Economic Evaluation of Frequent Home Nocturnal Hemodialysis Based on a Randomized Controlled Trial
Bibliographic record
Abstract
Provider and patient enthusiasm for frequent home nocturnal hemodialysis (FHNHD) has been renewed; however, the cost-effectiveness of this technique is unknown. We performed a cost-utility analysis of FHNHD compared with conventional hemodialysis (CvHD; 4 hours three times per week) from a health payer perspective over a lifetime horizon using patient information from the Alberta NHD randomized controlled trial. Costs, including training costs, were obtained using microcosting and administrative data (CAN$2012). We determined the incremental cost per quality-adjusted life year (QALY) gained. Robustness was assessed using scenario, sensitivity, and probabilistic sensitivity analyses. Compared with CvHD (61% in-center, 14% satellite, and 25% home dialysis), FHNHD led to incremental cost savings (-$6700) and an additional 0.38 QALYs. In sensitivity analyses, when the annual probability of technique failure with FHNHD increased from 7.6% (reference case) to ≥19%, FHNHD became unattractive (>$75,000/QALY). The cost/QALY gained became $13,000 if average training time for FHNHD increased from 3.7 to 6 weeks. In scenarios with alternate comparator modalities, FHNHD remained dominant compared with in-center CvHD; cost/QALYs gained were $18,500, $198,000, and $423,000 compared with satellite CvHD, home CvHD, and peritoneal dialysis, respectively. In summary, FHNHD is attractive compared with in-center CvHD in this cohort. However, the attractiveness of FHNHD varies by technique failure rate, training time, and dialysis modalities from which patients are drawn, and these variables should be considered when establishing FHNHD programs.
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 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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".