Abstract 3631: Elevated In-hospital Glucose Level, and not Known History of Diabetes Mellitus, Predicts 30-day Mortality Following Acute ST-elevation Myocardial Infarction
Bibliographic record
Abstract
Introduction: According to clinical risk assessment guidelines, a history of diabetes mellitus (DM) portends poor outcomes following acute MI. Elevated in-hospital glucose levels also predict early mortality in acute MI patients, but the degree to which glucose levels and diabetic history independently predict post-MI mortality is unclear. Methods and Hypothesis: We analyzed data from the combined cohort of the CREATE-ECLA and OASIS-6 randomized trials that evaluated the impact of glucose-insulin-potassium (GIK) infusion versus no infusion on 30-day mortality in 22,943 patients hospitalized with acute ST-elevation MI. We calculated the average in-hospital glucose level for each patient (mean of the admission, 6-hour, and 24-hour glucose levels). Logistic regression was performed to determine whether average glucose level and history of DM remained significant mortality predictors after adjusting for age, sex, and GIK allocation. Results: Glucose data were recorded in 22,860 (99.6%) patients; 10,050 (44%) had an average in-hospital glucose level ≥ 8 mmol/L (144 mg/dL), of whom 65% did not have known prior DM. Among patients with glucose >8 mmol/L, 30-day mortality rates were similar in patients with and without known DM (Figure ). In-hospital glucose, but not history of DM, was a significant multivariable predictor of mortality (Table). Conclusions: By considering only history of DM and not in-hospital glucose levels, risk assessment guidelines for acute MI overlook a large proportion of patients at high risk for early death. Therefore, clinicians should emphasize elevated glucose levels in addition to history of DM as a risk marker in patients with acute MI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".