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Record W4396992750 · doi:10.1681/asn.20213210s1283d

Potential Cost Savings Associated with the Reduction of Hospital Admissions by Using Online High-Volume Hemodiafiltration (Hv-HDF) vs. High-Flux hemodialysis (Hf-HD)

2021· article· en· W4396992750 on OpenAlexaboutno aff
Farah Farahati, Linda Ficociello, Claudy Mullon, Michael S. Anger

Bibliographic record

VenueJournal of the American Society of Nephrology · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisMedicineFlux (metallurgy)Volume (thermodynamics)Reduction (mathematics)Intensive care medicineUrologyInternal medicineEmergency medicineCardiologyMaterials sciencePhysicsMathematicsThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.017
Bibliometrics0.0090.006
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.009
GPT teacher head0.246
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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