Comparative efficacy and cost-effectiveness of sodium-glucose co-transporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, and nonsteroidal mineralocorticoid receptor antagonists in type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
Type 2 diabetes (T2D) and chronic kidney disease (CKD) are major public health challenges. People with T2D and CKD have high risks of cardiovascular (CV) events and kidney failure. Novel drug classes, including sodium-glucose cotransporter-2 inhibitors (SGLT-2i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and nonsteroidal mineralocorticoid receptor antagonists (nsMRA), have provided more cardiorenal protection for treatment of T2D and CKD than standard of care (SoC). This thesis examines the comparative efficacy in improving CV and renal outcomes and assesses the cost-effectiveness of these novel drugs for treatment of T2D and CKD. This thesis has four chapters. Chapter 1 provides background information about epidemiology, disease burden, definition, and treatment of T2D and CKD. It also describes the study design and reviews the existing literature. Chapter 2 is a systematic review and network meta-analysis comparing the relative efficacy of SGLT-2i, GLP-1RA, and nsMRA in improving CV and renal outcomes in patients with T2D and CKD. We found that SGLT-2i provided better cardiorenal protection than GLP-1RA and nsMRA in patients with T2D and CKD. There were no significant differences between GLP-1RA and nsMRA in reducing CV and renal outcomes. Chapter 3 examines the cost-effectiveness of adding SGLT-2i (canagliflozin or dapagliflozin) to SoC versus SoC alone for the treatment of T2D and CKD. Our study showed that adding canagliflozin or dapagliflozin to SoC was cheaper and more effective than SoC alone. Dapagliflozin plus SoC incurred lower cost and was more effective than canagliflozin plus SoC over the 5- or 10-year horizons, but it was not cost-effective versus canagliflozin plus SoC over longer time horizon. Chapter 4 concludes and discusses implications, limitations, and future research directions in T2D and CKD treatment.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".