Cost‐effectiveness of canagliflozin and dapagliflozin for treatment of patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
AIM: To examine the cost-effectiveness of adding canagliflozin or dapagliflozin to standard of care (SoC) versus SoC alone in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). MATERIALS AND METHODS: We used a Markov microsimulation model to assess the cost-effectiveness of canagliflozin plus SoC (canagliflozin + SoC), dapagliflozin plus SoC (dapagliflozin + SoC) and SoC alone. Analyses were conducted from a healthcare system perspective. Costs were measured in 2021 Canadian dollars (C$), and effectiveness was measured in quality-adjusted life-years (QALYs). RESULTS: Over a patient's lifetime, canagliflozin + SoC and dapagliflozin + SoC yielded cost savings of C$33 460 and C$26 764 and generated 1.38 and 1.44 additional QALYs compared with SoC alone, respectively. While QALY gains with dapagliflozin + SoC were higher than those with canagliflozin + SoC, this strategy was also more costly with the incremental cost-effectiveness ratio exceeding the willingness to pay threshold of C$50 000 per QALY. Dapagliflozin + SoC, however, generated cost savings and QALY gains compared with canagliflozin + SoC over shorter time horizons of 5 or 10 years. CONCLUSIONS: Dapagliflozin + SoC was not cost-effective versus canagliflozin + SoC in patients with CKD and T2D over the lifetime horizon. However, adding canagliflozin or dapagliflozin to SoC was less costly and more effective relative to SoC alone for treatment of CKD and T2D.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".