Impact of elevated glucose levels on cardiac function in STEMI patients: glucose delta as a prognostic biomarker
Bibliographic record
Abstract
BACKGROUND: Elevated glucose levels have emerged as a significant prognostic factor following acute myocardial infarction (AMI). OBJECTIVE: This study aimed to evaluate glycemic parameters associated with infarct size and left ventricular function. RESEARCH DESIGN AND METHODS: A total of 244 patients with ST-segment elevation myocardial infarction (STEMI) treated using a pharmacoinvasive strategy were included. Glucose delta was calculated as the difference between mean glucose levels estimated from glycated hemoglobin (HbA1c) and serum glucose levels collected at hospital admission. Infarct size and left ventricular ejection fraction (LVEF) were assessed 30 days post-infarction using cardiac magnetic resonance (CMR) imaging. RESULTS: Higher glucose delta values were significantly associated with reduced LVEF and larger infarct size, regardless of diabetes diagnosis. Differences in infarcted ventricular mass were noted between diabetic and non-diabetic patients above specific thresholds: (18.62 ± 11.0 g) vs. (16.24 ± 13.17 g), p = 0.019, with an effect size of 0.55. The receiver operating characteristic curve yielded an area under the curve (AUC) of 0.65 (95% CI 0.57-0.72). CONCLUSIONS: In STEMI patients undergoing pharmacoinvasive treatment, a higher glycemic delta was associated with greater infarct size and lower LVEF. This straightforward glycemic parameter provides valuable prognostic insight for both diabetic and non-diabetic populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".