Reduced Mortality Associated With the Use of ACE Inhibitors in Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: ACE inhibitor therapy is widely used in lower-risk patients with type 2 diabetes to reduce mortality, despite limited evidence to support this clinical strategy. The aim of this study was to evaluate the association between ACE inhibitor use and mortality in patients with diabetes and no cardiovascular disease. RESEARCH DESIGN AND SETTINGS: Using the Saskatchewan health databases, 12,272 new users of oral hypoglycemic agents were identified between the years of 1991 and 1996. We excluded 3,202 subjects with previous cardiovascular disease. Of the remaining subjects, 1,187 "new users" of ACE inhibitors were identified (ACE inhibitor cohort). Subjects not receiving ACE inhibitor therapy throughout the follow-up period served as the control cohort (n = 4,989). Subjects were prospectively followed until death or the end of 1999. Multivariate Cox proportional hazards models were used to assess differences in all-cause and cardiovascular-related mortality between cohort groups. RESULTS: Subjects were 60.7 +/- 13.7 years old, 43.6% female, and were followed for an average of 5.3 +/- 2.1 years. Mean duration of ACE inhibitor therapy was 3.6 +/- 1.8 years. We observed significantly fewer deaths in the ACE inhibitor group (102 [8.6%]) compared with the control cohort (853 [17.1%]), with an adjusted hazard ratio (HR) and 95% CI of 0.49 (0.40-0.61) (P < 0.001). Cardiovascular-related mortality was also reduced (40 [3.4%] vs. 261 [5.2%], adjusted HR, 0.63 [0.44-0.90]; P = 0.012). CONCLUSIONS: The use of ACE inhibitors was associated with a significant reduction in all-cause and cardiovascular-related mortality in a broad spectrum of patients with type 2 diabetes and no cardiovascular disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".