Losartan reduces the costs associated with nephropathy and end-stage renal disease from type 2 diabetes: Economic evaluation of the RENAAL study from a Canadian perspective.
Bibliographic record
Abstract
BACKGROUND: The Reduction of Endpoints in NIDDM [non-insulin-dependent diabetes mellitus] with the Angiotensin II Antagonist Losartan (RENAAL) study demonstrated the renoprotective effects of losartan in patients with nephropathy from type 2 diabetes. OBJECTIVE: To perform an economic evaluation of the costs associated with end-stage renal disease (ESRD) from a Canadian public health perspective, based on the clinical outcomes reported in the RENAAL study. METHODS: ESRD-related costs were determined by estimating the mean number of days with ESRD multiplied by the daily cost of ESRD (140 dollars); mean days with ESRD were calculated by subtracting the area under the Kaplan-Meier survival curve for time to the first event of ESRD or all-cause mortality from the area under the curve for all-cause mortality. Daily ESRD cost was determined using Canadian specific data sources. ESRD-related cost savings with losartan were obtained by subtracting the ESRD-related costs of the losartan group from those of the placebo group. Net cost savings were ESRD-related cost savings with losartan minus the drug cost of losartan. RESULTS: Losartan reduced the number of ESRD days by 33.6 per patient over 3.5 years (95% CI 10.9 to 56.3) compared with placebo. Losartan reduced ESRD-related costs by 4,695 dollars per randomized patient over 3.5 years (95% CI 1,523 dollars to 7,868 dollars). After accounting for the drug cost of losartan, net cost savings with losartan were 3,675 dollars per randomized patient over 3.5 years. CONCLUSION: Losartan therapy for patients with nephropathy from type 2 diabetes reduces the clinical incidence of ESRD and can result in considerable cost savings for the Canadian public health system.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".