Sodium‐glucose co‐transporter‐2 inhibitors in type 2 diabetes: Are clinical trial benefits for heart failure reflected in real‐world clinical practice? A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
AIM: To determine the absolute risk reduction (ARR) of heart failure events in people treated with sodium-glucose co-transporter-2 (SGLT2) inhibitors. MATERIALS AND METHODS: We searched PubMed, EMBASE, CINAHL and ISI Web of Science for observational studies published to 9 May 2022 that explored the association between SGLT2 inhibitors and any indication for heart failure (including new diagnosis or hospitalization for heart failure) in type 2 diabetes. Identified studies were independently screened by two reviewers and assessed for bias using the Newcastle-Ottawa scale. Eligible studies with comparable outcome data were pooled for meta-analysis using random-effects models, reporting hazard ratios (HRs) with 95% confidence intervals (CIs). The ARR per 100 person-years was determined overall, and in subgroups with and without baseline cardiovascular disease (CVD). RESULTS: From 43 eligible studies, with a total of 4 818 242 participants from 17 countries, 21 were included for meta-analysis. SGLT2 inhibitors were associated with a reduced risk of hospitalization for heart failure (HR 0.65, 95% CI 0.59-0.72) overall and both in those with CVD (HR 0.78, 95% CI 0.68-0.89) and without CVD (HR 0.53, 95% CI 0.39-0.71). Risk reduction for hospitalization for heart failure in people with a history of CVD (ARR 1.17, 95% CI 0.78-1.55) was significantly greater than for those without CVD (ARR 0.39, 95% CI 0.32-0.47). The number-needed-to-treat to prevent one event of hospitalization for heart failure was 86 (95% CI 65-128) person-years of treatment for the CVD group and 256 (95% CI 215-316) person-years for those without CVD. CONCLUSIONS: Real-world SGLT2 inhibitor use supports randomized trial data for the size effect of reduced hospitalization for heart failure in type 2 diabetes, although with a much lower ARR in people without CVD.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".