Potential Cost Savings Associated with the Reduction of Hospital Admissions by Using Online High-Volume Hemodiafiltration (Hv-HDF) vs. High-Flux hemodialysis (Hf-HD)
Bibliographic record
Abstract
Background: On-line HDF for maintenance dialysis patients is available in Europe and Canada but is essentially absent in the US. The National Institute for Health and Care Excellence (NICE) conducted a systematic review and built economic models to compare hemodiafiltration (HDF) with Hf-HD. They found HDF to be cost-effective due to benefits such as increased survival and reduced medication requirements. In addition, NICE found HDF using high convection volumes ˜20+ L (HvHDF) had greater mortality benefits compared to Hf-HD. Economic models built upon payment systems outside of the US may be difficult to apply within the US due to differing payment structures. This analysis estimates the potential cost-savings associated with reducing hospital admissions with online HvHDF (vs Hf-HD) based on published studies and USRDS cost data. Methods: We updated the NICE systematic literature review on HDF studies, especially for articles on hospitalization by searching EMBASE (Ovid), PubMed and NHS EED from 2010 to present. We used an input-output Microsoft Excel® database to calculate the potential cost-saving of online HvHDF compared to Hf-HD from reducing hospitalization and estimating the savings associated with those averted hospitalization and missed in-center HD. The average cost of hospitalization was derived from USRDS and adjusted to 2021 ($17,181), and the average hospital stay was 6.42 days and assuming thrice weekly would result in 2.75 missed HD treatments. It is assumed that reimbursement rate for in-center HD is $253.13 per treatment and costs of treating with HvHDF and Hf-HD are equivalent. Results: Out of 107 studies found, 4 reported hospitalization rates for HDF and Hf-HD, and 1 compared HvHDF with Hf-HD. This study found 10.8 fewer hospital admissions with HDF per 100 patient-years (Maduell, et al, High efficiency postdilution online hemodiafiltration reduces all-cause mortality in hemodialysis patients. J Am Soc Nephrol, 2013: 487-97). We identified potential saving of $1,856 per patient per year (PPPY) due to averted hospitalizations and $75 PPPY due to avoiding missed HD treatment for a total of $1,931 PPPY. Conclusions: The potential annual cost-savings of using HvHDF over Hf-HD in maintenance in-center HD was estimated as $1,931 PPPY or $193,071 per 100 patients. Funding: Commercial Support - Frresenius Medical Care Renal Therapies Group, Waltham, MA
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".