Economic evaluation of adding dapagliflozin to standard care in the treatment of chronic kidney disease: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease is a significant public health issue. Dapagliflozin has been shown to improve the quality of life for patients with chronic kidney disease. This review aimed to systematically assess the cost-effectiveness of adding dapagliflozin to standard care compared with standard care alone for treating chronic kidney disease. METHODS: The relevant studies were searched in PubMed, Web of Science, Scopus, Embase, and Cochrane from the inception date to June 1, 2024. The titles, abstracts, and full texts were independently evaluated and screened by two authors. Additionally, the economic evaluation studies were assessed independently by two authors using the consolidated health economic evaluation reporting standards checklist. RESULTS: 14 studies were included which were about the economic evaluations of adding dapagliflozin in the treatment of chronic kidney disease. The minimum consolidated health economic evaluation reporting standards score for the studies was 0.77, indicating very good quality. Adding dapagliflozin to the standard of care would be more effective and cost-saving in Mexico, Malaysia, Canada, Thailand, and China. The highest incremental cost-effectiveness ratio of dapagliflozin ($67962.75/QALY) originated from the USA. According to the available studies, adding dapagliflozin to standard of care for the treatment of chronic kidney disease is considered cost-effectiveness from both the healthcare system and the payer's perspective. CONCLUSION: Adding dapagliflozin to standard care in the treatment of chronic kidney disease is cost-effective from both the healthcare system and the payer's perspective in well-developed countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".