Dipeptidyl peptidase-4 inhibitors and the risk of heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Given recent discrepant results from randomized controlled trials (RCTs), we examined the totality of RCT evidence assessing the association between dipeptidyl peptidase-4 (DPP-4) inhibitors and heart failure. Methods: MEDLINE, Embase and ClinicalTrials.gov were searched without language restrictions to August 2016 for RCTs comparing DPP-4 inhibitors to placebo or no therapy for a period of 24 weeks or more. We included all heart failure outcomes when listed either as a serious adverse event or adverse event. Pooled analyses used random-effects. Results: We identified 100 RCTs (n = 79 867) - 3 large cardiovascular-safety RCTs (SAVOR-TIMI 53[saxagliptin]/n = 16 492, EXAMINE[alogliptin]/n = 5380, and TECOS[sitagliptin]/n = 14 735), and 97 smaller RCTs with a primary outcome that was usually change in glycated hemoglobin. Virtually all RCTs were high-quality, multicentre, placebo-controlled trials. A total of 96% (1192/1244) of heart failure events were prespecified, blindly adjudicated and required hospital admission. Pooled results suggested a 13% increase in heart failure (relative risk [RR] 1.13, 95% confidence interval [CI] 1.01-1.26, I2 = 0%; 32 RCTs, n = 54 640, 1244 events). When including only the 3 large RCTs, the increase was similar, but not significant (RR 1.14, 95% CI 0.97-1.32; 3 RCTs, n = 36 543, 1169 adjudicated events; number needed to harm 246) owing to heterogeneity (I2 = 42%), which lead to wider CIs, because SAVOR-TIMI 53 showed increased heart failure (RR 1.26, 95% CI 1.06-1.49) and TECOS showed no effect (RR 1.00, 95% CI 0.83-1.19). Interpretation: Despite pooled data from 79 867 patients, whether DPP-4 inhibitors increase heart failure overall or exhibit within-class differences remains unresolved. Our results highlight the importance of ongoing trials that are comparing DPP-4 inhibitors to placebo, although no large cardiovascular-safety RCTs are comparing different DPP-4 inhibitors to each other; consequently, these will address the overall but not class-difference question.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".