also will need to mitigate PD technique failure (TF). McGill et al. (AJKD, 2019) found that 6 out of 7 patients who start treatment with PD experience TF and will switch to hemodialysis (HD) within five years. Of patients who switch, Jaar et al. (BMC Nephrology, 2009) found that 20% did so by 6 months. Improving PD treatment time is important for patient quality of life and reducing healthcare costs. The goal of this study was to model Medicare cost savings associated with extending patient time on PD. Methods: Using USRDS data, we calculated total Medicare spending per patient per day to be $226.71 for PD and $266.26 for HD, respectively. We estimated potential savings if treatment with PD could be extended each month up to 1 year using a base case of an incident ESKD patient who transitions to HD after receiving PD for 6 months. We assumed that during this year patients neither had a transplant nor died and that patients received HD once they stopped PD. Results: Extending PD beyond 6 months for incident patients could result in potential savings to payers. Extending PD by 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months could save $1,203.05, $2,406.10, $3,609.15, $4,812.20, $6,015.25, or $7,218.30, respectively per patient. We found that if a patient could avoid TF, $14,436.59 could be saved annually. Conclusions: Extending the PD treatment time beyond 6 months has the potential to reduce treatment costs by $1,203.05-$7,218.30 for patients staying on PD 1 month to 6 months longer, respectively. Annually, avoiding TF could result in savings of $14,436.59. Multiple risk factors are associated with TF. Focusing on identifying and addressing modifiable conditions may help to keep more patients dialyzing at home. Funding: Commercial Support - Fresenius Medical Care North America Renal Therapies Group
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".