A systematic review of cost-effectiveness analyses of continuous versus intermittent renal replacement therapy in acute kidney injury
Bibliographic record
Abstract
Though cost-effectiveness analyses (CEAs) have evaluated continuous renal replacement therapy (RRTs) and intermittent RRTs in acute kidney injury (AKI) patients; it is yet to establish which RRT technique is most cost-effective. We systematically reviewed the current evidence from CEAs of CRRT versus IRRT in patients with AKI. PubMed, EMBASE, and Cochrane databases searched for CEAs comparing two RRTs. Overall, seven CEAs, two from Brazil and one from US, Canada, Colombia, Belgium, and Argentina were included. Five CEAs used Markov model, three reported following CHEERS, none accounted indirect costs. Time horizon varied from 1-year–lifetime. Marginal QALY gain with CRRT compared to IRRT was reported across CEAs. Older CEAs found CRRT to be costlier and not cost-effective than IRRT (ICER 2019 US$: 152,671$/QALY); latest CEAs (industry-sponsored) reported CRRT to be cost-saving versus IRRT (−117,614$/QALY). Risk of mortality, dialysis dependence, and incidence of renal recovery were the key drivers of cost-effectiveness. CEAs of RRTs for AKI show conflicting findings with secular trends. Latest CEAs suggested CRRT to be cost-effective versus IRRT with dialysis dependence rate as major driver of cost-effectiveness. Future CEAs, preferably non-industry sponsored, may account for indirect costs to improve the generalizability of CEAs.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".