Meta-analysis of admission hyperglycaemia in acute myocardial infarction patients treated with primary angioplasty: a cause or a marker of mortality?
Bibliographic record
Abstract
AIMS: Admission hyperglycaemia (AH) has been associated with worse outcomes in acute myocardial infarction (AMI). In the current review, we evaluated the impact of primary angioplasty (pPCI) on mortality in AMI patients with AH. Our second aim was to evaluate if AH is a marker of baseline risk or an independent predictor of mortality. METHODS AND RESULTS: A comprehensive search of four major databases was performed. We included original research studies reporting data on mortality in AMI patients with AH (mean plasma glucose >156 mg/dL/8.7 mmol) and euglycaemia who were treated with pPCI. Of 481 citations, 12 studies were included in the analysis. Admission hyperglycaemia was associated with a higher 30-day [risk ratio (RR) 4.30, P < 0.0001] and 1- to 3-year mortality (RR 2.26, P < 0.0001). As well, AH was more prevalent in women and in patients with an increasing number of cardiac risk factors or angiographic predictors of mortality, such as previous AMI (RR 0.89, P = 0.01), multivessel coronary disease (RR 0.72, P = 0.0001), and involvement of left anterior descending artery (RR 0.92, P < 0.0001). Moreover, patients with AH had larger infarcts (higher creatine kinase-MB; P = 0.004) and more frequent ventricular arrhythmias (P = 0.002). CONCLUSION: Despite rapid revascularization and treatment of hyperglycaemia, patients with AH continue to have a higher mortality. Admission hyperglycaemia occurs more commonly in patients who have traditional predictors of worse outcomes-specifically prior infarction, anterior wall infarctions, and multivessel disease. Likely, AH is a predictor of rather than a bona fide therapeutic target in AMI.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".