Cost-Utility Analysis of Renal Replacement Therapy Modalities in the Management of Severe Acute Kidney Injury in US Critically Ill Patients
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is common among patients admitted to the intensive care unit (ICU), with 5-15% receiving renal replacement therapy (RRT). Continuous renal replacement therapy (CRRT) and intermittent hemodialysis (IHD) are well-established treatments for severe AKI, but renal recovery is variable and often incomplete, leading to long-term morbidity and mortality. The clinical and cost-effectiveness of either therapy are under active debate. This analysis aims to strengthen the evidence on the cost-utility of CRRT compared with IHD to manage severe AKI in ICU using a US third-party costing perspective. Methods: The analysis used a 90-day decision tree simulating hospital admission and a semi-Markov process with annual cycles and half-cycle correction to capture lifetime costs and outcomes, discounted at 3% annually. Survivors at 90 days either progressed to ESKD on dialysis (ESKD-D), with some receiving transplants, or became dialysis-independent. In the case of transplant failure, patients returned to ESKD-D. Tunnel states addressed Markov memoryless properties. A US-representative analysis of real-world data applying propensity score matching to control for selection bias informed the probability of lifetime dialysis dependence. Costs and utilities were sourced from peer-reviewed publications or national data. Uncertainty was investigated using deterministic and probabilistic sensitivity analyses. Results: In the base case, lifetime costs and quality-adjusted life-years (QALYs) were $273,314 and 5.681 for CRRT compared to $268,449 and 5.457 for IHD. CRRT had an 89.6% probability of being cost-effective ($23,860/QALY gained), being associated with 0.269 additional life-years. Long-term CKD management costs, accounting for 50% of CRRT's excess costs, significantly influenced results and were examined in scenario analyses. Conclusion: CRRT is likely a cost-effective option for managing severe AKI in the ICU compared with IHD. This study builds on existing economic evaluations by incorporating large comparative studies and exploring clinical uncertainty. The model highlights the need to clarify RRT's role in CKD progression and enhance post-AKI care to improve patient outcomes.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".